Using accounting data based indexing to create a portfolio of financial objects

Abstract

A system, method and computer program product creates an index based on accounting based data, as well as a portfolio of financial objects based on the index where the portfolio is weighted according to accounting based data. A passive investment system may be based on indices created from various metrics. The indexes may be built with metrics other than market capitalization weighting, price weighting or equal weighting. Non-financial metrics may also be used to build indexes to create passive investment systems. Additionally, a combination of financial non-market capitalization metrics may be used along with non-financial metrics to create passive investment systems. Once the index is built, it may be used as a basis to purchase securities for a portfolio. Specifically excluded are widely-used capitalization-weighted indexes and price-weighted indexes, in which the price of a security contributes in a substantial way to the calculation of the weight of that security in the index or the portfolio, and equal weighting weighted indexes. Valuation indifferent indexes avoid overexposure to overvalued securities and underexposure to undervalued securities, as compared with conventional capitalization-weighted and price-weighted.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of and claims the benefit of U.S. Patent Application No. 60/896,867, filed Mar. 23, 2007, the contents of which are incorporated herein by reference in their entirety and are of common assignee.

The present application also claims the benefit of U.S. patent application Ser. No. 11/509,002, filed Aug. 24, 2006, the contents of which are incorporated herein by reference in their entirety and are of common assignee, which claims the benefit of (i) U.S. Patent Application No. 60/751,212, filed Dec. 19, 2005, the contents of which are incorporated herein by reference in their entirety and are of common assignee, and (ii) U.S. patent application Ser. No. 11/196,509, filed Aug. 4, 2005, the contents of which are incorporated herein by reference in their entirety and are of common assignee, which claims the benefit (a) of U.S. patent application Ser. No. 10/159,610, filed Jun. 3, 2002, the contents of which are incorporated herein by reference in their entirety and are of common assignee, and (b) U.S. patent application Ser. No. 10/961,404, filed Oct. 12, 2004, the contents of which are incorporated herein by reference in their entirety and are of common assignee, which in turn claims the benefit of (A) U.S. Patent Application No. 60/541,733, filed Feb. 4, 2004, the contents of which are incorporated herein by reference in their entirety and are of common assignee.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Exemplary embodiments relate generally to securities investing, and more particularly to construction and use of indexes and portfolios based on indexes.

2. Related Background

Conventionally, there are various broad categories of securities portfolio management. One conventional securities portfolio management category is active management wherein the securities are selected for a portfolio individually based on economic, financial, credit, and/or business analysis; on technical trends; on cyclical patterns; etc. Another conventional category is passive management, also called indexing, wherein the securities in a portfolio duplicate those that make up an index. The securities in a passively managed portfolio are conventionally weighted by relative market capitalization weighting or equal weighting. Another middle ground conventional category of securities portfolio management is called enhanced indexing, in which a portfolio's characteristics, performance and holdings are substantially dominated by the characteristics, performance and holdings of the index, albeit with modest active management departures from the index.

The present invention relates generally to the passive and enhanced indexing categories of portfolio management. A securities market index, by intent, reflects an entire market or a segment of a market. A passive portfolio based on an index may also reflect the entire market or segment. Often every security in an index is held in the passive portfolio. Sometimes statistical modeling is used to create a portfolio that duplicates the profile, risk characteristics, performance characteristics, and securities weightings of an index, without actually owning every security included in the index. (Examples could be portfolios based on the Wilshire 5000 Equity Index or on the Lehman Aggregate Bond Index.) Sometimes statistical modeling is used to create the index itself such that it duplicates the profile, risk characteristics, performance characteristics, and securities weightings of an entire class of securities. (The Lehman Aggregate Bond Index is an example of this practice.)

Indexes are generally all-inclusive of the securities within their defined markets or market segments. In most cases indexes may include each security in the proportion that its market capitalization bears to the total market capitalization of all of the included securities. The only common exceptions to market capitalization weighting are equal weighting of the included securities (for example the Value Line index or the Standard & Poors 500 Equal Weighted Stock Index, which includes all of the stocks in the S&P 500 on a list basis; each stock given equal weighting as of a designated day each year) and share price weighting, in which share prices are simply added together and divided by some simple divisor (for example, the Dow Jones Industrial Average). Conventionally, passive portfolios are built based on an index weighted using one of market capitalization weighting, equal weighting, and share price weighting.

Most commonly used stock market indices are constructed using a methodology that is based upon either the relative share prices of a sample of companies (such as the Dow Jones Industrial Average) or the relative market capitalization of a sample of companies (such as the S&P 500 Index or the FTSE 100 Index). The nature of the construction of both of these types of indices means that if the price or the market capitalization of one company rises relative to its peers it is accorded a larger weighting in the index. Alternatively, a company whose share price or market capitalization declines relative to the other companies in the index is accorded a smaller index weighting. This can create a situation where the index, index funds, or investors who desire their funds to closely track an index, are compelled to have a higher weighting in companies whose share prices or market capitalizations have already risen and a lower weighting in companies that have seen a decline in their share price or market capitalization.

Advantages of passive investing include: a low trading cost of maintaining a portfolio that has turnover only when an index is reconstituted, typically once a year; a low management cost of a portfolio that requires no analysis of individual securities; and/or no chance of the portfolio suffering a loss—relative to the market or market segment the index reflects—because of misjudgments in individual securities selection.

Advantages of using market capitalization weighting as the basis for a passive portfolio include that the index (and therefore a portfolio built on it) remains continually ‘in balance’ as market prices for the included securities change, and that the portfolio performance participates in (i.e., reflects) that of the securities market or market segment included in the index.

The disadvantages of market capitalization weighting passive indexes, which can be substantial, center on the fact that any under-valued securities are underweighted in the index and related portfolios, while any over-valued securities are over weighted. Also, the portfolio based on market capitalization weighting follows every market (or segment) bubble up and every market crash down. Finally, in general, portfolio securities selection is not based on a criteria that reflects a better opportunity for appreciation than that of the market or market segment overall.

Most commonly used stock market indices are constructed using a methodology that is based upon either the relative share prices of a sample of companies (such as the Dow Jones Industrial Average) or the relative market capitalization of a sample of companies (such as the S&P 500 Index or the FTSE 100 Index). The nature of the construction of both of these types of indices means that if the price or the market capitalization of one company rises relative to its peers it is accorded a larger weighting in the index. Alternatively, a company whose share price or market capitalization declines relative to the other companies in the index is accorded a smaller index weighting. This can create a situation where the index, index funds, or investors who desire their funds to closely track an index, are compelled to have a higher weighting in companies whose share prices or market capitalizations have already risen and a lower weighting in companies that have seen a decline in their share price or market capitalization.

Price or market capitalization based indices can contribute to a ‘herding’ behavior on the behalf of investors by effectively compelling any of the funds that attempt to follow these indices to have a larger weighting in shares as their price goes up and a lower weighting in shares that have declined in price. This creates unnecessary volatility, which is not in the interests of most investors. It may also lead to investment returns that have had to absorb the phenomenon of having to repeatedly increase weightings in shares after they have risen and reduce weightings in them after they have fallen.

Capitalization-weighted indexes (“cap-weighted indexes”) dominate the investment industry today, with approximately $2 trillion currently invested. Unfortunately, cap-weighted indexes suffer from an inherent flaw as they overweight all overvalued stocks and underweight all undervalued stocks. This causes cap-weighted indexes to under-perform relative to indexes that are immune to this shortcoming. In addition, cap-weighted indexes are vulnerable to speculative bubbles and emotional bear markets which may unnaturally drive up or down stock prices respectively.

Equal-weighted indexation is a popular alternative to cap-weighting but one that suffers from its own shortcomings. One significant problem with equal-weighted indexes is that they come out of the same cap-weighted universes as cap-weighted indexes. For example, the S&P Equal Weighted Index simply re-weights the 500 equities that comprise the S&P 500, retaining the bias already inherent to cap-weighted indexes.

High turnover and associated high costs are additional problems of equal-weighted indexes. Equal-weighted indexes include small illiquid stocks, which are required to be held in equal proportion to the larger, more liquid stocks in the index. These small illiquid stocks must be traded as often as the larger stocks but at a higher cost because they are less liquid.

What is needed then is an improved method of weighting financial objects in a portfolio based on an index that overcomes shortcomings of conventional solutions.

SUMMARY

In an exemplary embodiment a system, method and computer program product for index construction and/or portfolio weighting of financial objects for the purpose of investing in the index is disclosed.

Exemplary embodiments may use accounting data based indexing, i.e., accounting data based measures of firm size, rather than market capitalization, to construct an index of financial objects Construction of an index, according to an exemplary embodiment, may include selecting financial objects to be included in an index, and weighting the financial objects in the index. By avoiding the inherent valuation bias of cap-weighted indexes, accounting data based indexes (ADBI) may outperform cap-weighted indexes by as much as 200 bps in the US and by more than 250 bps internationally, based on extensive back testing (to 1962 in the US and to 1988 internationally).

An exemplary embodiment may use four specific metrics in ADBI construction: book equity value; income (free cash flow); sales; and/or gross dividends, if any. Another exemplary embodiment may include additional and/or alternative metrics. Metrics may be varied by country according to another exemplary embodiment. An ADBI construction strategy may offer several advantages. For example, ADBI may outperform cap-weighted indexes. Additionally, ADBI may be adaptable to distinct strategies. ADBI may be used to construct either large or small company indexes, industry sector indexes, geographic indexes and others. ADBI may also effectively limit portfolio risk by providing the benefits of traditional cap-weighted indexes, including diversification, broad market participation, liquidity and low turnover, while generating incrementally higher returns with somewhat lower volatility than comparable cap-weighted indexes. ADBI may also provide protection against market bubbles and fads because a stock's weight in the index is immune to errors in stock valuation.

An exemplary embodiment may be a method of constructing a portfolio of financial objects, including the steps of: purchasing a portfolio of a plurality of mimicking or resampling of financial objects to obtain and/or create a mimicking portfolio, where performance of the portfolio of mimicking or resampled financial objects substantially mirrors the performance of an accounting data based index based portfolio without substantially replicating the accounting data based index based portfolio.

The embodiment may further include: obtaining and/or using a risk model for the portfolio of mimicking or resampled financial objects, where the risk model mirrors a risk model of the accounting data based index.

The performance of the portfolio of mimicking or resampled financial objects may substantially mirror the performance of the accounting data based index based portfolio without substantially replicating financial objects and/or weightings in the accounting data based index based portfolio. The risk model may be substantially similar to the Fama-French factors, where the Fama-French factors may include at least one of size effect, value effect, and/or momentum effect.

A financial object, according to one exemplary embodiment, may include: at least one unit of interest in at least one of: an asset; a liability; a tracking portfolio; a resampled portfolio, a financial instrument and/or a security, where the financial instrument and/or the security denotes a debt, an equity interest, and/or a hybrid; a financial position, a currency position, a trust, a real estate investment trust (REIT), a portfolio of trusts and/or REITS, a security instrument, an equitizing instrument, a commodity, an exchange traded note, a derivatives contract, including at least one of: a future, a forward, a put, a call, an option, a swap, and/or any other transaction relating to a fluctuation of an underlying asset, notwithstanding the prevailing value of the contract, and notwithstanding whether such contract, for purposes of accounting, is considered an asset or liability; a fund; and/or an investment entity or account of any kind, including an interest in, or rights relating to: a hedge fund, an exchange traded fund (ETF), a fund of funds, a mutual fund, a closed end fund, an investment vehicle, and/or any other pooled and/or separately managed investments. In an exemplary embodiment, the financial object may include a debt instrument, including, according to one exemplary embodiment, any one or more of a bond, a debenture, a subordinated debenture, a mortgage bond, a collateral trust bond, a convertible bond, an income bond, a guaranteed bond, a serial bond, a deep discount bond, a zero coupon bond, a variable rate bond, a deferred interest bond, a commercial paper, a government security, a certificate of deposit, a Eurobond, a corporate bond, a government bond, a municipal bond, a treasury-bill, a treasury bond, a foreign bond, an emerging market bond, a developed market bond, a high yield bond, a junk bond, a collateralized instrument, an exchange traded note (ETN), and/or other agreements between a borrower and a lender.

Another exemplary embodiment, may be a method of constructing a portfolio of financial objects, including the steps of: purchasing a plurality of financial objects according to weightings substantially similar to the weightings of an accounting data based index, where performance of the plurality of financial objects substantially mirrors the performance of the accounting data based index without using substantially the same financial objects in the accounting data based index.

The financial object may include: at least one unit of interest in at least one of: an asset; a liability; a tracking portfolio; a financial instrument and/or a security, where the financial instrument and/or the security denotes a debt, an equity interest, and/or a hybrid; a derivatives contract, including at least one of: a future, a forward, a put, a call, an option, a swap, and/or any other transaction relating to a fluctuation of an underlying asset, notwithstanding the prevailing value of the contract, and notwithstanding whether such contract, for purposes of accounting, is considered an asset or liability; a fund; and/or an investment entity or account of any kind, including an interest in, or rights relating to: a hedge fund, an exchange traded fund (ETF), a fund of funds, a mutual fund, a closed end fund, an investment vehicle, and/or any other pooled and/or separately managed investments.

Another exemplary embodiment, the may be a method of constructing a portfolio of financial objects, including the steps of: determining overlapping financial objects appearing in both an accounting data based index (ADBI) and a conventional weighted index, where the conventionally weighted index may include an index weighted based on at least one of capitalization, equal weighting, and/or share price weighting, and where the ADBI may include weighting based on at least one accounting data based factor and not based on any of capitalization, equal weighting, and/or share price weighting index; comparing weightings of the overlapping financial objects in the ADBI with weightings of the overlapping financial objects in the conventionally weighted index; and/or purchasing at least one financial object based on the comparing.

The purchasing may include at least one of: purchasing a long position in at least one overlapping financial object when the comparing indicates the at least one overlapping financial object is over weighted in the non-capitalization weighted index relative to the conventional index; and/or purchasing a short position in at least one overlapping financial object when the comparing indicates the at least one overlapping financial object is underweighted in the non-capitalization weighted index relative to the conventional index.

The purchasing of the long and/or short positions may be implemented by using total return swaps. The long and/or short positions may be held for one year.

The embodiment may further include rebalancing the portfolio. The rebalancing may include: at least one of creating new long and/or short positions using cash flow from new capital contributions; and/or altering existing long and/or short positions using cash flow from new capital contributions.

The embodiment may further include using leverage to obtain the long and/or short positions.

The comparing may include calculating a difference between the weightings, and/or calculating a difference between arithmetically modified values of the weightings. The arithmetically modified values of the weightings may include square roots of the weightings.

The comparing may include calculating a difference based on tiers of weightings using stratified sampling.

The financial object may include: at least one unit of interest in at least one of: an asset; a liability; a tracking portfolio; a financial instrument and/or a security, where the financial instrument and/or the security denotes a debt, an equity interest, and/or a hybrid; a derivatives contract, including at least one of: a future, a forward, a put, a call, an option, a swap, and/or any other transaction relating to a fluctuation of an underlying asset, notwithstanding the prevailing value of the contract, and notwithstanding whether such contract, for purposes of accounting, is considered an asset or liability; a fund; and/or an investment entity or account of any kind, including an interest in, or rights relating to: a hedge fund, an exchange traded fund (ETF), a fund of funds, a mutual fund, closed end fund, an investment vehicle, and/or any other pooled and/or separately managed investments or accounts.

In another exemplary embodiment, the present invention may be a method of constructing a portfolio of financial objects, including the steps of: determining non-overlapping financial objects appearing in only one of either an accounting data based index (ADBI) or a conventional weighted index by comparing financial objects in an ADBI with financial objects in a conventionally weighted index, where the conventionally weighted index may include conventionally weighting based on at least one of capitalization, equal weighting, and/or share price weighting, and where the ADBI may include accounting data based weighting on at least one accounting data based factor and not based on any of capitalization, equal weighting, and/or share price weighting index; weighting the non-overlapping financial objects appearing only in the ADBI by accounting data based weighting; weighting the non-overlapping financial objects appearing only in the conventionally weighted index by the conventional weighting; and/or purchasing financial objects based on the weightings.

The accounting data based weighting may include: (a) gathering data about a plurality of financial objects; (b) selecting a plurality of financial objects to create an index of financial objects; and/or (c) weighting each of the plurality of financial objects selected in the index based on an objective measure of scale and/or size based on accounting data of a company associated with each of the plurality of financial objects, where the weighting may include: (i) weighting at least one of the plurality of financial objects based on accounting data; and/or (ii) weighting other than weighting based on at least one of market capitalization, equal weighting, and/or share price weighting.

The embodiment may further include weighting each of the plurality of financial objects, where each of the financial objects may include: at least one unit of interest in at least one of: an asset; a liability; a tracking portfolio; a financial instrument and/or a security, where the financial instrument and/or the security denotes a debt, an equity interest, and/or a hybrid; a derivatives contract, including at least one of: a future, a forward, a put, a call, an option, a swap, and/or any other transaction relating to a fluctuation of an underlying asset, notwithstanding the prevailing value of the contract, and notwithstanding whether such contract, for purposes of accounting, is considered an asset or liability; a fund; and/or an investment entity or account of any kind, including an interest in, or rights relating to: a hedge fund, an exchange traded fund (ETF), a fund of funds, a mutual fund, closed end fund, an investment vehicle, and/or any other pooled and/or separately managed investments.

An exemplary embodiment may further include weighting each of the plurality of financial objects, where the each of the financial objects may include a stock.

Exemplary objective measures of scale and/or size may include weighting based on any dividends, book value, cash flow, and/or revenue. An exemplary embodiment may include additional metrics. The embodiment may further include equally weighting each objective measure of scale and/or size.

The embodiment may further include weighting based on the objective measure of scale and/or size, where the objective measure of scale and/or size may include a measure of company size and/or country or industry sector size associated with each of the plurality of financial objects.

The measure of company size may include at least one of: inventory, revenue, sales, income, book income, taxable income, earnings growth rate, earnings before interest and tax (EBIT), earnings before interest, taxes, depreciation and amortization (EBITDA), retainer earnings, number of employees, capital expenditures, salaries, book value, assets, fixed assets, current assets, quality of assets, operating assets, intangible assets, dividends, gross dividends, dividend yields,

where r_f is the instantaneous risk free rate and λ_F is the risk premium for holding one unit of the factor risk exposure. It may be noted that the risk premium formula may be assumed. If the true stock price were observable and tradable, then the above equation (2) may arise naturally in equilibrium in the limit following the APT argument. The latter explicit relationship between factor exposure and expected returns may not be needed to drive most of the provided results. However, this relationship between factor loading and return may be considered intuitively appealing and may be necessary for analyzing the cross-section return variance and time series analysis in a CAPM context.

(2) β_i is stock i's factor loading.

(3) dW_F is an increment to a standard Wiener process and represents the common factor to all stocks.

(4) dW_νi is an increment to a standard Wiener process and represent idiosyncratic shocks to the true stock value. Additionally, it may be assumed that E[dW_νidW_νj]=0 for i≠j and E[dW_νidW_F]=0.

It may be noted that in an embodiment, there is only one risk factor in the exemplary modeled economy and risk premium may only be earned from holding exposure to this one factor risk.

It may further be assumed that the observed market price may be a noisy proxy for the true stock value. The market price may be defined by
P_i=V_iU_i,   (3)

• • where U_i is defined by
    U_i=1+Ũ_i,   (4)
  • where Ũ_i is a mean-reverting process defined by
    dŨ_i=(1+Ũ_i)(−ρ_iŨ_idt+σ_ŨidW_Ũi),   (5)
  • where 0≦ρ_i<1 and dW_Ũi is an increment to a standard Wiener process. It may be noted that when Ũ_i>0, the market price may be overvalued relative to the fair price. Additionally, it may be assumed that E[dW_ŨidW_Ũj]=0 for i≠j, E[dW_ŨidW_νj]=0 for all i and j, and E[dW_ŨidW_F]=0.

The market price dynamics can then be written as
dP_i=V_idU_i+U_idV_i.   (6)

Substituting, the following may be obtained
dP_i=V_iU_i(−ρ_iŨ_idt+σ_ŨidW_Ũi)+U_iV_i(μ_idt+β_iσ_FdW_F+σ_νidW_νi).   (7)

Rearranging, the mark-to-market return process is given by

$\begin{array}{cc}{\mathrm{dr}}_i\frac{{\mathrm{dP}}_i}{P_i}=\left({\mu }_i-{\rho }_i{\tilde{U}}_i\right)\mathrm{dt}+{\beta }_i{\sigma }_F{\mathrm{dW}}_F+{\sigma }_{\mathrm{ri}}{\mathrm{dW}}_{\mathrm{ri}},& \left(8\right)\end{array}$

where
σ_ridW_ri=σ_ŨidW_Ũi+σ_νidW_νi,   (9)

and where
σ_ri=√{square root over (σ_Ũi²+σ_νi²)}.   (10)

It may be noted from equation (8), that the mean-reverting pricing error process does not have an impact on the equity premium, though the cumulative return does suffer from the increased volatility. From equation (8), the mark-to-market return process may be mean-reverting, suggesting that observed stock returns are negatively autocorrelated. While empirical evidences may support negative autocorrelation, the literature may also conclude that the magnitude may be too small or the effect too unreliable to be profitably exploited given the volatility in stock returns. However, in an embodiment, it may be conceded that the mean-reversion in returns can be an uncomfortable prediction, especially in a partial equilibrium model. The 1986 teaching of Summers may be used to argue that standard statistical tests cannot reject the random walk hypothesis even when the true process is strongly mean-reverting; as such investors may not take large positions to trade on any perceived mean-reversion in stock returns.

The return on a portfolio Ω defined by a vector of weights {ω₁, ω₂, . . . ω_N} can be written as

$\begin{array}{cc}{\mathrm{dr}}_{\Omega }=\underset{i=1}{\sum ^N}{\omega }_i{\mathrm{dr}}_i=\left({\mu }_{\Omega }-\rho {\tilde{U}}_{\Omega }\right)\mathrm{dt}+{\beta }_{\Omega }{\sigma }_F\mathrm{dF}+{\sigma }_{\Omega }{\mathrm{dW}}_{\Omega },& \left(11\right)\\ \mathrm{where}& \\ {\mu }_{\Omega }=\underset{i=1}{\sum ^N}{\omega }_i{\mu }_i=r_f+{\beta }_{\Omega }\lambda ,& \left(12\right)\\ \rho {\tilde{U}}_{\Omega }=\underset{i=1}{\sum ^N}{\omega }_i{\rho }_i{\tilde{U}}_i,& \left(13\right)\\ {\beta }_{\Omega }=\underset{i=1}{\sum ^N}{\omega }_i{\beta }_i,& \left(14\right)\\ {\sigma }_{\Omega }{\mathrm{dW}}_{\Omega }=\underset{i=1}{\sum ^N}{\omega }_i{\sigma }_{\mathrm{ri}}{\mathrm{dW}}_{\mathrm{ri}},& \left(15\right)\\ \mathrm{where}& \\ {\sigma }_{\Omega }=\sqrt{\underset{i=1}{\sum ^N}{\omega }_i^2{\sigma }_{\mathrm{ri}}^2},& \left(16\right)\end{array}$
and where in the limiting case σ_ΩdW_Ω→0 and N→∞.

To derive additional portfolio implications it may be needed to make explicit the portfolio weighting scheme. In the following two sections, the portfolio return dynamics for a cap-weighted portfolio and a non-cap-weighted portfolio are considered.

For simplicity and without loss of generality, it may be assumed each company issues only 1 share of stock (therefore market price and market cap are the same). The cap-weighted portfolio may be the defined by the following vector of weights

$\begin{array}{cc}\mathrm{CW}=\left\{\frac{P_1}{P_{\Sigma }},\frac{P_2}{P_{\Sigma }},\dots \frac{P_N}{P_{\Sigma }}\right\},& \left(17\right)\\ \mathrm{where}& \\ P_{\Sigma }=\underset{i=1}{\sum ^N}{P}_i,& \left(18\right)\end{array}$

The return on the cap-weighted portfolio may then be
dr_CW=(μ_CW−ρŨ_CW)dt+β_CWσ_FdF+σ_CWdW_CW,   (19)

where

$\begin{array}{cc}{\mu }_{\mathrm{CW}}=\underset{i=1}{\sum ^N}\frac{P_i}{P_{\Sigma }}{\mu }_i=r_f+{\beta }_{\mathrm{CW}}\lambda ,& \left(20\right)\\ \sigma {\tilde{U}}_{\mathrm{CW}}=\underset{i=1}{\sum ^N}\frac{P_i}{P_{\Sigma }}{\rho }_i{\tilde{U}}_i=\underset{i=1}{\sum ^N}\frac{V_i}{P_{\Sigma }}{\rho }_i\left(1+{\tilde{U}}_i\right){\tilde{U}}_i,& \left(21\right)\\ {\beta }_{\mathrm{CW}}=\underset{i=1}{\sum ^N}\frac{P_i}{P_{\Sigma }}{\beta }_i,& \left(22\right)\\ {\sigma }_{\mathrm{CW}}{\mathrm{dW}}_{\mathrm{CW}}=\underset{i=1}{\sum ^N}\frac{P_i}{P_{\Sigma }}{\sigma }_{\mathrm{ri}}{\mathrm{dW}}_{\mathrm{ri}},& \left(23\right)\end{array}$

and where σ_CWdW_CW→0 as N→∞.

Rewriting the drift term for the portfolio dynamics in (19), the following may be obtained

$\begin{array}{cc}\left({\mu }_{\mathrm{CW}}-\underset{i=1}{\sum ^N}\frac{V_i}{P_{\Sigma }}{\rho }_i{\tilde{U}}_i^2\right)-\underset{i=1}{\sum ^N}\frac{V_i}{P_{\Sigma }}{\rho }_i{\tilde{U}}_i,& \left(24\right)\end{array}$

where

$-\underset{i=1}{\sum ^N}\frac{1}{P_{\Sigma }}{\rho }_i{V}_i{\tilde{U}}_i^2$
is strictly negative except when ρ_i=0 for all i (when pricing errors are not mean-reverting but random walks). The latter may be used to assert that cap-weighting leads to a drag in portfolio expected return.

While there may be only a finite number of stocks (this is both realistic and necessary to prevent arbitrage in our economy), the exposition may be more clear when the limiting case expression is examined. Though not necessary for the results provided here, the latter format may be used throughout the explanation hereof for improvement of intuition.

In the limiting case,

$\underset{i=1}{\sum ^N}\frac{V_i}{P_{\Sigma }}{\rho }_i{\tilde{U}}_i\to 0\mathrm{as}N\to \infty \mathrm{and}\underset{i=1}{\sum ^N}\frac{V_i}{P_{\Sigma }}{\rho }_i{\tilde{U}}_i^2\to {\delta }_{\mathrm{CW}}.$
Note δ_CW is monotone increasing in the average variance of the pricing noise in the stock cross-section. Equation (19) then reduces to
dr_CW=(μ_CW−δ_CW)dt+β_CWσ_FdF.   (25)

And the holding period return is
E_t[r_t,t+T]=E_te^∫tt+TdrCW=e^{(rj+βCWλ−δCW−0.5βCW2σF2)T}.  (26)

Equation (25) may suggest that in a well diversified portfolio constructed from cap-weighting, the portfolio expected return may be the cap-weighted expected returns of the constituent stocks less a drag term δ_CW. This return drag may occur because portfolio weights are positively correlated with prices. Stocks that are overvalued may receive added weights in the portfolio and stocks that are undervalued may receive lesser weights. The greater the mispricing in the market, the more severe may be this problem and the larger the resulting drag (δ_CW) to the cap-weighted portfolio.

Portfolio weights which do not depend on market capitalizations (or market prices) may be considered. The weights could be as arbitrary as random weights or as simple as equal weights.

The vector of weights may be denoted as
NC={w₁, w₂, . . . w_N},   (27)
The return on the non-cap-weighted portfolio may then be
dr_NC=(μ_NC−ρŨ_NC)dt+β_NCσ_FdF+σ_NCdW_NC,   (28)
where

$\begin{array}{cc}{\mu }_{\mathrm{NC}}=\underset{i=1}{\sum ^N}{w}_i{\mu }_i=r_f+{\beta }_{\mathrm{NC}}\lambda ,& \left(29\right)\\ \rho {\tilde{U}}_{\mathrm{NC}}=\underset{i=1}{\sum ^N}{w}_i{\rho }_i{\tilde{U}}_i,& \left(30\right)\\ {\beta }_{\mathrm{NC}}=\underset{i=1}{\sum ^N}{w}_i{\beta }_i,& \left(31\right)\\ {\sigma }_{\mathrm{NC}}{\mathrm{dW}}_{\mathrm{NC}}=\underset{i=1}{\sum ^N}{w}_i{\sigma }_{\mathrm{ri}}{\mathrm{dW}}_{\mathrm{ri}}.& \left(32\right)\end{array}$
The non-cap-weighted portfolio drift term may be

$\begin{array}{cc}{\mu }_{\mathrm{NC}}-\underset{i=1}{\sum ^N}{w}_i\rho {\tilde{U}}_i.& \left(33\right)\end{array}$
Comparing equation (33) to (24), it may be found that a non-cap-weighted portfolio does not suffer a drag in expected return.
In the limit, σ_NCdW_NC→0 and ρŨ_NC→0 as N→∞. Equation (28) may then reduce to

$\begin{array}{cc}{\mathrm{dr}}_i=\left({\mu }_i-{\rho }_i{\tilde{U}}_i-\frac{{\beta }_i}{{\beta }_{\mathrm{CW}}}\left({\mu }_{\mathrm{CW}}-\rho {\tilde{U}}_{\mathrm{CW}}\right)\right)\mathrm{dt}+\frac{{\beta }_i}{{\beta }_{\mathrm{CW}}}{\mathrm{dr}}_{\mathrm{CW}}-\frac{{\beta }_i}{{\beta }_{\mathrm{CW}}}{\sigma }_{\mathrm{CW}}{\mathrm{dW}}_{\mathrm{CW}}+{\sigma }_{\mathrm{ri}}{\mathrm{dW}}_{\mathrm{ri}}.& \left(37\right)\end{array}$

Additionally, a new process may be defined, the excess market return process
dR_M=dr_CW−r_fdt,  (38)

and a new variable

${\gamma }_i=\frac{{\beta }_i}{{\beta }_{\mathrm{CW}}}.$

Substituting into (37), the following is obtained
dr_i=(μ_i−ρ_iŨ_i−γ_i(μ_CW−r_f−ρŨ_CW))dt+γ_idR_M−γ_iσ_CWdW_CW+σ_ridW_ri.   (39)

Recalling equation (2), where μ_i=r_f+β_iλ_F, equation (39) can be rewritten as
dr_i=(r_f−ρ_iŨ_i+γ_iρŨ_CW)dt+γ_idR_M−γ_iσ_CWdW_CW+σ_ridW_ri.   (40)

In the limiting case as N→∞, the following may be obtained
dr_i=(r_f−ρ_iŨ_i+γ_iδ_CW)d_i+γ_idR_M+σ_ridW_ri.   (41)

• • It may be noted that the average stock may be expected to show an “alpha” equal to γ_iδ_CW when its excess stock return is regressed against the excess market return.

For a non-cap-weighted portfolio, equation (28) can be expressed as
dr_NC=(r_f−Ũ_NC+γ_NCρŨ_CW)dt+γ_NCdR_M−γ_NCσ_CWdW_CW+σ_NCdW_NC.   (42)
dr_NC=μ_Ndt+β_NCσ_FdF.   (34)
And the holding period return may be
E_t[r_t,t+T]=E_te^{17 tt+TdrNC}=e^{(rj+βNCλ−βNC2σF2)T}.  (35)

Comparing the expected cumulative holding period return for a cap-weighted portfolio and a non-cap-weighted portfolio of the same factor exposure or same β (the limiting case shown in (26) and (35)), it may be found that the non-cap-weighted portfolio has a higher return. In fact, in the limit, there is arbitrage as indicated by (34) and (25). Therefore, in an embodiment it may be considered important that in the economy, N is sufficiently different from infinity and/or that the factor loading β cannot be measured with perfect precision.

In the following embodiment, return dynamics for stocks and portfolios are expressed relative to the observed cap-weighted “market” portfolio instead of the unobserved factor F. This shift in measure may lead naturally to the CAPM regression formula and predict that in the stock cross-section, the average stock will show a CAPM alpha.

Rewriting equation (19),

$\begin{array}{cc}{\sigma }_F\mathrm{dF}=\frac{1}{{\beta }_{\mathrm{CW}}}{\mathrm{dr}}_{\mathrm{CW}}-\frac{\left({\mu }_{\mathrm{CW}}-\rho {\tilde{U}}_{\mathrm{CW}}\right)}{{\beta }_{\mathrm{CW}}}\mathrm{dt}-\frac{{\sigma }_{\mathrm{CW}}}{{\beta }_{\mathrm{CW}}}{\mathrm{dW}}_{\mathrm{CW}}.& \left(36\right)\end{array}$

For individual stocks, substituting into (8),

In the limiting case as N→∞,
dr_NC=(r_f+γ_NCδ_CW)dt+γ_NCdR_M.  (43)

A non-cap-weighted portfolio may be expected to show an “alpha” in a CAPM regression.

In the following embodiment, it may be shown that, in this economy, size and value exposure in a stock or portfolio can be used to predict future returns. Specifically, small size exposure and value exposure may lead to superior stock or portfolio returns, adjusting for “market” beta. By assumption, we may be in a one risk factor economy, and size and value may not be risk factors. The observed alpha in a CAPM regression may be driven purely by the return drag in the cap-weighted market portfolio.

Recalling from (40) that the individual stock return dynamics can be written as
dr_i=(r_f−ρ_iŨ_i+γ_iρŨ_CW)dt+γ_idR_M−γ_iσ_CWdW_CW+σ_ridW_ri.  (44)

Examining equation (44), it may be seen that a larger stock would on average have a negative drift term in excess of the risk free r_f. It may be straightforward to show that a larger stock, denoted by p_i> p, where p denote the capitalization of the average company, will have a greater chance of receiving a positive pricing error Ũ in the last period and therefore be more likely to underperform going forward as the positive pricing error reverts to zero.

More formally, since Ũ_i is a mean zero random variable, E[Ũ_i|P_i> P]>0 if the conditional probability Pr{Ũ_i>0|P_i> P}>Pr{Ũ_i>0}.

Using Bayes rule of conditional probability:

$\begin{array}{cc}\mathrm{Pr}\left\{{\tilde{U}}_i>0|P_i>\stackrel{\_}{P}\right\}=\frac{\mathrm{Pr}\left\{P_i>\stackrel{\_}{P}|{\tilde{U}}_i>0\right\}·\mathrm{Pr}\left\{{\tilde{U}}_i>0\right\}}{\mathrm{Pr}\left(P_i>\stackrel{\_}{P}\right\}}.& \left(45\right)\end{array}$

$\begin{array}{cc}\mathrm{Pr}\left\{{\tilde{U}}_i>0|P_i>\stackrel{\_}{P}\right\}=\frac{\mathrm{Pr}\left\{P_i>\stackrel{\_}{P}|{\tilde{U}}_i>0\right\}·\mathrm{Pr}\left\{{\tilde{U}}_i>0\right\}}{\mathrm{Pr}\left(P_i>\stackrel{\_}{P}\right\}}>\mathrm{Pr}\left\{{\tilde{U}}_i>0\right\},& \left(47\right)\end{array}$

• • which completes the proof that E[Ũ_i|P_i> P]>0. This in turn may prove that size predicts next period return, E[∫_t^t+Δdr_i|P_i,t> P_t]<E[∫_t^t+Δdr_i].

Similarly, it may be shown that, under some fairly general and reasonable assumptions on the book value process, a growth stock (as defined by above average price-to-book ratio or

$\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}\hspace{1em})$
may be more likely to have received a positive pricing error and therefore have a negative drift term in excess of the risk free r_f.

It may now be shown that

$E\left[{\tilde{U}}_i|\frac{P_i}{{\beta }_i}<\frac{\stackrel{\_}{P}}{B}\right]<0$ $\mathrm{and}$ $E\left[{\tilde{U}}_i|\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}\right]>0.$

Again, it is shown that

$\mathrm{Pr}\left\{{\tilde{U}}_i>0|\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}\right\}>\mathrm{Pr}\left\{{\tilde{U}}_i>0\right\}$
to prove that

$E\left[{\tilde{U}}_i|\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}\right]>0.$

First, Bayes rule gives:

$\begin{array}{cc}\mathrm{Pr}\left\{{\tilde{U}}_i>0|P_i>\stackrel{\_}{P}\right\}=\frac{\mathrm{Pr}\left\{\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}|{\tilde{U}}_i>0\right\}·\mathrm{Pr}\left\{{\tilde{U}}_i>0\right\}}{\mathrm{Pr}\left\{\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}\right\}}.& \left(48\right)\end{array}$

The following would need to be shown:

It is clear that:
Pr{P_i> P|Ũ_i>0}>Pr{P_i> P}  (46)

Substituting (46) into (45):

$\begin{array}{cc}\mathrm{Pr}\left\{\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}{\tilde{U}}_i>0\right\}>\mathrm{Pr}\left\{\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}\right\}.& \left(49\right)\end{array}$

• • A sufficient condition for this inequality to hold may be that the book value process B is not influenced by market mispricing Ũ_i as strongly as the price process P_i. More specifically, as long as the process for P_i/B_i has a drift term that is negative in Ũ_i, the inequality may bear true.

Hence, in an embodiment, if the book values of companies are not subjected to the effects of mispricings in stock prices, then

$E\left[{\tilde{U}}_i|\frac{P_i}{{\beta }_i}>\frac{\stackrel{\_}{P}}{B}\right]>0,$
which indicates that price-to-book ratio can predict next period return,

$E\left[{\int }_t^{t+\Delta }d{r}_i|\frac{P_{i,t}}{{\beta }_{i,t}}>\frac{{\stackrel{\_}{P}}_t}{B_t}\right]<E\left[{\int }_t^{t+\Delta }d{r}_i\right].$

The inequality in equation (49) can be extended to include more than just price-to-book ration ratios but also price-to-dividend and price-to-earnings ratios. This further explains the empirical observations that low yielding stocks and high P/E stocks tend to underperform.

Since conditional expectation may be considered linearly additive, based on the above, in another embodiment it may be straight forwardly shown that any portfolio which has smaller weighted average cap than the “market” portfolio would have a positive excess drift and would show a positive CAPM alpha in a time series regression. Similarly, any portfolio which has a lower price-to-book ratio (lower P/E or higher yield) than the “market” portfolio, would have a positive excess drift and show a positive CAPM alpha.

Exemplary Computer System Embodiments

FIG. 6 depicts an exemplary computer system that may be used in implementing an exemplary embodiment of the present invention. Specifically, FIG. 6 depicts an exemplary embodiment of a computer system 600 that may be used in computing devices such as, e.g., but not limited to, a client and/or a server, etc., according to an exemplary embodiment of the present invention. FIG. 6 depicts an exemplary embodiment of a computer system that may be used as client device 600, or a server device 600, etc. The present invention (or any 65 part(s) or function(s) thereof) may be implemented using hardware, software, firmware, or a combination thereof and may be implemented in one or more computer systems or other processing systems. In fact, in one exemplary embodiment, the invention may be directed toward one or more computer systems capable of carrying out the functionality described herein. An example of a computer system 600 may be shown in FIG. 6, depicting an exemplary embodiment of a block diagram of an exemplary computer system useful for implementing the present invention. Specifically, FIG. 6 illustrates an example computer 600, which in an exemplary embodiment may be, e.g., (but not limited to) a personal computer (PC) system running an operating system such as, e.g., (but not limited to) MICROSOFT® WINDOWS® NT/98/2000/XP/CE/ME/VISTA, etc. available from MICROSOFT® Corporation of Redmond, Wash., U.S.A. However, the invention may not be limited to these platforms. Instead, the invention may be implemented on any appropriate computer system running any appropriate operating system. In one exemplary embodiment, the present invention may be implemented on a computer system operating as discussed herein. An exemplary computer system, computer 600 may be shown in FIG. 6. Other components of the invention, such as, e.g., (but not limited to) a computing device, a communications device, mobile phone, a telephony device, a telephone, a personal digital assistant (PDA), a personal computer (PC), a handheld PC, an interactive television (iTV), a digital video recorder (DVD), client workstations, thin clients, thick clients, proxy servers, network communication servers, remote access devices, client computers, server computers, routers, web servers, data, media, audio, video, telephony or streaming technology servers, etc., may also be implemented using a computer such as that shown in FIG. 6. Services may be provided on demand using, e.g., but not limited to, an interactive television (iTV), a video on demand system (VOD), and via a digital video recorder (DVR), or other on demand viewing system.

The computer system 600 may include one or more processors, such as, e.g., but not limited to, processor(s) 604. The processor(s) 604 may be connected to a communication infrastructure 606 (e.g., but not limited to, a communications bus, cross-over bar, or network, etc.). Various exemplary software embodiments may be described in terms of this exemplary computer system. After reading this description, it may become apparent to a person skilled in the relevant art(s) how to implement the invention using other computer systems and/or architectures.

Computer system 600 may include a display interface 602 that may forward, e.g., but not limited to, graphics, text, and other data, etc., from the communication infrastructure 606 (or from a frame buffer, etc., not shown) for display on the display unit 630.

The computer system 600 may also include, e.g., but may not be limited to, a main memory 608, random access memory (RAM), and a secondary memory 610, etc. The secondary memory 610 may include, for example, (but not limited to) a hard disk drive 612 and/or a removable storage drive 614, representing a floppy diskette drive, a magnetic tape drive, an optical disk drive, a compact disk drive CD-ROM, etc. The removable storage drive 614 may, e.g., but not limited to, read from and/or write to a removable storage unit 618 in a well known manner. Removable storage unit 618, also called a program storage device or a computer program product, may represent, e.g., but not limited to, a floppy disk, magnetic tape, optical disk, compact disk, etc. which may be read from and written to by removable storage drive 614. As may be appreciated, the removable storage unit 618 may include a computer usable storage medium having stored therein computer software and/or data. In some embodiments, a “machine-accessible medium” may refer to any storage device used for storing data accessible by a computer. Examples of a machine-accessible medium may include, e.g., but not limited to: a magnetic hard disk; a floppy disk; an optical disk, like a compact disk read-only memory (CD-ROM) or a digital versatile disk (DVD); a magnetic tape; and/or a memory chip, etc.

In alternative exemplary embodiments, secondary memory 610 may include other similar devices for allowing computer programs or other instructions to be loaded into computer system 600. Such devices may include, for example, a removable storage unit 622 and an interface 620. Examples of such may include a program cartridge and cartridge interface (such as, e.g., but not limited to, those found in video game devices), a removable memory chip (such as, e.g., but not limited to, an erasable programmable read only memory (EPROM), or programmable read only memory (PROM) and associated socket, and other removable storage units 622 and interfaces 620, which may allow software and data to be transferred from the removable storage unit 622 to computer system 600.

Computer 600 may also include an input device 616 such as, e.g., (but not limited to) a mouse or other pointing device such as a digitizer, and a keyboard or other data entry device (not shown).

Computer 600 may also include output devices, such as, e.g., (but not limited to) display 630, and display interface 602. Computer 600 may include input/output (I/O) devices such as, e.g., (but not limited to) communications interface 624, cable 628 and communications path 626, etc. These devices may include, e.g., but not limited to, a network interface card, and modems (neither are labeled). Communications interface 624 may allow software and data to be transferred between computer system 600 and external devices.

In this document, the terms “computer program medium” and “computer readable medium” may be used to generally refer to media such as, e.g., but not limited to removable storage drive 614, a hard disk installed in hard disk drive 612, and signals 628, etc. These computer program products may provide software to computer system 600. The invention may be directed to such computer program products.

References to “one embodiment,” “an embodiment,” “example embodiment,” “various embodiments,” etc., may indicate that the embodiment(s) of the invention so described may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrase “in one embodiment,” or “in an exemplary embodiment,” do not necessarily refer to the same embodiment, although they may.

In the following description and claims, the terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms may be not intended as synonyms for each other. Rather, in particular embodiments, “connected” may be used to indicate that two or more elements are in direct physical or electrical contact with each other. “Coupled” may mean that two or more elements are in direct physical or electrical contact. However, “coupled” may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.

An algorithm may be here, and generally, considered to be a self-consistent sequence of acts or operations leading to a desired result. These include physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers or the like. It should be understood, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities.

Unless specifically stated otherwise, as apparent from the following discussions, it may be appreciated that throughout the specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices.

In a similar manner, the term “processor” may refer to any device or portion of a device that processes electronic data from registers and/or memory to transform that electronic data into other electronic data that may be stored in registers and/or memory. A “computing platform” may comprise one or more processors.

Embodiments of the present invention may include apparatuses for performing the operations herein. An apparatus may be specially constructed for the desired purposes, or it may comprise a general purpose device selectively activated or reconfigured by a program stored in the device.

In yet another exemplary embodiment, the invention may be implemented using a combination of any of, e.g., but not limited to, hardware, firmware and software, etc.

In one or more embodiments, the present embodiments are embodied in machine-executable instructions. The instructions can be used to cause a processing device, for example a general-purpose or special-purpose processor, which is programmed with the instructions, to perform the steps of the present invention. Alternatively, the steps of the present invention can be performed by specific hardware components that contain hardwired logic for performing the steps, or by any combination of programmed computer components and custom hardware components. For example, the present invention can be provided as a computer program product, as outlined above. In this environment, the embodiments can include a machine-readable medium having instructions stored on it. The instructions can be used to program any processor or processors (or other electronic devices) to perform a process or method according to the present exemplary embodiments. In addition, the present invention can also be downloaded and stored on a computer program product. Here, the program can be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem or network connection) and ultimately such signals may be stored on the computer systems for subsequent execution).

Exemplary Communications Embodiments

In one or more embodiments, the present embodiments are practiced in the environment of a computer network or networks. The network can include a private network, or a public network (for example the Internet, as described below), or a combination of both. The network includes hardware, software, or a combination of both.

From a telecommunications-oriented view, the network can be described as a set of hardware nodes interconnected by a communications facility, with one or more processes (hardware, software, or a combination thereof) functioning at each such node. The processes can inter-communicate and exchange information with one another via communication pathways between them called interprocess communication pathways.

On these pathways, appropriate communications protocols are used. The distinction between hardware and software may not be easily defined, with the same or similar functions capable of being preformed with use of either, or alternatives.

An exemplary computer and/or telecommunications net- work environment in accordance with the present embodiments may include node, which include may hardware, software, or a combination of hardware and software. The nodes may be interconnected via a communications network. Each node may include one or more processes, executable by processors incorporated into the nodes. A single process may be run by multiple processors, or multiple processes may be run by a single processor, for example. Additionally, each of the nodes may provide an interface point between network and the outside world, and may incorporate a collection of sub-networks.

As used herein, “software” processes may include, for example, software and/or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Also, each process may refer to multiple processes, for carrying out instructions in sequence or in parallel, continuously or intermittently.

In an exemplary embodiment, the processes may communicate with one another through interprocess communication pathways (not labeled) supporting communication through any communications protocol. The pathways may function in sequence or in parallel, continuously or intermittently. The pathways can use any of the communications standards, protocols or technologies, described herein with respect to a communications network, in addition to standard parallel instruction sets used by many computers.

The nodes may include any entities capable of performing processing functions. Examples of such nodes that can be used with the embodiments include computers (such as personal computers, workstations, servers, or mainframes), handheld wireless devices and wireline devices (such as personal digital assistants (PDAs), modem cell phones with processing capability, wireless e-mail devices including Black-Berry™ devices), document processing devices (such as scanners, printers, facsimile machines, or multifunction document machines), or complex entities (such as local-area networks or wide area networks) to which are connected a collection of processors, as described. For example, in the context of the present invention, a node itself can be a wide-area network (WAN), a local-area network (LAN), a private network (such as a Virtual Private Network (VPN)), or collection of networks.

Communications between the nodes may be made possible by a communications network. A node may be connected either continuously or intermittently with communications network. As an example, in the context of the present invention, a communications network can be a digital communications infrastructure providing adequate bandwidth and information security.

The communications network can include wireline communications capability, wireless communications capability, or a combination of both, at any frequencies, using any type of standard, protocol or technology. In addition, in the present embodiments, the communications network can be a private network (for example, a VPN) or a public network (for example, the Internet).

A non-inclusive list of exemplary wireless protocols and technologies used by a communications network may include BlueTooth™, general packet radio service (GPRS), cellular digital packet data (CDPD), mobile solutions platform (MSP), multimedia messaging (MMS), wireless application protocol (WAP), code division multiple access (CDMA), short message service (SMS), wireless markup language (WML), handheld device markup language (HDML), binary runtime environment for wireless (BREW), radio access network (RAN), and packet switched core networks (PS-CN). Also included are various generation wireless technologies. An exemplary non-inclusive list of primarily wireline protocols and technologies used by a communications network includes asynchronous transfer mode (ATM), enhanced interior gateway routing protocol (EIGRP), frame relay (FR), high-level data link control (HDLC), Internet control message protocol (ICMP), interior gateway routing protocol (IGRP), internetwork packet exchange (IPX), ISDN, point-to-point protocol (PPP), transmission control protocol/internet protocol (TCP/IP), routing information protocol (RIP) and user datagram protocol (UDP). As skilled persons will recognize, any other known or anticipated wireless or wireline protocols and technologies can be used.

The embodiments may be employed across different generations of wireless devices. This includes 1G-5G according to present paradigms. 1G refers to the first generation wide area wireless (WWAN) communications systems, dated in the 1970s and 1980s. These devices are analog, designed for voice transfer and circuit-switched, and include AMPS, NMT and TACS. 2G refers to second generation communications, dated in the 1990s, characterized as digital, capable of voice and data transfer, and include HSCSD, GSM, CDMA IS-95-A and D-AMPS (TDMA/IS-136). 2.5G refers to the generation of communications between 2G and 3 G. 3G refers to third generation communications systems recently coming into existence, characterized, for example, by data rates of 144 Kbps to over 2 Mbps (high speed), being packet-switched, and permitting multimedia content, including GPRS, 1xRTT, EDGE, HDR, W-CDMA. 4G refers to fourth generation and provides an end-to-end IP solution where voice, data and streamed multimedia can be served to users on an “anytime, anywhere” basis at higher data rates than previous generations, and will likely include a fully IP-based and integration of systems and network of networks achieved after convergence of wired and wireless networks, including computer, consumer electronics and communications, for providing 100 Mbit/s and 1 Gbit/s communications, with end-to-end quality of service and high security, including providing services anytime, anywhere, at affordable cost and one billing. 5G refers to fifth generation and provides a complete version to enable the true World Wide Wireless Web (WWWW), i.e., either Semantic Web or Web 3.0, for example. Advanced technologies may include intelligent antenna, radio frequency agileness and flexible modulation are required to optimize ad-hoc wireless networks.

As noted, each node 102-108 includes one or more processes 112, 114, executable by processors 110 incorporated into the nodes. In a number of embodiments, the set of processes 112, 114, separately or individually, can represent entities in the real world, defined by the purpose for which the invention is used.

Furthermore, the processes and processors need not be located at the same physical locations. In other words, each processor can be executed at one or more geographically distant processor, over for example, a LAN or WAN connection. A great range of possibilities for practicing the embodiments may be employed, using different networking hardware and software configurations from the ones above mentioned.

FIG. 7 depicts an exemplary embodiment of a chart 700 graphing cumulative returns by date for exemplary high yield debt instrument metrics according to an exemplary embodiment. FIG. 8 depicts block diagram 800 of an exemplary system according to an exemplary embodiment. The system may include an entity database 802 that, according to an exemplary embodiment, may store aggregated accounting based data and/or other data, metrics, measures, parameters, technical parameters, characteristics and/or factors about a plurality of entities, obtained from an external data source 804. Each database 802 entity may have at least one object type associated with the entity. The aggregated accounting based data may include, according to an exemplary embodiment, at least one non-market capitalization, non-price related objective measure of scale and/or size metric associated with each entity. The system may include an analysis host computer processing apparatus 102 coupled to the entity database 802. The analysis host computer processing apparatus 102 may include a data retrieval and storage subsystem 806, according to an exemplary embodiment, which may retrieve the aggregated accounting based data from the entity database and may store the aggregated accounting based data to the entity database 802. The analysis host computer processing apparatus 102 may include, according to an exemplary embodiment, an index generation subsystem 808, which may include, according to an exemplary embodiment, a selection subsystem 810 operative to select a group of the entities based on at least one non-market capitalization objective measure of scale or size metric including one or more technical parameters and/or metrics; a weighting function generation subsystem 812, according to an exemplary embodiment, may be operative to generate a weighting function based on at least one non-market capitalization, non-price related objective measure of scale and/or size metric; an exemplary index creation subsystem 814, according to an exemplary embodiment, may be operative to create a non-market capitalization non-price objective measure of scale and/or size index based on the group of selected entities and/or the weighting function; and/or a storing subsystem 816, according to an exemplary embodiment, operative to store the non-market capitalization, non-price related objective measure of scale and/or size based index, and/or multi-dimensional array of data objects. The index or array of data objects may be stored on a storage device, in one exemplary embodiment.

According to one exemplary embodiment, the system 800 may further include a normalization calculation and/or computation subsystem 818, operative to normalize entity object data to be stored in the entity database 802.

According to another exemplary embodiment, the system 800 may further include a trading host computer system 104 which may include, according to an exemplary embodiment, an index retrieval subsystem 820 operative to retrieve and/or store an instance of the non-market capitalization, non-price related objective measure of scale and/or size based index, and/or multi-dimensional array of data objects from a storage device; a trading accounts management subsystem 822 operative to manage accounts data relating to a plurality of accounts including positions data, position owner data, and position size data, any data of which may be stored in trading accounts database 108; and/or a purchasing subsystem 824 operative to purchase from an exchange host system 112 one or more positions for the position owner, according to the index and/or array of data objects.

Exemplary Process Control System

According to an exemplary embodiment, the system 800 may be used to compute using data objects input via an input/output subsystem, a multi-dimensional array storing database system for storage of a multi-dimensional array computed via a multi-dimensional object array creation sub-system comprising a selection subsystem operative to select one or more objects based on one or more technical parameters, and a weighting subsystem operative to weight the selected one or more objects based on one or more technical parameters, wherein the technical parameters are chosen such that the technical parameters avoid influence of an undesirable predetermined technical criterion and/or criteria, so as to avoid influence of the undesirable predetermined technical criterion and/or criteria. As a result of elimination of the undesirable predetermined technical criterion and/or criteria, the multi-dimensional array selected and/or weighted to avoid influence of the undesirable predetermined technical criterion and/or criteria may as a result perform processing from negative effects from the undesirable predetermined technical criterion and/or criteria. An exemplary embodiment of the selection subsystem may be operative to select objects from a predetermined universe of objects to obtain a subset of the universe, where the selection is based on a technical parameter that is not influenced by the undesirable technical criterion and/or criteria. Following execution of the selection subsystem, according to an exemplary embodiment, an exemplary weighting subsystem may operative to weight the resulting selected objects by a weighted combination of two or more technical weighting criteria, which are not influenced by the undesirable technical criterion and/or criteria. The process may be used for such technical processes as may include, e.g. but are not limited to, industrial automation, production process automation, a manufacturing process, and/or a chemical processing system, among others as described elsewhere, herein.

According to one exemplary embodiment, the weighting subsystem may further compute an algorithmically computed summation of a plurality of weighting factors, the plurality of weighting factors including a first of the plurality of weighting factors, where the first includes a first given computational product of a first object value and a first technical parameter value associated with the first object value, and a second of the plurality of weighting factors, where the second includes a second given computational product of a second object value and a second technical parameter value associated with the second object value, and/or any additional of the plurality of weighting factors, where the any additional includes an additional given computational product of an additional object value and an additional technical parameter value associated with the additional object value.

FIG. 9 depicts an exemplary embodiment of a chart 900 graphing cumulative returns by date for exemplary emerging market debt instrument metrics according to an exemplary embodiment.

FIG. 10 depicts an exemplary embodiment of a chart 1000 graphing cumulative returns by date for exemplary emerging market debt instrument metrics illustrating growth of an exemplary investment, according to an exemplary embodiment.

FIG. 11 depicts an exemplary embodiment of a chart 1100 graphing a rolling 36-month value added composite exemplary emerging market debt instrument metrics vs. cap-weighted emerging market bonds, according to an exemplary embodiment.

While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should instead be defined only in accordance with the following claims and their equivalents.

Claims

1. A computer-implemented method for constructing data indicative of a financial object index, the method comprising: constructing, by at least one computer processor, data indicative of the financial object index comprising:

selecting, by the at least one computer processor, data indicative of constituent financial objects of the financial object index based upon at least one accounting data rather than price of said financial objects, said at least one accounting data regarding at least one entity relating to each of said financial objects, the at least one entity comprising at least one of a region, a country, a company, or a sovereign associated with said each of said financial objects; and

weighting, by the at least one computer processor, data indicative of said constituent financial objects based upon at least one accounting data rather than price of said financial objects, said at least one accounting data regarding the at least one entity, to obtain data indicative of constituent weightings for each of said constituent financial objects, and

storing, by the at least one computer processor, said data indicative of said constituent financial objects and said constituent weightings on at least one computer storage device,

wherein each of said financial objects comprises at least one instrument,

wherein each of said financial objects comprises at least one of:

a bond;

a fixed income debt instrument;

a debt instrument;

an emerging market debt instrument;

a high yield debt instrument;

a corporate debt instrument;

an investment grade debt instrument;

a debenture;

a bank loan;

a convertible bond;

a senior debt;

a subordinated debt;

a term loan;

a government debt instrument;

a government bond;

a corporate bond;

a high yield bond;

an emerging market bond;

a municipal bond debt instrument;

a treasury bond debt instrument;

a treasury bill debt instrument;

a mortgage based debt instrument;

a securitized debt instrument;

a security;

a stock;

a commodity;

a futures contract;

a mutual fund;

a hedge fund;

a fund of funds;

an exchange traded fund (ETF);

a derivative;

a negative weighting on any asset;

an asset account;

a separate account;

a pooled trust; or

a limited partnership; and wherein said at least one accounting data comprises at least one of:

at least one financial metric of the at least one entity associated with each of said constituent financial objects; or

at least one metric, level, rate, or expenditure amount from information disclosures of the at least one entity.

2. The computer-implemented method of claim 1, wherein said weighting is not based on any of: equal weighting, weighting in proportion to share price, weighting in proportion to market capitalization, or weighting in proportion to free float.

3. The computer-implemented method of claim 1, wherein the accounting data comprises data found within a database of information pertaining to at least one of: regions, sovereigns or countries.

4. The computer-implemented method of claim 1, further comprising: basing the constituent weightings of the financial objects, wherein the financial objects comprise emerging market financial objects, upon at least one of: a ratio, a mathematical transformation, or a manipulation of the accounting data.

5. The computer-implemented method of claim 1, further comprising constructing, by the at least one computer processor, data indicative of a financial object portfolio based on the financial object index, wherein the constituent weightings of the constituent financial objects within at least one of the financial object index or the financial object portfolio are altered as at least one of the at least one accounting data about the at least one entity in or outside the financial object index changes.

6. The computer-implemented method of claim 1, wherein said weighting comprises: calculating said constituent weightings based upon said at least one accounting data.

7. The computer-implemented method of claim 1, wherein said at least one accounting data, comprises at least one of: total assets, funds from operations (FFO), adjusted funds from operations (AFFO), sales, revenues, total dividend distributions, or ratios pertaining thereto.

8. The computer-implemented method of claim 1, wherein the at least one accounting data, comprises data found within a generally accepted accounting principles (GAAP) company annual report and accounts (GAAP reports).

9. The computer-implemented method of claim 1, wherein said weighting based on said at least one accounting data further comprises weighting based on at least one demographic data comprising at least one of:

country metrics including at least one of: economic metrics, area, population, unemployment rate, reserves, resource consumption, democracy index, corruption index, government debt, private debt, government expenditures, nominal interest rate, commercial paper yield, consumer price index (CPI), purchasing power, relation of purchasing power to nominal exchange rate and any deviations from historical trend, or country current account flow; wherein said economic metrics including at least one of: a gross domestic product (GDP), a gross national product (GNP), a gross net income (GNI), or a gross national debt (GND); or

industry metrics including at least one of: industry growth rate, total capital expenditures, inventories total—end of year, average industry dividends, supplemental labor costs, inventories finished products—end of year, new orders for manufactured goods, fuel costs, inventories work in process—end of year, shipments, electric energy used, inventories, materials, supplies, or fuels,—end of year, unfilled orders, inventories by stage of fabrication, value of manufacturers inventories by stage of fabrication—beginning of year, Inventories Number of production workers, inventories total—beginning of year, inventories-to-shipments ratio, payroll of production workers, inventories finished products—beginning of year, value of product shipments, hours of production workers, inventories work in process—beginning of year, statistics from department of commerce, industry associations, for industry groups and industries, cost of purchased fuels and electric energy, inventories, materials, supplies, fuels, —beginning of year, geographic area statistics, electric energy quantity purchased, value of shipments—total, annual survey of manufacturers (ASM), electric energy cost, value of shipments—products, employment, electric energy generated, value of shipments—total miscellaneous receipts, all employees payroll, electric energy sold or transferred, total miscellaneous receipts—value of resales, all employees hours, cost of purchased fuels, total miscellaneous receipts—contract receipts, all employees total, compensation, capital expenditure for plant or equipment total, other total miscellaneous receipts, all employees total fringe benefit costs, capital expenditure for plant or equipment—buildings or other structures, interplant transfers, total cost of materials, capital expenditure for plant and equipment—machinery or equipment total, costs of materials—total, payroll, capital expenditure for plant and equipment—autos, or trucks, for highway use, costs of materials—materials, parts, containers, packaging, value added by manufacture, capital expenditure for plant and equipment—computers, peripheral data processing equipment, costs of materials—resales, cost of materials consumed, capital expenditure for plant and equipment—all other expenditures, costs of materials—purchased fuels, value of shipments, value of manufacturers inventories by stage of fabrication—end of year, costs of materials—purchased electricity, costs of materials—contract work, industry cost of capital, or average industry dividend.

10. The computer-implemented method of claim 1, wherein said financial objects are selected from a universe comprising at least one of:

a sector;

a market;

a market sector;

an industry sector;

a geographic sector;

an international sector;

a sub-industry sector;

a government issue; or

a tax exempt financial object;

agriculture, forestry, fishing or hunting industry sector;

mining industry sector;

utilities industry sector;

construction industry sector;

manufacturing industry sector;

wholesale trade industry sector;

retail trade industry sector;

transportation or warehousing industry sector;

information industry sector;

finance or insurance industry sector;

real estate and/or rental or leasing industry sector;

professional, scientific, or technical services industry sector;

management of companies and/or enterprises industry sector;

administrative or support or waste management or remediation services industry sector;

education services industry sector;

health care or social assistance industry sector;

arts, entertainment, or recreation industry sector;

accommodation or food services industry sector;

other services (except public administration) industry sector; or

public administration industry sector.

11. The computer-implemented method of claim 1, wherein said at least one accounting data rather than price comprises at least one of:

dividends, if any;

sales;

revenue;

cash flow;

book value;

collateral;

assets;

distributions;

funds from operations;

adjusted funds from operations;

earnings;

income;

liquidity;

employees;

margin;

profit margin;

term structure;

interest rate;

seasonal factor;

a financial ratio of a company;

a ratio of accounting based data;

a ratio of accounting based data per share;

a ratio of a first accounting based data to a second accounting based data;

a liquidity ratio;

a working capital ratio;

a current ratio;

a quick ratio;

a cash ratio;

an asset turnover ratio;

a receivables turnover ratio;

an average collection period ratio;

an average collection period ratio;

an inventory turnover ratio;

an inventory period ratio;

a leverage ratio;

a debt ratio;

a debt-to-equity ratio;

an interest coverage ratio;

a profitability ratio;

a return on common equity (ROCE) ratio;

profit margin ratio;

an earnings per share (EPS) ratio;

a gross profit margin ratio;

a return on assets ratio;

a return on equity ratio;

a dividend policy ratio; or

a dividend yield ratio;

a payout ratio;

a capital market analysis ratio;

a price to earnings (PE) ratio; or

a market to book ratio.

12. The computer-implemented method of claim 1, further comprising performing negative weighting for purposes of at least one of establishing or measuring performance for at least one of:

any security;

a portfolio of assets;

a hedge fund; or

a long/short position.

13. The computer-implemented method of claim 1, wherein said at least one accounting data comprises a measure of size of the at least one entity.

14. The computer-implemented method of claim 1, wherein said at least one accounting data is not market capitalization.

15. The computer-implemented method of claim 14, wherein each of said financial objects comprises a financial object type.

16. The computer-implemented method of claim 15, wherein said financial object type comprises at least one of:

a municipal bond;

a corporate bond;

a sovereign bond;

a government bond;

a fixed income instrument;

a real estate fixed income instrument;

an investment grade bond;

a high yield bond;

an emerging market bond

a debt instrument; or

a bond from a geographic region; and

the at least one entity comprises at least one of:

a company;

a country;

a government;

a geographic region;

a market;

a municipality;

a municipality issuing bonds;

a sovereign;

a sovereign issuing bonds; or

a commodity provider.

17. The computer-implemented method of claim 1, wherein said at least one accounting data, comprises at least one of:

revenue;

profitability;

sales;

total sales;

foreign sales,

domestic sales;

net sales;

gross sales;

profit margin;

operating margin;

earnings;

retained earnings;

earnings per share;

book value;

book value adjusted for inflation;

book value adjusted for replacement cost;

book value adjusted for liquidation value;

dividends;

assets;

tangible assets;

intangible assets;

fixed assets;

property;

plant;

equipment;

goodwill;

replacement value of assets;

liquidation value of assets;

liabilities;

long term liabilities;

short term liabilities;

net worth;

research and development expense;

accounts receivable;

earnings before interest and tax (EBIT);

earnings before interest, taxes, dividends, and amortization (EBITDA);

accounts payable;

cost of goods sold (CGS);

debt ratio;

budget;

capital budget;

cash budget;

direct labor budget;

factory overhead budget;

operating budget;

sales budget;

inventory system;

type of stock offered;

liquidity;

book income;

tax income;

capitalization of earnings;

capitalization of goodwill;

capitalization of interest;

capitalization of revenue;

capital spending;

cash;

compensation;

employee turnover;

overhead costs;

credit rating;

growth rate;

tax rate;

liquidation value of entity;

capitalization of cash;

capitalization of earnings;

capitalization of revenue;

cash flow; or

future value of expected cash flow.

18. The computer-implemented method of claim 1, wherein said at least one accounting data comprises a ratio of any combination of two or more non-market capitalization objective measure of scale metrics.

19. The computer-implemented method of claim 18, wherein said ratio of any combination of said objective measure of scale metrics comprise at least one of:

current ratio;

debt ratio;

overhead expense as a percent of sales; or

debt service burden ratio.

20. The computer-implemented method of claim 1, wherein said weighting comprises weighting based on at least one demographic data, comprising at least one of:

a measure relating to employees;

a financial metric;

a non-financial metric;

a non-market related metric;

a number of employees;

floor space;

office space;

a geographic metric;

an area;

a geographic area;

a measure of inhabitants;

a population;

location; or

other demographics of a financial object.

21. The computer-implemented method of claim 1, wherein said at least one accounting data comprises a metric relating to a geographic area.

22. The computer-implemented method of claim 21, wherein said metric relating to said geographic area comprises a geographic metric comprising at least one of: an economic metric; a gross domestic product; or an other than gross domestic product (GDP) metric.

23. The computer-implemented method of claim 1, further comprising: rebalancing a pre-selected group of said financial objects based on said financial object index.

24. The computer-implemented method of claim 23, wherein said rebalancing is performed on a periodic basis.

25. The computer-implemented method of claim 23, wherein said rebalancing is based upon a predetermined threshold.

26. The computer-implemented method of claim 25, further comprising: applying one or more rules associated with said index.

27. The computer-implemented method of claim 1, wherein at least one of said selecting or said weighting are used for at least one of:

investment management, or

investment portfolio benchmarking.

28. The computer-implemented method of claim 1, further comprising performing enhanced index investing, comprising: constructing, by the at least one computer processor, data indicative of a financial object portfolio of financial objects based on said financial object index, comprising at least one of purchasing or selling financial objects in said financial object portfolio based on said financial object index, wherein said enhanced index investing is performed in a fashion wherein at least one of: holdings; performance; or characteristics, are substantially similar to an external index.

29. The computer-implemented method of claim 1, further comprising:

wherein said selecting based on said at least one accounting data comprises:

selecting based on at least one demographic data regarding the at least one entity.

30. The computer-implemented method of claim 29, wherein the method further comprises:

wherein said at least one demographic data comprises at least one of:

a demographic measure,

a population level,

an area,

a geographic area,

an economic factor,

a gross domestic product (GDP),

GDP growth,

a natural resource characteristic,

an energy metric,

a petroleum characteristic,

a resource consumption metric,

a petroleum consumption amount,

a liquid natural gas (LNG) characteristic,

a liquefied petroleum gas (LPG) characteristic,

an expenditures characteristic,

gross national income (GNI),

a debt characteristic,

a rate of inflation,

a rate of unemployment,

a reserves level,

a population characteristic,

a corruption characteristic,

a democracy characteristic,

a social metric,

a political metric,

a per capita ratio of any of the foregoing;

a derivative of any foregoing; or

a ratio of any two or more of the foregoing.

31. The computer-implemented method of claim 1, further comprising:

wherein said weighting based on said at least one accounting data comprises:

weighting based on at least one demographic data regarding the at least one entity.

32. The computer-implemented method of claim 31, further comprising:

wherein said at least one demographic data comprises at least one of:

a demographic measure,

a population level,

an area,

a geographic area,

an economic factor,

a gross domestic product (GDP),

GDP growth,

a natural resource characteristic,

an energy metric,

a petroleum characteristic,

a resource consumption metric,

a petroleum consumption amount,

a liquid natural gas (LNG) characteristic,

a liquefied petroleum gas (LPG) characteristic,

an expenditures characteristic,

gross national income (GNI),

a debt characteristic,

a rate of inflation,

a rate of unemployment,

a reserves level,

a population characteristic,

a corruption characteristic,

a democracy characteristic,

a social metric,

a political metric,

a per capita ratio of any of the foregoing;

a derivative of any foregoing; or

a ratio of any two or more of the foregoing.

33. The computer-implemented method according to claim 1, wherein the method is executed on a data processing system, the method further comprising:

wherein said constructing comprises:

creating an accounting data based index (ADBI) based on accounting data comprising wherein said selecting further comprises:

selecting a universe of financial objects,

selecting a subset of said financial objects of said universe based on at least one accounting data, and

weighting said subset of said universe according to at least one accounting data to obtain the ADBI; and

creating a portfolio of financial objects using the ADBI, including said subset of selected and weighted financial objects.

34. The computer-implemented method according to claim 33, wherein said universe comprises at least one of:

a sector;

a market;

a market sector;

an industry sector;

a geographic sector;

an international sector;

a sub-industry sector;

a government issue; or

a tax exempt financial object;

agriculture, forestry, fishing or hunting industry sector;

mining industry sector;

utilities industry sector;

construction industry sector;

manufacturing industry sector;

wholesale trade industry sector;

retail trade industry sector;

transportation or warehousing industry sector;

information industry sector;

finance or insurance industry sector;

real estate or rental or leasing industry sector;

professional, scientific, or technical services industry sector;

management of companies or enterprises industry sector;

administrative or support or waste management or remediation services industry sector;

education services industry sector;

health care or social assistance industry sector;

arts, entertainment, or recreation industry sector;

accommodation or food services industry sector;

other services (except public administration) industry sector; or

public administration industry sector.

35. The computer-implemented method according to claim 33, wherein said accounting based data rather than price used in weighting as a measure of value of the at least one entity associated with each of the financial objects, comprises at least one of:

dividends, if any;

revenue;

cash flow;

book value;

collateral;

assets;

distributions;

funds from operations;

adjusted funds from operations;

earnings;

income;

liquidity;

country metrics including at least one of: economic metrics, area, population, unemployment rate, reserves, resource consumption, democracy index, corruption index, government debt, private debt, government expenditures, nominal interest rate, commercial paper yield, consumer price index (CPI), purchasing power, relation of purchasing power to nominal exchange rate and any deviations from historical trend, or country current account flow;

said economic metrics including at least one of: a gross domestic product (GDP), a gross national product (GNP), a gross net income (GNI), or a gross national debt (GND);

industry metrics including at least one of: industry growth rate, total capital expenditures, inventories total—end of year, average industry dividends, supplemental labor costs, inventories finished products—end of year, new orders for manufactured goods, fuel costs, inventories work in process—end of year, shipments, electric energy used, inventories, materials, supplies, or fuels,—end of year, unfilled orders, inventories by stage of fabrication, value of manufacturers inventories by stage of fabrication—beginning of year, Inventories Number of production workers, inventories total—beginning of year, inventories-to-shipments ratio, payroll of production workers, inventories finished products—beginning of year, value of product shipments, hours of production workers, inventories work in process—beginning of year, statistics from department of commerce, industry associations, for industry groups and industries, cost of purchased fuels and electric energy, inventories, materials, supplies, fuels, —beginning of year, geographic area statistics, electric energy quantity purchased, value of shipments—total, annual survey of manufacturers (ASM), electric energy cost, value of shipments—products, employment, electric energy generated, value of shipments—total miscellaneous receipts, all employees payroll, electric energy sold or transferred, total miscellaneous receipts—value of resales, all employees hours, cost of purchased fuels, total miscellaneous receipts—contract receipts, all employees total, compensation, capital expenditure for plant or equipment total, other total miscellaneous receipts, all employees total fringe benefit costs, capital expenditure for plant or equipment—buildings or other structures, interplant transfers, total cost of materials, capital expenditure for plant and equipment—machinery or equipment total, costs of materials—total, payroll, capital expenditure for plant and equipment—autos, or trucks, for highway use, costs of materials—materials, parts, containers, packaging, value added by manufacture, capital expenditure for plant and equipment—computers, peripheral data processing equipment, costs of materials—resales, cost of materials consumed, capital expenditure for plant and equipment—all other expenditures, costs of materials—purchased fuels, value of shipments, value of manufacturers inventories by stage of fabrication—end of year, costs of materials—purchased electricity, costs of materials—contract work, industry cost of capital, or average industry dividend;

employees;

margin;

profit margin;

term structure;

interest rate;

seasonal factor;

a financial ratio of a company;

a ratio of accounting based data;

a ratio of accounting based data per share;

a ratio of a first accounting based data to a second accounting based data;

a liquidity ratio;

a working capital ratio;

a current ratio;

a quick ratio;

a cash ratio;

an asset turnover ratio;

a receivables turnover ratio;

an average collection period ratio;

an average collection period ratio;

an inventory turnover ratio;

an inventory period ratio;

a leverage ratio;

a debt ratio;

a debt-to-equity ratio;

an interest coverage ratio;

a profitability ratio;

a return on common equity (ROCE) ratio;

profit margin ratio;

an earnings per share (EPS) ratio;

a gross profit margin ratio;

a return on assets ratio;

a return on equity ratio;

a dividend policy ratio; or

a dividend yield ratio;

a payout ratio;

a capital market analysis ratio;

a price to earnings (PE) ratio; or

a market to book ratio.

36. The computer-implemented method according to claim 35, wherein said accounting based data are weighted relatively dependent on the geography of the at least one entity associated with each of the financial objects.

37. The computer-implemented method of claim 33, wherein each of said financial objects comprises:

at least one unit of interest in at least one of:

an asset;

a liability;

a tracking portfolio;

a financial instrument or a security, wherein said financial instrument or said security denotes a debt, an equity interest, or a hybrid;

a derivatives contract, including at least one of:

a future, a forward, a put, a call, an option, a swap, or any other transaction relating to a fluctuation of an underlying asset, notwithstanding the prevailing value of the contract, and notwithstanding whether such contract, for purposes of accounting, is considered an asset or liability;

a commodity;

a financial position;

a currency position;

a trust, a real estate investment trust (REIT), a real estate operating company (REOC), or a portfolio of trusts;

a debt instrument comprising at least one of: a bond, a debenture, a subordinated debenture, a mortgage bond, a collateral trust bond, a convertible bond, an income bond, a guaranteed bond, a serial bond, a deep discount bond, a zero coupon bond, a variable rate bond, a deferred interest bond, a commercial paper, a government security, a certificate of deposit, a Eurobond, a corporate bond, a government bond, a municipal bond, a treasury-bill, a treasury bond, a foreign bond, an emerging market bond, a high yield bond, a developed market bond, a junk bond, a collateralized instrument, an exchange traded note (ETN), or other agreements between a borrower and a lender;

a fund; or

an investment entity or account of any kind, including an interest in, or rights relating to:

a hedge fund, an exchange traded fund (ETF), a fund of funds, a mutual fund, a closed end fund, an investment vehicle, or any other pooled or separately managed investments.

38. The computer-implemented method of claim 1, wherein said financial objects comprise at least one of:

a high-yield debt instruments index; or

a portfolio of high-yield debt instruments based on the high yield debt instruments index, the method further comprising:

selecting constituent high-yield debt instruments of said high-yield debt instruments index based upon at least one metric regarding the entities associated with said high-yield debt instruments,

wherein said at least one metric comprises at least one of sales, book value, cash flow, dividends if any, collateral, a composite of the other metrics, or ratios pertaining thereto; and

weighting said constituent high-yield debt instruments based upon at least one metric regarding the size of the entities associated with said high-yield debt instruments to obtain constituent weightings for each respective constituent high-yield debt instrument,

wherein said at least one metric comprises at least one of sales, book value, cash flow, dividends if any, collateral, a composite of the other metrics, or ratios pertaining thereto.

39. The computer-implemented method of claim 38, wherein said at least one metric comprises data found within a generally accepted accounting principles (GAAP) company annual report and accounts (GAAP reports).

40. The computer-implemented method of claim 38, further comprising basing the constituent weightings of the high-yield debt instruments upon at least one of a ratio or a manipulation of the accounting data.

41. The computer-implemented method of claim 40, wherein the basing the constituent weightings upon at least one of a ratio or a manipulation of the accounting data comprises basing the constituent weightings on at least one of: a relative size of the return on assets of said selected companies, the return on investment thereof, or the return on capital thereof compared to the cost of capital thereof, wherein said return is determined based on cash flow.

42. The computer-implemented method of claim 38, wherein the constituent weightings of the high-yield debt instruments within the high-yield debt instruments index or high yield debt instruments fund are altered as at least one of: the accounting data concerning the entities in or outside the index changes; or the constituents change.

43. The computer-implemented method of claim 42, wherein the constituent weightings of the high-yield debt instruments within the fund are altered when at least one of: one or more of said entities report their at least one of: monthly, quarterly, biannually, or annual accounting information; or at a pre-determined time after which the majority of said entities in the index have reported their at least one of: monthly, quarterly, biannually, or annual accounting data.

44. The computer-implemented method of claim 38, wherein said weighting comprises calculating said constituent weightings based upon said at least one accounting data.

45. The computer-implemented method of claim 44, wherein said calculating is performed by an index construction manager device.

46. The computer-implemented method of claim 1, wherein said weighting comprises:

determining, by the at least one computer processor, a proportional fundamental index weight for each of said index constituent financial objects based on at least one objective measure of scale associated with said entities or said financial objects;

wherein said at least one objective measure of scale comprises a financial metric associated with one of said entities or said financial objects other than the market capitalization of said entities or the price of said financial objects;

wherein said financial metric comprises at least one of: book value; sales; cash flow; or any dividends; and

managing, by the at least one computer processor, a portfolio of financial objects based on said index of financial objects, wherein said managing comprises at least one of:

adjusting, by the at least one computer processor, the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to weight the plurality of financial objects used to construct the index of financial objects;

adjusting, by the at least one computer processor, the relative weightings of the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to weight the plurality of financial objects used to construct the index of financial objects;

rebalancing, by the at least one computer processor, the relative weightings of the financial objects that comprise said portfolio when the weighting of one or more of said financial objects at least one of: exceeds a threshold value, or deviates from a target weight; or

rebalancing the relative weightings of the financial objects that comprise said portfolio to minimize turnover of said financial objects.

47. The computer-implemented method of claim 1, wherein said weighting comprises:

determining, by the at least one computer processor, a proportional fundamental index weight for each of said index constituent financial objects based on at least one objective measure of scale associated with said entities or said financial objects;

wherein said at least one objective measure of scale comprises a financial metric associated with one of said entities or said financial objects other than the market capitalization of said entities or the price of said financial objects;

wherein said financial metric comprises at least one of: book value; sales; cash flow; or any dividends;

wherein said weighting comprises:

weighting, by the at least one computer processor, by a mathematical combination of a plurality of financial metric data for a given financial object of a given entity, said plurality of financial metric data of said given financial object of said given entity, comprising at least one:

a plurality of time periods;

a plurality of years;

a plurality of quarters;

a plurality of months; or

a plurality of accounting periods; and

wherein said mathematical combination of said plurality of financial metric data for said given financial object of said given entity, comprises at least one of:

calculating, by the at least one computer processor, a mathematical average of said plurality of financial metric data of said given financial object of said given entity;

calculating, by the at least one computer processor, a mathematical weighted average of said plurality of financial metric data of said given financial object of said given entity;

calculating, by the at least one computer processor, a statistical mean of said plurality of financial metric data of said given financial object of said given entity;

calculating, by the at least one computer processor, a statistical median of said plurality of financial metric data of said given financial object of said given entity; or

calculating, by the at least one computer processor, a midpoint of said plurality of financial metric data of said given financial object of said given entity.

48. The computer-implemented method of claim 1, further comprising:

receiving a plurality of historical data of a plurality of financial metrics of a plurality of financial objects, said plurality of financial objects comprising publicly traded entities; and

wherein said weighting comprises:

weighting, by the at least one computer processor, a plurality of index constituent financial objects, each of said plurality of index constituent financial objects associated with at least one entity, and

wherein said weighting comprises:

determining, by the at least one computer processor, a proportional fundamental index weight for each of said index constituent financial objects based on at least one objective measure of scale associated with said entities or said financial objects;

wherein said at least one objective measure of scale comprises at least one of:

at least one financial metric associated with one of said entities or said financial objects;

at least one demographic measure of one of said entities or said financial objects; or

at least one metric from information disclosures of a publicly traded entity; and

wherein said at least one objective measure of scale comprises a metric other than the market capitalization of said entities or the price of said financial objects; and

weighting, by the at least one computer processor, by a mathematical combination of a plurality of data for said at least one objective measure of scale of a given financial object of a given entity, said plurality of data for said at least one objective measure of scale of said given financial object of said given entity, comprising at least one:

a plurality of time periods;

a plurality of years;

a plurality of quarters;

a plurality of months; or

a plurality of accounting periods; and

wherein said mathematical combination of said plurality data for said given financial object of said given entity, comprises at least one of:

calculating, by the at least one computer processor, a mathematical average of said plurality of data for said given financial object of said given entity;

calculating, by the at least one computer processor, a mathematical weighted average of said plurality of financial metric data of said given financial object of said given entity;

calculating, by the at least one computer processor, a statistical mean of said plurality of financial metric data of said given financial object of said given entity;

calculating, by the at least one computer processor, a statistical median of said plurality of financial metric data of said given financial object of said given entity; or

calculating, by the at least one computer processor, a midpoint of said plurality of financial metric data of said given financial object of said given entity.

49. The computer-implemented method of claim 48, further comprising:

normalizing, by the at least one computer processor, data over a plurality of time periods.

50. The computer-implemented method of claim 48, further comprising:

rebalancing, by the at least one computer processor, said index on a periodic basis.

51. The computer-implemented method of claim 50, wherein said rebalancing said index on a periodic basis comprises at least one of:

rebalancing, by the at least one computer processor, on a yearly basis;

rebalancing, by the at least one computer processor, on a quarterly basis;

rebalancing, by the at least one computer processor, on a half year basis; or

rebalancing, by the at least one computer processor, on a multiple year basis.

52. The computer-implemented method of claim 48, further comprising:

recalculating, by the at least one computer processor, said index on a periodic basis.

53. The computer-implemented method of claim 52, wherein said recalculating said index on said periodic basis comprises at least one of:

recalculating, by the at least one computer processor, on a yearly basis;

recalculating, by the at least one computer processor, on a quarterly basis;

recalculating, by the at least one computer processor, on a half year basis; or

recalculating, by the at least one computer processor, on a multiple year basis.

54. The computer-implemented method of claim 48, further comprising:

adjusting, by the at least one computer processor, said index based on changes over time.

55. The computer-implemented method of claim 54, wherein said adjusting said index based on said changes comprises at least one of:

adjusting, by the at least one computer processor, said index upon a change in financial market status of an index constituent;

adjusting, by the at least one computer processor, said index upon an index constituent going bankrupt;

adjusting, by the at least one computer processor, said index upon an index constituent stock split;

adjusting, by the at least one computer processor, said index upon an index constituent modifying at least one class of stock;

adjusting, by the at least one computer processor, said index upon a price shift of an index constituent; or adjusting, by the at least one computer processor, said index upon a delisting of an index constituent.

56. The computer-implemented method of claim 48, further comprising:

adjusting, by the at least one computer processor, said index based on missing data.

57. The computer-implemented method of claim 56, wherein said adjusting said index based on missing data comprises:

adjusting, by the at least one computer processor, said index if a plurality of metrics are being used, and for a given entity or financial object one of said plurality of metrics is missing.

58. The computer-implemented method of claim 57, wherein said adjusting said index based on missing data comprises:

averaging said remaining plurality of metrics, leaving out said missing metric.

59. The computer-implemented method of claim 1, further comprising:

receiving a plurality of historical data of a plurality of financial metrics of a plurality of financial objects, said plurality of financial objects each relating to an entity; and

wherein said weighting comprises:

weighting, by the at least one computer processor, a plurality of index constituent financial objects, each of said plurality of index constituent financial objects associated with an entity, and

wherein said weighting comprises:

determining, by the at least one computer processor, a proportional fundamental index weight for each of said index constituent financial objects based on at least one objective measure of scale associated with said entities or said financial objects;

wherein said at least one objective measure of scale comprises at least one of:

at least one financial metric associated with at least one of said entities or said financial objects;

at least one demographic measure of at least one of said entities or said financial objects; or

at least one metric from information disclosures of a publicly traded entity; and

wherein said at least one objective measure of scale comprises a metric other than the market capitalization of said entities or the price of said financial objects; and

weighting, by the at least one computer processor, by a mathematical combination of a plurality of data for said at least one objective measure of scale of a given financial object of a given entity, said plurality of data for said at least one objective measure of scale of said given financial object of said given entity, comprising at least one of:

a plurality of time periods;

a plurality of years;

a plurality of quarters;

a plurality of months; or

a plurality of accounting periods; and

wherein said mathematical combination of said plurality data for said given financial object of said given entity, comprises at least one of:

calculating, by the at least one computer processor, a mathematical average of said plurality of data for said given financial object of said given entity;

calculating, by the at least one computer processor, a mathematical weighted average of said plurality of financial metric data of said given financial object of said given entity;

calculating, by the at least one computer processor, a statistical mean of said plurality of financial metric data of said given financial object of said given entity;

calculating, by the at least one computer processor, a statistical median of said plurality of financial metric data of said given financial object of said given entity; or

calculating, by the at least one computer processor, a midpoint of said plurality of financial metric data of said given financial object of said given entity.

60. The computer-implemented method of claim 59, wherein said calculating said mathematical combination comprises reducing risk.

61. The computer-implemented method of claim 59, wherein said objective measure of scale comprises at least one of:

book value;

sales;

revenue;

profit;

earnings;

cash flow;

cash earnings; or

a fundamental accounting variable.

62. The computer-implemented method of claim 1, further comprising:

receiving fundamental accounting data about a plurality of entities, over a plurality of time periods, each of said entities associated with one of said financial objects;

receiving a plurality of index constituents;

wherein said weighting comprises:

weighting said plurality of said index constituents according to at least one financial metric of said fundamental accounting data, each of said at least one financial metrics having data for said plurality of time periods from said fundamental accounting data to obtain relative weightings, and wherein said weighting comprises:

averaging said fundamental accounting data over said plurality of said time periods for said each of said at least one financial metrics; and

weighting said index constituents using at least one economic-centric metric about said entities rather than a market-centric metric to obtain an economic-centric index,

wherein said at least one economic-centric metric comprises a metric comprising at least one of:

at least one economic size metric;

at least one economic impact metric; or

at least one economic footprint metric;

providing said economic-centric index to a third party, wherein said third party manages, by at least one computer processor, a portfolio of financial objects based on said index of financial objects, wherein said third party manages, comprising at least one of:

adjusts, by the at least one computer processor, the financial objects that comprise said portfolio based on changes to said one or more financial metrics used to weight the plurality of financial objects used to construct the economy-centric index of financial objects;

adjusts, by the at least one computer processor, the relative weightings of the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to weight the plurality of financial objects used to construct the economy-centric index of financial objects;

rebalances, by the at least one computer processor, the relative weightings of the financial objects that comprise said portfolio when the weighting of one or more of said financial objects at least one of: exceeds a threshold value, or deviates from a target weight; or rebalances, by the at least one computer processor, the relative weightings of the financial objects that comprise said portfolio to minimize turnover of said financial objects.

63. The computer-implemented method according to claim 62, wherein said weighting comprises:

weighting based on a plurality of said economic-centric metrics.

64. The computer-implemented method according to claim 63, wherein said weighting based on said plurality of economic-centric metrics comprises:

weighting based on at least one of:

book value;

book value of operating assets;

sales;

revenue;

profit;

earnings;

cash flow;

cash earnings;

cash flow from operations; or

a fundamental accounting variable.

65. The computer-implemented method according to claim 63, wherein said weighting based on said plurality of economic-centric metrics comprises:

weighting based on metrics comprising:

book value;

sales; and

cash flow.

66. The computer-implemented method according to claim 62, wherein said third party further manages comprising:

rebalances on a periodic time basis; or

rebalances on a periodic accounting period basis.

67. The computer-implemented method according to claim 1, further comprising:

receiving fundamental accounting data about a plurality of entities, over a plurality of accounting periods, each of said entities associated with one of said financial objects;

receiving a plurality of index constituents;

wherein said weighting comprises:

weighting said plurality of said index constituents according to one or more financial metrics of said fundamental accounting data, each of said one or more financial metrics having data for said plurality of accounting periods from said fundamental accounting data to obtain relative weightings, and wherein said weighting comprises:

averaging said fundamental accounting data over said plurality of said accounting periods for said each of said one or more financial metrics; and

weighting said index constituents using at least one economic-centric metric about said entities rather than market-centric metric to obtain an economic-centric index,

wherein said at least one economic-centric metric comprises a metric comprising at least one of:

at least one economic size metric;

at least one economic impact metric; or

at least one economic footprint metric;

providing said economic-centric index to a third party, wherein said third party manages, by at least one computer processor, a portfolio of financial objects based on said index of financial objects, wherein said third party manages, comprising at least one of:

adjusts, by the at least one computer processor, the financial objects that comprise said portfolio based on changes to said one or more financial metrics used to weight the plurality of financial objects used to construct the economy-centric index of financial objects;

adjusts, by the at least one computer processor, the relative weightings of the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to weight the plurality of financial objects used to construct the economy-centric index of financial objects;

rebalances, by the at least one computer processor, the relative weightings of the financial objects that comprise said portfolio when the weighting of one or more of said financial objects at least one of: exceeds a threshold value, or deviates from a target weight; or

rebalances, by the at least one computer processor, the relative weightings of the financial objects that comprise said portfolio to minimize turnover of said financial objects.

68. The computer-implemented method according to claim 67, wherein said weighting comprises:

weighting based on a plurality of said economic-centric metrics.

69. The computer-implemented method according to claim 68, wherein said weighting based on said plurality of economic-centric metrics comprises:

weighting based on at least one of:

book value;

book value of operating assets;

sales;

revenue;

profit;

earnings;

cash flow;

cash earnings;

cash flow from operations; or

a fundamental accounting variable.

70. The computer-implemented method according to claim 68, wherein said weighting based on said plurality of economic-centric metrics comprises:

weighting based on metrics comprising:

book value;

sales; and

cash flow.

71. The computer-implemented method according to claim 67, wherein said third party further manages comprising:

rebalances on a periodic time basis; or

rebalances on a periodic accounting period basis.

72. The computer-implemented method according to claim 1, further comprising:

receiving, by the at least one computer processor, data about a plurality of entities and a plurality of corresponding financial objects associated with the plurality of entities from at least one database storing and permitting retrieval of such data;

receiving, by the at least one computer processor, data indicative of a set of financial objects comprising a plurality of constituent financial objects;

wherein said weighting comprises:

weighting, by the at least one computer processor, said constituent financial objects, wherein said weighting comprises:

determining, by the at least one computer processor, a proportional fundamental weight for each said constituent financial object based on at least one objective measure of scale associated with said entities or said financial objects;

wherein said at least one objective measure of scale comprises at least one financial metric associated with one of said entities or said financial objects other than the market capitalization of said entities or the price of said financial objects; wherein said at least one financial metric comprises at least one of: book value; sales; cash flow; or any dividends; and

managing, by the at least one computer processor, a portfolio of financial objects based on said set of financial objects, wherein said managing comprises at least one of:

adjusting, by the at least one computer processor, the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to weight the plurality of financial objects used to construct the set of financial objects;

adjusting, by the at least one computer processor, the proportional fundamental weight of the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to weight the plurality of financial objects used to construct the set of financial objects;

rebalancing, by the at least one computer processor, the proportional fundamental weight of the financial objects that comprise said portfolio when the weighting of one or more of said financial objects at least one of: exceeds a threshold value, or deviates from a target weight; or

rebalancing the proportional fundamental weight of the financial objects that comprise said portfolio to minimize turnover of said financial objects.

73. A nontransitory computer processor readable storage medium having computer processor readable program code such that when executed by a computer processor in a data processing apparatus, performs a computer processor-implemented method for constructing data indicative of a financial object index, the method comprising:

constructing, by at least one computer processor, data indicative of the financial object index comprising:

selecting, by the at least one computer, data indicative of constituent financial objects of said financial object index based upon at least one accounting data rather than price of said financial objects, said at least one accounting data regarding at least one entity relating to each of said financial objects, the at least one entity comprising at least one of a region, a country, a company, or a sovereign associated with said financial objects; and

weighting, by the at least one computer processor, data indicative of said constituent financial objects based upon at least one accounting data rather than price of said financial objects, said at least one accounting data regarding the at least one entity, to obtain data indicative of constituent weightings for each of said constituent financial objects, and

storing, by the at least one computer processor, said data indicative of said constituent financial objects and said constituent weightings on at least one computer storage device,

wherein each of said financial objects comprises at least one instrument,

wherein each of said financial objects comprises at least one of:

a bond;

a fixed income debt instrument;

a debt instrument;

an emerging market debt instrument;

a high yield debt instrument;

a corporate debt instrument;

an investment grade debt instrument;

a debenture;

a bank loan;

a convertible bond;

a senior debt;

a subordinated debt;

a term loan;

a government debt instrument;

a government bond;

a corporate bond;

a high yield bond;

an emerging market bond;

a municipal bond debt instrument;

a treasury bond debt instrument;

a treasury bill debt instrument;

a mortgage based debt instrument;

a securitized debt instrument;

a security;

a stock;

a commodity;

a futures contract;

a mutual fund;

a hedge fund;

a fund of funds;

an exchange traded fund (ETF);

a derivative;

a negative weighting on any asset;

an asset account;

a separate account;

a pooled trust; or

a limited partnership; and

wherein said at least one accounting data comprises at least one of:

at least one financial metric of the at least one entity associated with each of said constituent financial objects; or

at least one metric, level, rate, or expenditure amount from information disclosures of the at least one entity.

74. The nontransitory computer processor readable storage medium of claim 73, wherein the method further comprises:

wherein said selecting based on said at least one accounting data comprises:

selecting based on at least one demographic data regarding the at least one entity.

75. The nontransitory computer processor readable storage medium of claim 74, wherein the method further comprises:

wherein said at least one demographic data comprises at least one of:

a demographic measure,

a population level,

an area,

a geographic area,

an economic factor,

a gross domestic product (GDP),

GDP growth,

a natural resource characteristic,

an energy metric,

a petroleum characteristic,

a resource consumption metric,

a petroleum consumption amount,

a liquid natural gas (LNG) characteristic,

a liquefied petroleum gas (LPG) characteristic,

an expenditures characteristic,

gross national income (GNI),

a debt characteristic,

a rate of inflation,

a rate of unemployment,

a reserves level,

a population characteristic,

a corruption characteristic,

a democracy characteristic,

a social metric,

a political metric,

a per capita ratio of any of the foregoing;

a derivative of any foregoing; or

a ratio of any two or more of the foregoing.

76. The nontransitory computer processor readable storage medium of claim 73, wherein the method further comprises:

wherein said weighting based on said at least one accounting data comprises:

weighting based on at least one demographic data regarding the at least one entity.

77. The nontransitory computer processor readable storage medium of claim 76, wherein the method further comprises:

wherein said at least one demographic data comprises at least one of:

a demographic measure,

a population level,

an area,

a geographic area,

an economic factor,

a gross domestic product (GDP),

GDP growth,

a natural resource characteristic,

an energy metric,

a petroleum characteristic,

a resource consumption metric,

a petroleum consumption amount,

a liquid natural gas (LNG) characteristic,

a liquefied petroleum gas (LPG) characteristic,

an expenditures characteristic,

gross national income (GNI),

a debt characteristic,

a rate of inflation,

a rate of unemployment,

a reserves level,

a population characteristic,

a corruption characteristic,

a democracy characteristic,

a social metric,

a political metric,

a per capita ratio of any of the foregoing;

a derivative of any foregoing; or

a ratio of any two or more of the foregoing.

78. A system for constructing data indicative of an investable index of financial objects, comprising:

an analysis host computer processing apparatus coupled to an entity database, wherein the entity database is operative to store aggregated accounting based data about a plurality of entities obtained from an external data source, said analysis host computer processing apparatus comprising:

a data retrieval and storage subsystem operative to at least one of access, or retrieve said aggregated accounting based data from the entity database and to at least one of access from, provide, or store said aggregated accounting based data to the entity database;

an index generation subsystem comprising:

a selection subsystem operative to select a group of said entities based on at least one accounting data;

a weighting function generation subsystem operative to generate a weighting function based on at least one accounting data;

an index creation subsystem operative to create an investable accounting data based index based on said group of selected entities and said weighting function; and

a storing subsystem operative to at least one of provide, or store said investable accounting data based index,

wherein said investable index generation subsystem is operative to construct data indicative of said investable accounting data based index, wherein said investable accounting data based index comprises a financial object index, said index generation subsystem comprising at least one computer processor, and at least one memory coupled to said at least one computer processor, said at least one computer processor operative to construct data indicative of said financial object index;

wherein said selection subsystem comprises at least one computer processor, and at least one memory coupled to said at least one computer processor, said at least one computer processor operative to select data indicative of constituent financial objects of said financial object index based upon at least one accounting data rather than price of said financial objects, said at least one accounting data regarding at least one entity relating to each of said financial objects, the at least one entity comprising at least one of a region, a country, a company, or a sovereign associated with said each of said financial objects; and

wherein said weighting function generation subsystem comprises at least one computer processor, and at least one memory coupled to said at least one computer processor, said at least one computer processor operative to weight data indicative of said constituent financial objects based upon at least one accounting data rather than price of said financial objects, said at least one accounting data regarding the at least one entity, to obtain data indicative of constituent weightings for each of said constituent financial objects, and

wherein said storing subsystem comprises at least one computer processor, and at least one memory coupled to said at least one computer processor, said at least one computer processor operative to at least one of access from, provide, or store said data indicative of said constituent financial objects and said constituent weightings on at least one computer storage device,

wherein each of said financial objects comprises at least one instrument,

wherein each of said financial objects comprises at least one of:

a bond;

a fixed income debt instrument;

a debt instrument;

an emerging market debt instrument;

a high yield debt instrument;

a corporate debt instrument;

an investment grade debt instrument;

a debenture;

a bank loan;

a convertible bond;

a senior debt;

a subordinated debt;

a term loan;

a government debt instrument;

a government bond;

a corporate bond;

a high yield bond;

an emerging market bond;

a municipal bond debt instrument;

a treasury bond debt instrument;

a treasury bill debt instrument;

a mortgage based debt instrument;

a securitized debt instrument;

a security;

a stock;

a commodity;

a futures contract;

a mutual fund;

a hedge fund;

a fund of funds;

an exchange traded fund (ETF);

a derivative;

a negative weighting on any asset;

an asset account;

a separate account;

a pooled trust; or

a limited partnership; and

wherein said at least one accounting data comprises at least one of:

at least one financial metric of the at least one entity associated with each of said constituent financial objects; or

at least one metric, level, rate or expenditure amount from information disclosures of the at least one entity.

79. The system according to claim 78, wherein said analysis host computer processing apparatus further comprises:

a normalization calculation sub-system operative to normalize said data for said at least one accounting data across said plurality of entities.

80. The system according to claim 78, wherein said at least one accounting data used by said selection subsystem at least one of differs from, or is the same as, said at least one accounting data used by said weighting function generating sub-system.

81. The system according to claim 78, wherein said at least one accounting data comprises at least one of:

revenue;

profitability;

sales;

total sales;

foreign sales,

domestic sales;

net sales;

gross sales;

profit margin;

operating margin;

retained earnings;

earnings per share;

book value;

book value adjusted for inflation;

book value adjusted for replacement cost;

book value adjusted for liquidation value;

dividends;

assets;

tangible assets;

intangible assets;

fixed assets;

property;

plant;

equipment;

goodwill;

replacement value of assets;

liquidation value of assets;

liabilities;

long term liabilities;

short term liabilities;

net worth;

research and development expense;

accounts receivable;

earnings before interest and tax (EBIT);

earnings before interest, taxes, dividends, and amortization (EBITDA);

accounts payable;

cost of goods sold (CGS);

debt ratio;

budget;

capital budget;

cash budget;

direct labor budget;

factory overhead budget;

operating budget;

sales budget;

inventory system;

type of stock offered;

liquidity;

book income;

tax income;

capitalization of earnings;

capitalization of goodwill;

capitalization of interest;

capitalization of revenue;

capital spending;

cash;

compensation;

employee turnover;

overhead costs;

credit rating;

growth rate;

tax rate;

liquidation value of entity;

capitalization of cash;

capitalization of earnings;

capitalization of revenue;

cash flow; or

future value of expected cash flow.

82. The system according to claim 78, wherein at least one accounting data comprises a ratio of any combination of two or more accounting data.

83. The system according to claim 82, wherein said ratio of any combination of said accounting data comprise at least one of:

current ratio;

debt ratio;

overhead expense as a percent of sales; or

debt service burden ratio.

84. The system according to claim 78, wherein said at least one accounting data further comprises at least one demographic measure.

85. The system according to claim 78, wherein said at least one demographic measure comprises at least one of:

a financial metric;

a nonfinancial metric;

a market metric;

a nonmarket related metric;

a measure relating to employees;

floor space;

office space;

location;

other demographics of an asset;

a demographic measure,

a population level,

an area,

a geographic area,

an economic factor,

a gross domestic product (GDP),

GDP growth,

a natural resource characteristic,

an energy metric,

a petroleum characteristic,

a resource consumption metric,

a petroleum consumption amount,

a liquid natural gas (LNG) characteristic,

a liquefied petroleum gas (LPG) characteristic,

an expenditures characteristic,

gross national income (GNI),

a debt characteristic,

a rate of inflation,

a rate of unemployment,

a reserves level,

a population characteristic,

a corruption characteristic,

a democracy characteristic,

a social metric,

a political metric,

a per capita ratio of any of the foregoing;

a derivative of any foregoing; or

a ratio of any two or more of the foregoing.

86. The system of claim 78, further comprising:

a trading host computer processing apparatus, coupled to said analysis host computer processing apparatus, and operative to construct a portfolio of assets comprising one or more trading assets based on said investable accounting data based index, said trading host computer processing apparatus comprising at least one of:

an index retrieval subsystem operative to retrieve said investable accounting data based index;

a trading accounts management subsystem operative to receive one or more data indicative of investment amounts from one or more investors; or

a purchasing subsystem operative to permit purchasing of one or more of said trading assets using said investment amounts based on said investable accounting data based index.

87. The system of claim 86, further comprising:

a trading accounts database coupled to said trading accounts management subsystem, said trading accounts database operative to at least one of provide access to, or store said one or more data indicative of said investment amounts.

88. The system of claim 86, further comprising:

an exchange host computer processing apparatus coupled to said purchasing subsystem, said exchange host computer processing apparatus operative to perform one or more functions of said purchasing subsystem.

89. The system of claim 86, wherein said asset type comprises at least one of:

a fund;

a mutual fund;

a fund of funds;

an asset account;

an exchange traded fund (ETF);

a separate account, a pooled trust; or

a limited partnership.

90. The system according to claim 86, further comprising: a subsystem operative to rebalance a pre-selected group of trading assets based on said accounting data based index.

91. The system according to claim 90, wherein said rebalancing is performed on at least one of: a periodic basis, or an aperiodic basis.

92. The system according to claim 90, wherein said rebalancing is based on at least one of the group of assets crossing a predetermined threshold, or an occurrence of an event.

93. The system according to claim 86, further comprising:

applying one or more rules associated with said accounting data based index.

94. The system according to claim 78, wherein the system may be used for at least one of:

investment management, or

investment portfolio benchmarking.

95. The system of claim 78, wherein the selection sub-system is operative to perform enhanced index investing, comprising: computing said portfolio of assets in a fashion wherein at least one of: holdings; performance; or characteristics, are substantially similar to an external index.

96. A computer-implemented method of constructing data indicative of an investable index of financial objects by at least one computer processor, the method comprising:

receiving by the at least one computer processor data about a plurality of entities and a plurality of corresponding financial objects associated with the plurality of entities from one or more databases storing and permitting retrieval of such data;

selecting by the at least one computer processor a plurality of said financial objects to construct an investable index of financial objects,

wherein said selecting comprises:

selecting a plurality of financial objects to construct an investable index of financial objects based on at least one objective measure of scale associated with said entities or said financial objects,

each of said entities comprising at least one of a region, a country, a company, or a sovereign associated with said each of said financial objects,

wherein said at least one objective measure of scale comprises at least one financial metric associated with one of said entities or said financial objects other than the market capitalization of said entities or the price of said financial objects;

weighting by the at least one computer processor the plurality of financial objects selected to construct the investable index of financial objects, wherein said weighting comprises:

determining a proportional weight for each financial object based on at least one objective measure of scale associated with said entities or said financial objects;

wherein said at least one objective measure of scale comprises at least one financial metric associated with one of said entities or said financial objects other than the market capitalization of said entities or the price of said financial objects;

storing or providing by the at least one computer processor the plurality of financial objects selected to construct the index and the proportional weight for each of said financial objects;

wherein each of said financial objects comprises at least one of:

a bond;

a fixed income debt instrument;

a debt instrument;

an emerging market debt instrument;

a high yield debt instrument;

a corporate debt instrument;

an investment grade debt instrument;

a debenture;

a bank loan;

a convertible bond;

a senior debt;

a subordinated debt;

a term loan;

a government debt instrument;

a government bond;

a corporate bond;

a high yield bond;

an emerging market bond;

a municipal bond debt instrument;

a treasury bond debt instrument;

a treasury bill debt instrument;

a mortgage based debt instrument;

a securitized debt instrument;

a security;

a stock;

a commodity;

a futures contract;

a mutual fund;

a hedge fund;

a fund of funds;

an exchange traded fund (ETF);

a derivative;

a negative weighting on any asset;

an asset account;

a separate account;

a pooled trust; or

a limited partnership; and

managing by the at least one computer processor a portfolio of financial objects based on said index of financial objects, wherein said managing comprises at least one of:

adjusting the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to select the plurality of financial objects used to construct the investable index of financial objects;

adjusting the relative weightings of the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to weight the plurality of financial objects used to construct the investable index of financial objects;

rebalancing the relative weightings of the financial objects that comprise said portfolio when the weighting of one or more of said financial objects at least one of: exceeds a threshold value, or deviates from a target weight; or

rebalancing the relative weightings of the financial objects that comprise said portfolio to minimize turnover of said financial objects.

97. The method of claim 96, wherein:

said selecting based on said at least one objective measure of scale comprises selecting wherein said at least one financial metric comprises any dividends.

98. The method of claim 97, wherein:

said selecting wherein said financial metric comprises any dividends comprises selecting based on a calculation based on said dividends exceeding a minimum value.

99. The method of claim 98, wherein:

said selecting based on a calculation based on said dividends comprises selecting based on a calculation based on said dividends comprising a sum of total dividends paid over a period of time.

100. The method of claim 96, wherein:

said selecting based on said at least one objective measure of scale comprises selecting wherein said at least one financial metric comprises earnings.

101. The method of claim 100, wherein:

said selecting based on said at least one financial metric comprising earnings comprises selecting based on said earnings being positive over a period of time.

102. The method of claim 96, wherein:

said selecting comprises sorting said plurality of financial objects into one or more sets of financial objects based on said at least one objective measure of scale.

103. The method of claim 96, wherein:

said selecting comprises sorting said plurality of financial objects into one or more sets of financial objects based on a derivative of said at least one objective measure of scale.

104. The method of claim 96, wherein:

said weighting based on said at least one objective measure of scale comprises weighting based on any dividends.

105. The method of claim 96, wherein:

said weighting based on said at least one objective measure of scale comprises weighting based on earnings.

106. The method of claim 96, wherein:

said selecting comprises selecting a plurality of said financial objects to construct an index of financial objects from a universe of said entities or said financial objects.

107. The method of claim 106, wherein:

said universe comprises at least one of: a sector, a market, a market sector, an industry sector, a geographic sector, an international sector, a sub-industry sector, a government issue sector or a tax-exempt sector.

108. A system for constructing data indicative of an investable index of financial objects by at least one computer processor comprising:

the at least one computer processor being coupled to at least one data network;

wherein the at least one data network comprises a connection or a coupling to at least one database adapted to store and adapted to permit at least one of access or retrieval of data;

wherein said data comprises data about a plurality of entities and a plurality of corresponding financial objects associated with the plurality of entities;

the at least one computer processor being configured to:

receive from the at least one data network data about a plurality of entities and a plurality of corresponding financial objects associated with the plurality of entities from said one or more databases;

select a plurality of said financial objects to construct an index of financial objects based on said data received from the at least one data network, wherein said select comprises wherein the at least one computer processor being configured to:

select a plurality of financial objects to construct an index of financial objects based on at least one objective measure of scale associated with said entities or said financial objects,

said entity comprising at least one of a region, a country, a company, or a sovereign associated with said financial object,

 wherein said at least one objective measure of scale comprises at least one financial metric associated with one of said entities or said financial objects other than the market capitalization of said entities or the price of said financial objects;

weight by the plurality of financial objects selected to construct the index of financial objects, wherein said weight comprises wherein the at least one computer processor being configured to:

determine a proportional weight for each financial object based on at least one objective measure of scale associated with said entities or said financial objects;

 wherein said at least one objective measure of scale comprises at least one financial metric associated with one of said entities or said financial objects other than the market capitalization of said entities or the price of said financial objects;

store or provide wherein the at least one computer processor being configured to store or provide the plurality of financial objects selected to construct the index and the proportional weight for each of said financial objects;

wherein each of said financial objects comprises at least one of:

a bond;

a fixed income debt instrument;

a debt instrument;

an emerging market debt instrument;

a high yield debt instrument;

a corporate debt instrument;

an investment grade debt instrument;

a debenture;

a bank loan;

a convertible bond;

a senior debt;

a subordinated debt;

a term loan;

a government debt instrument;

a government bond;

a corporate bond;

a high yield bond;

an emerging market bond;

a municipal bond debt instrument;

a treasury bond debt instrument;

a treasury bill debt instrument;

a mortgage based debt instrument;

a securitized debt instrument;

a security;

a stock;

a commodity;

a futures contract;

a mutual fund;

a hedge fund;

a fund of funds;

an exchange traded fund (ETF);

a derivative;

a negative weighting on any asset;

an asset account;

a separate account;

a pooled trust; or

a limited partnership; and

manage a portfolio of financial objects based on said index of financial objects,

wherein said manage comprises wherein the at least one computer processor being configured to at least one of:

adjust the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to select the plurality of financial objects used to construct the index of financial objects;

adjust the relative weightings of the financial objects that comprise said portfolio based on changes to the at least one objective measure of scale used to weight the plurality of financial objects used to construct the index of financial objects;

rebalance the relative weightings of the financial objects that comprise said portfolio when the weight of one or more of said financial objects at least one of:

exceeds a threshold value; or deviates from a target weight; or rebalance the relative weightings of the financial objects that comprise said portfolio to minimize turnover of said financial objects.

109. The system of claim 108, wherein:

said select based on said at least one objective measure of scale comprises wherein the at least one computer processor being configured to select wherein said at least one financial metric comprises any dividends.

110. The system of claim 109, wherein:

said select wherein said financial metric comprises any dividends and comprises wherein the at least one computer processor being configured to select based on a calculation based on said dividends exceeding a minimum value.

111. The system of claim 110, wherein:

said select based on a calculation based on said dividends comprises wherein the at least one computer processor being configured to select based on a calculation based on said dividends comprising a sum of total dividends paid over a period of time.

112. The system of claim 108, wherein:

said select based on said at least one objective measure of scale comprises wherein the at least one computer processor being configured to select wherein said at least one financial metric comprises earnings.

113. The system of claim 112, wherein:

said select based on said at least one financial metric comprising earnings and comprises wherein the at least one computer processor being configured to select based on said earnings being positive over a period of time.

114. The system of claim 108, wherein: said select comprises wherein the at least one computer processor being configured to sort said plurality of financial objects into one or more sets of financial objects based on said at least one objective measure of scale.

115. The system of claim 108, wherein:

said select comprises wherein the at least one computer processor being configured to sort said plurality of financial objects into one or more sets of financial objects based on a derivative of said at least one objective measure of scale.

116. The system of claim 108, wherein:

said weight based on said at least one objective measure of scale comprises wherein the at least one computer processor being configured to weight based on any dividends.

117. The system of claim 108, wherein:

said weight based on said at least one objective measure of scale comprises wherein the at least one computer processor being configured to weight based on earnings.

118. The system of claim 108, wherein:

said select comprises wherein the at least one computer processor being configured to select a plurality of said financial objects to construct an index of financial objects from a universe of said entities or said financial objects.

119. The system of claim 118, wherein:

said universe comprises at least one of: a sector, a market, a market sector, an industry sector, a geographic sector, an international sector, a sub-industry sector, a government issue sector or a tax-exempt sector.