Matched filter approach to portfolio optimization

Abstract

Given a fixed amount of capital, how to invest it optimally by distributing it among a set of stocks and securities so as to maximize the return while minimizing the overall risk is addressed here. Given that one has full freedom in selecting the type of stocks, a new strategy is outlined here by maximizing the ratio of the gain to risk—rather than minimizing the risk alone—to determine the fraction of capital that must go to each stock. An optimum gain versus variance plot can be used to determine the type of stocks to be selected in addition to their relative quantity for maximum yield over the duration of interest. By modifying the definition of risk to include a function of the covariance matrix of secondary stocks that are sympathetic to the primary stocks of interest, an alternate investment strategy is also developed here. If short selling of stocks and securities is not allowed in a portfolio, then stock selection becomes important so as to maintain the desired fractions to be positive. In this context, a new iterative method that incrementally increases the diagonal loading of the covariance matrix of the primary returns so as to achieve positive weight factors is also developed.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flow chart of a method in accordance with an embodiment of the present invention;

FIG. 2 shows a diagram of a whitening filter followed by a matched filter;

FIG. 3 is a chart showing gain versus square root of portfolio variance (standard deviation); and

FIG. 4 shows a flow chart of another method in accordance with another embodiment of the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

One embodiment of the present invention makes use of matched filtering concepts for the purpose of picking stocks. These matched filtering concepts were previously used in the field of electrical engineering but were not previously used for the purpose of picking the right mix of stocks in a portfolio.

At least one embodiment of the present invention provides a method for picking stocks, which maximizes gain and simultaneously minimizes risk. This is achieved by maximizing the ratio of gain to risk. The right mix of stocks are selected so as to maximize the gain G while simultaneously minimizing the overall risk σ_P². In at least one embodiment the following ratio is maximized:

$\begin{array}{cc}\frac{G}{{\sigma }_P^2}.& \left(25\right)\end{array}$

Equation (25) represents the gain over the portfolio risk. However, in one embodiment of the present invention, instead of maximizing (25), the following ratio is maximized:

$\begin{array}{cc}\frac{G^2}{{\sigma }_P^2}=\frac{{{\underset{\_}{a}}^T\underset{\_}{\mu }}^2}{{\underset{\_}{a}}^TR\underset{\_}{a}}& \left(26\right)\end{array}$

subject to the normalization constraint α^Te=1.

Strategy-1 (in Accordance with an Embodiment of the Present Invention):

Clearly, equation (26) represents a more aggressive strategy in term of maximizing gain, but more interestingly, the ratio in equation (26) is the same as the familiar SNR (Signal to Noise Ratio) maximization strategy used in classical receiver design in Communication theory, in Electrical Engineering, where a signal corrupted by interference and noise is presented to a receiver to minimize the effect of output interference plus noise while maximizing the output signal component at the decision instant as referred to in “Signals Analysis”, A. Papoulis, McGraw-Hill Companies, New York, USA, 1977, and also “Digital Communications”, Fourth edition, J. Proakis, McGraw-Hill Companies, New York, USA, 2001. The solution to the SNR maximization problem leads to well known matched filter (MF) solution as referred to in “Signals Analysis”, A. Papoulis.

From (26), with

$\mathrm{SNR}=\frac{G^2}{{\sigma }_P^2},$

we get

$\begin{array}{cc}{\mathrm{SNR}}_{\max }={\left(\frac{G^2}{{\sigma }_P^2}\right)}_{\max }=\underset{\underset{\_}{a}}{\max }\frac{{{\underset{\_}{a}}^T\underset{\_}{\mu }}^2}{{\underset{\_}{a}}^TR\underset{\_}{a}}\le {\underset{\_}{\mu }}^TR^{-1}\underset{\_}{\mu }& \left(27\right)\end{array}$

since by Schwarz's inequality

|α^Tμ|²=|(R^1/2α)^T(R^−1/2μ)|²≦(α^TRα)(μ^TR⁻¹μ),   (28)

With equality if

α _opt=kR⁻¹μ.   (29)

This gives

$\begin{array}{cc}{\mathrm{SNR}}_{\max }={\left(\frac{G^2}{{\sigma }_P^2}\right)}_{\max }={\underset{\_}{\mu }}^TR^{-1}\underset{\_}{\mu }.& \left(30\right)\end{array}$

In general the entries of the optimum portfolio mix vector shown in equation (29) can be both positive or negative. Negative entries indicate that the corresponding stock is to be shorted. If short sale strategies are prohibited, for example, as in the case of most of mutual funds, then one needs to maintain α>0, and in that case one can perform a constrained optimization strategy of maximizing equation (26) subject to the non-negativity constraint of α. This leads to a suboptimum solution with all positive or non-negative entries for the vector α that requires no short selling. This strategy can be applied to any given set of stocks and securities that the investor has a-priori selected. In that case the capital will be partitioned according to the entries of the suboptimum vector so obtained and invested in the corresponding stocks.

An alternate strategy is to keep the pool of the desired stocks and securities to be selected as potentially open, and select them from a larger pool of stocks and securities in such a way that the inverse of their covariance matrix R⁻¹ turns out to be a positive matrix. If this condition turns out to be too restrictive or severe especially for a portfolio containing a large number of stocks, one can also settle for the less restrictive new condition

R⁻¹μ>0   (31)

by

(i) the judicious selection of stocks that go into the portfolio and by

(ii) the choice of μ vector in (29) that represent the expected average return.

Observe that

${\underset{\_}{a}}^T\underset{\_}{e}=\sum _ia_i=1$

can be easily maintained with the constant k in equation (29) chosen to be

$\begin{array}{cc}k=\frac{1}{{\underset{\_}{e}}^TR^{-1}\underset{\_}{\mu }}.& \left(32\right)\end{array}$

This gives the desired portfolio mixing vector to be

$\begin{array}{cc}{\underset{\_}{a}}_{\mathrm{opt}}=\frac{R^{-1}\underset{\_}{\mu }}{{\underset{\_}{e}}^TR^{-1}\underset{\_}{\mu }}& \left(33\right)\end{array}$

that maximizes the gain and minimizes volatility. In this case,

$\begin{array}{cc}{G}_{\mathrm{opt}}={\underset{\_}{a}}^T\underset{\_}{\mu }=\frac{{\underset{\_}{\mu }}^TR^{-1}\underset{\_}{\mu }}{{\underset{\_}{e}}^TR^{-1}\underset{\_}{\mu }}>0& \left(34\right)\end{array}$

and

$\begin{array}{cc}{\left({\sigma }_P^2\right)}_{\min }=\frac{{\underset{\_}{\mu }}^TR^{-1}\underset{\_}{\mu }}{{\left({\underset{\_}{e}}^TR^{-1}\underset{\_}{\mu }\right)}^2}>0.& \left(35\right)\end{array}$

FIG. 1 shows a flow chart 10 of a method in accordance with an embodiment of the present invention. At step 12, a stock returns vector, such as r(n) calculated by equation (3) and (7), is determined for m stocks. Next a means of returns vector, such as μ calculated by equation (4) and (8), is determined at step 14. At step 16, an inverse matrix of m stocks, such as R⁻¹ is determined. At step 18 weighting factors, such as α are determined based on the inverse co-variance matrix times the means for returns vectors divided by the sum of the vector so obtained, such as by the equation (29) or (33).

Equation (33) can be given the whitening followed by matched filtering interpretation as well, as will be shown with reference to FIG. 2. FIG. 2 shows a diagram 100 of a whitening filter 102 followed by a matching filter 104. This technique was previously used in classical receiver design in Communication theory in Electrical Engineering, but not for the purpose of picking the right mix of stocks in a portfolio. In this example, the input to the whitening filter 102 is stock returns vector r(n). The whitening filter 102 reduces the noise or volatility in the stock returns vector r(n) and produces the filter output shown below:

$\begin{array}{cc}\underset{\_}{x}\left(n\right)=\left[\begin{array}{c}{x}_1\left(n\right)\\ x_2\left(n\right)\\ ⋮\\ x_m\left(n\right)\end{array}\right]=R^{-1/2}\underset{\_}{r}\left(n\right)& \left(36\right)\end{array}$

The filter output above is uncorrelated and has unit variance since its covariance matrix equals

$\begin{array}{cc}\begin{array}{c}{R}_x=E\left\{\left(\underset{\_}{x}\left(n\right)-E\left\{\underset{\_}{x}\left(n\right)\right\}\right){\left(\underset{\_}{x}\left(n\right)-E\left\{\underset{\_}{x}\left(n\right)\right\}\right)}^T\right\}\\ =R^{-1/2}E\left\{\left(\underset{\_}{r}\left(n\right)-E\left\{\underset{\_}{r}\left(n\right)\right\}\right){\left(\underset{\_}{r}\left(n\right)-E\left\{\underset{\_}{r}\left(n\right)\right\}\right)}^T\right\}R^{-1/2}\\ =R^{-1/2}RR^{-1/2}=I\end{array}& \left(37\right)\end{array}$

and to maximally combine these outputs, the coefficients {b_i} in FIG. 2 must be selected so as to maximize the average portfolio gain

$\begin{array}{cc}G=E\left\{P\right\}=E\left\{\sum _ib_ix_i\left(n\right)\right\}={\underset{\_}{b}}^T{\underset{\_}{\mu }}_x& \left(38\right)\end{array}$

where

μ _x =E{x(n)}=R^−1/2μ.   (39)

From Schwarz's inequality (see (28)), Eq. (38) is maximized if

b=kμ_x=kR^−1/2μ.   (40)

Thus b in (40) is a maximal combiner with respect to μ_x. Hence,

G=b^Tμ_x=kμ^TR^−1/2μ_x=kμ^TR^−1/2R^−1/2μ=α^Tμ  (41)

or

α=kR⁻¹μ(42)

as in equation (29).

Interestingly, Equations (33)-(35) can be used to generate a gain-risk plot by varying over all sustainable μs. Following equation (33), an arbitrary μ is said to be sustainable if R⁻¹μ is a positive vector. Using a sustainable μ, one can compute the optimum gain and σ_P using equations (34)-(35).

Notice that although scaling μ does not affect the variance in equation (35), it does affect the gain in equation (34). Hence to avoid duplication by simple scaling, the first entry μ₁ in a sustainable μ may be normalized to unity. As an example, FIG. 3 shows a diagram 200 of an optimum average portfolio gain G as in (34) versus square root of the risk in (32) (standard deviation) plot using arbitrary sustainable normalized mean vectors for various sets of portfolios containing different numbers of actual stocks. The stocks in each portfolio are selected for illustrative purposes only. Table 1 lists the actual stocks used for FIG. 3. Observe that FIG. 3 shows cases for portfolios where the number of stocks equals m=12, 8 and 6. The results for cases m=12, m=8, and m=6 is shown as A, B, and C, respectively in FIG. 3. Covariance matrices in each case have been calculated using sample data collected for the period of January 2001 to December 2004 with weekly duration representing a time unit.

TABLE 1
Stock symbols used in FIG. 3.
	m = 12	m = 8	m = 6
	‘SLB’	‘TWX’	‘NOC’
	‘BK’	‘COST’	‘BAC’
	‘GD’	‘SBUX’	‘SBUX’
	‘SBUX’	‘MER’	‘AAPL’
	‘TWX’	‘NOC’	‘GE’
	‘Dell’	‘AAPL’	‘GD’
	‘NOC’	‘AFL’
	‘CFC’	‘DST’
	‘BA’
	‘GIS’
	‘EBAY’
	‘MHP’

From FIG. 3, as the number of stocks in a portfolio increases, the risk in terms of overall variance decreases. Interestingly, for the strategy of stock weight picking shown by equations (29) or (33), for a given set of stocks, the risk is more concentrated compared to the spread in gain. Each point in FIG. 3 corresponds to a positive weight vector that is optimum for the corresponding normalized mean vector and the given stocks. The desired gain and risk tolerance of the investor will dictate the actual point of interest that will be selected for investment.

Interestingly, other variations of the stock picking strategy shown by equations (29) and (33) also lead to the same result.

One may use a less aggressive strategy in terms of returns while maintaining low volatility. Then we may maximize:

$\begin{array}{cc}\frac{G}{{\sigma }_P^2}=\frac{{\underset{\_}{a}}^T\underset{\_}{\mu }}{{\underset{\_}{a}}^TR\underset{\_}{a}}& \left(43\right)\end{array}$

instead of equation (26). This leads to

$\begin{array}{cc}\underset{\underset{\_}{a}}{\max }\frac{{\underset{\_}{a}}^TR^{1/2}R^{-1/2}\underset{\_}{\mu }}{{\underset{\_}{a}}^TR\underset{\_}{a}}\le \sqrt{\frac{{\underset{\_}{\mu }}^TR^{-1}\underset{\_}{\mu }}{{\underset{\_}{a}}^TR\underset{\_}{a}}}.& \left(44\right)\end{array}$

Equality is achieved by solution given by equation (33) and in that case

$\begin{array}{cc}{\left(\frac{G}{{\sigma }_P^2}\right)}_{\max }={{\underset{\_}{e}}^TR^{-1}\underset{\_}{\mu }}^2.& \left(45\right)\end{array}$

On the other hand, one can use a much more aggressive strategy such as maximizing

$\begin{array}{cc}\underset{\underset{\_}{a}}{\max }\frac{G^4}{{\sigma }_P^2}=\frac{{{\underset{\_}{a}}^T\underset{\_}{\mu }}^4}{{\underset{\_}{a}}^TR\underset{\_}{a}}& \left(46\right)\end{array}$

subject to equation (1).

Notice that equation (46) is weighted more towards higher gains.

$\begin{array}{cc}\underset{\underset{\_}{a}}{\max }\frac{G^4}{{\sigma }_P^2}\le \frac{{\left({\underset{\_}{a}}^TR\underset{\_}{a}\right)\left({\underset{\_}{\mu }}^TR^{-1}\underset{\_}{\mu }\right)}^2}{{\underset{\_}{a}}^TR\underset{\_}{a}}={\underset{\_}{a}}^TR{\underset{\_}{a}\left({\underset{\_}{\mu }}^TR^{-1}\underset{\_}{\mu }\right)}^2.& \left(47\right)\end{array}$

Once again, equality is obtained in equation (47) by solution given by equation (33).

In summary, for a variety of optimization strategies, the new portfolio mixing vector in equation (33) represents the optimum strategy for building a portfolio. If short selling stocks is allowed, the above strategy can be applied to any set of stocks; if short selling is not permitted, then the selection of stocks and their number that goes into the actual portfolio becomes important and it must be accomplished so as to maintain the desired portfolio mixing vector to be positive, while maintaining a high yield (gain) with minimum fluctuations (risk).

Strategy-2 (in Accordance with Another Embodiment of the Present Invention):

In another embodiment of the present invention, a variation of the maximization of the gain to the risk strategy, to be described below, leads to a somewhat different result in terms of the desired portfolio vector. In the previous method, the risk is defined as the variance of the portfolio under consideration as in equation (9). In a method in accordance with an alternative embodiment of the present invention, this definition is extended as follows:

The actual stocks and securities that go into a portfolio as the primary stocks are identified, and equation (7) represents their returns. In that case the risk defined as in equation (9) represents the variance of the exact combination of the returns of these primary stocks that make up the portfolio. The method next includes identifying through market research and other means another set of stocks as secondary or sympathetic stocks that are correlated to these primary stocks in equation (7). Let R₀ represent the covariance matrix of the returns of these secondary stocks that is also defined similar to equation (10). Since the secondary stocks have some influence on the behavior of the primary stocks, the argument here is that their covariance matrix R₀ must also contribute to the overall risk of the portfolio. Thus in this approach, a scalar function of R₀ is added to the primary risk factor in equation (9). In our case, the trace of R₀ (sum of the diagonal entries of the covariance matrix R₀) of the secondary returns is used as the scalar function. This gives the modified risk of the portfolio to be

σ_P²=α^TRα+σ_o²   (48)

where

ν_o²=tr(R₀)   (49)

represents the trace of R₀. In this case the optimization problem in equation (26) gets modified as

$\begin{array}{cc}\frac{G^2}{{\sigma }_P^2}=\frac{{{\underset{\_}{a}}^T\underset{\_}{\mu }}^2}{{\underset{\_}{a}}^TR\underset{\_}{a}+{\sigma }_0^2}.& \left(50\right)\end{array}$

Let the vector α₀ represent the optimum nonnegative vector (constrained optimization using the non-negativity condition) that maximizes the ratio in equation (50) and whose elements add up to unity. In this approach, the capital will be partitioned according to the entries of this new vector α₀ and invested in the primary stocks.

The nonnegative vector α₀ above represents a suboptimum solution, and as in equations (26)-(33) there exists an unconstrained (without the nonnegative condition) optimum vector b_opt that maximizes equation (50), and once again the capital can be partitioned according to the entries of the vector b_opt and invested in the primary stocks. In this case the strategy can include short sales as well.

The globally optimum vector b_opt may be solved by noticing that (50) can be rewritten as

$\begin{array}{cc}\frac{G^2}{{\sigma }_P^2}=\frac{{{\underset{\_}{a}}^T\underset{\_}{\mu }}^2}{{\underset{\_}{a}}^T\left(R+\frac{{\sigma }_{0}^{2}}{{\underset{\_}{a}}^2}I\right)\underset{\_}{a}},& \left(51\right)\end{array}$

where I represents the m×m identity matrix (with ones along the main diagonal and zeros elsewhere), and

∥α∥²=α^Tα>0   (52)

represents the norm (square of the length) of the vector α. In (51), proceeding as in (27) through (29), we obtain the following solution

$\begin{array}{cc}\underset{\_}{a}={c\left(R+\frac{{\sigma }_{0}^{2}}{{\underset{\_}{a}}^2}I\right)}^{-1}\underset{\_}{\mu }& \left(53\right)\end{array}$

(where c is a normalization constant) that suggest the iteration

$\begin{array}{cc}{\underset{\_}{a}}_{k+1}={c\left(R+\frac{{\sigma }_{0}^{2}}{{{\underset{\_}{a}}_k}^2}I\right)}^{-1}\underset{\_}{\mu }.& \left(54\right)\end{array}$

After normalizing (54) as in equations (32)-(33) so that its entries add up to unity, we obtain the desired iteration to be

$\begin{array}{cc}{\underset{\_}{a}}_{k+1}=\frac{{\left(R+\frac{{\sigma }_{0}^{2}}{{{\underset{\_}{a}}_k}^2}I\right)}^{-1}\underset{\_}{\mu }}{{{\underset{\_}{e}}^T\left(R+\frac{{\sigma }_{0}^{2}}{{{\underset{\_}{a}}_k}^2}I\right)}^{-1}\underset{\_}{\mu }},& \left(55\right)\end{array}$

that can be used to solve for the above optimum vector b_opt. The above iteration is seen to converge in a variety of situations.

FIG. 4 shows a flow chart 300 of a method in accordance with an embodiment of the present invention. At step 302, a stock returns vector, such as r(n) calculated by equation (3) and (7), is determined for m primary stocks and at step 304 another stock returns vector is calculated for a certain number of secondary stocks in a similar manner. Next, at step 306 a means of returns vector, such as μ is calculated by equation (4) and (8), and a covariance matrix R is computed as in equation (10) for the primary stocks, and at step 308 a covariance matrix R₀ for the secondary stocks is computed similar to equation (10). Equation (49) is used to determine the trace of the secondary returns σ_o² at step 310. Next at step 312, an initial starting vector is determined, which becomes the old vector α_k at step 312 to start the iteration, and determine the norm of ∥α_k∥² as in equation (52). At step 310, a modified inverse covariance matrix such as

${\left(R+\frac{{\sigma }_{0}^{2}}{{{\underset{\_}{a}}_k}^2}I\right)}^{-1}$

is determined. At step 310 weighting factors, such as the new vector α_k+1 are determined based on the above modified inverse covariance matrix times the means for primary returns vectors divided by the sum of the new vector entries so obtained, such as by equations (54) or (55). At step 314, the difference of the new vector α_k+1 and old vector α_k is defined as the error vector. At step 316 the error norm is computed as in equation (52) for the error vector, and it is compared with a predetermined threshold value, such as for example 0.001 etc. If the error norm is less than the preset threshold value, the new vector obtained at step 310 is taken as the desired weighing factors at step 318. Otherwise, the old vector is replaced with the contents of the new vector at step 320 and it is fed back to step 310, where the entire cycle is repeated till the desired accuracy is achieved.

Optimum Nonnegative Solution: Interestingly, it is possible to guarantee the solution given by equations (54)-(55) to be nonnegative by treating σ_o² in equations (54)-(55) as a free parameter. Recall that σ_o² represents a measure of the effect of the correlation of the secondary stocks on the primary stocks, and for a given value of σ_o², the optimum vector in (54) can have both positive and negative entries. In such situations, by increasing the value of σ_o² the optimum vector can be made nonnegative there by avoiding short sales. In fact, for any given covariance matrix R and nonnegative vector μ, there exists a minimum positive value for the constant σ_o² in the equation

α=(R+σ₀²I)⁻¹μ  (56)

for which the vector α becomes nonnegative. The proof follows by expanding equation (56) and noticing that as the constant σ_o² becomes large, the perturbation terms to the first term μ in the expansion become of decreasing importance, and hence the vector α becomes nonnegative. Using this approach in equations (54)-(55), it follows that there exists a minimum threshold value for the sympathetic stocks' variance term σ_o², above which the optimum vector b_opt remains non-negative. Using any value above this threshold value for σ_o² in equations (54)-(55) avoids short sales for the optimum portfolio mixing strategy. To determine this threshold value, one may proceed using the iterative steps in FIG. 4 where the term σ_o² is treated as a free parameter. For a preset value of σ_o² if the final weight factor vector at stage step 318 in FIG. 4 turns out to have negative entries, the whole process is repeated with a larger value for the preset term σ_o² until all entries of the weight vector factor at step 318 turns out to be positive.

As an example, consider a portfolio containing three stocks whose 3×3 covariance matrix is give by

$\begin{array}{cc}R=\left(\begin{array}{ccc}1.0& 0.40& -0.25\\ \mathrm{.40}& 0.80& -\mathrm{.30}\\ -0.25& -0.30& 2.0\end{array}\right)& \left(57\right)\end{array}$

and let μ=(0.20 0.50 0.40)^T represent their the expected return values vector. In that case the solution in equation (33) that maximizes the overall gain to risk ratio is given by R⁻¹μ=(−0.237 0.7531 0.310)^T and it has one negative entry and hence it involves short sales. However using σ_o²=0.034995, the solution in (56) after normalization turns out to be α=(0.0000025 0.683533 0.293664)^T. Since the new solution has all nonnegative entries, it avoids short sales. Using any other value above this threshold for σ_o² results in all positive values for the solution and it avoids short sales in the optimum portfolio.

In summary, methods in accordance with embodiments of the present invention for determining the optimization strategies for building a new portfolio mixing vector are disclosed. In at least most if not all of these cases, the ratio of the overall portfolio gain function to the portfolio risk is maximized, where the definition of the portfolio risk is extended in one case to include the influence of stocks that are sympathetic to the primary stocks of interest. If short selling stocks is allowed, the above strategies can be applied to any set of stocks; if short selling is not permitted, then the selection of stocks and their number that goes into the actual portfolio becomes important and it must be accomplished so as to maintain the desired portfolio mixing vector to be positive, while maintaining a high yield (gain) with minimum fluctuations (risk). This can also be accomplished by extending the definition of risk to include a free variable term that denotes the effect of a secondary set of stocks, and by increasing this term the desired portfolio mixing vector can be made positive through an iterative procedure.

Although the invention has been described by reference to particular illustrative embodiments thereof, many changes and modifications of the invention may become apparent to those skilled in the art without departing from the spirit and scope of the invention. It is therefore intended to include within this patent all such changes and modifications as may reasonably and properly be included within the scope of the present invention's contribution to the art.

Claims

1. A method comprising determining a first return value for a first stock;determining a second return value for the first stock;determining a mean return value for the first stock based on averaging the first and second return values for the first stock;determining a first return value for a second stock;determining a second return value for the second stock;determining a mean return value for the second stock based on averaging the first and second return values for the second stock;determining a co-variance matrix and its inverse based on the first and second return values for the first stock, the mean return value for the first stock, the first and second return values for the second stock, and the mean return value for the second stock;determining weight factors for the first and second stocks, respectively, by multiplying the inverse co-variance matrix times the mean return values for the first and second stocks; and normalizing them by dividing them by their sum;specifying a first amount for purchasing of the first stock based on the first weight factor; andspecifying a second amount for purchasing of the second stock based on the second weight factor.

2. The method of claim 1purchasing the first stock using the first amount; andpurchasing the second stock using the second amount.

3. A method for investing a given capital sum by distributing it among a set of investments comprising determining a plurality of weight factors, one for each investment in the set of investments by which the capital sum will be partitioned so as to determine an actual amount to be invested in each investment;wherein the plurality of weight factors make up a weight factor vector;wherein the plurality of weight factors are determined by maximizing a ratio of an expected portfolio gain to an overall risk,wherein the expected portfolio gain is obtained by multiplying a transpose of the weight factor vector with a vector of mean return values, andthe overall risk is obtained by first multiplying the transpose of the weight factor vector with a covariance matrix of a plurality of returns corresponding to the set of investments, and multiplying the result with the weight factor vector.

4. A method for investing a given capital sum by distributing it among a set of investments comprising determining a plurality of weight factors, one for each investment in the set of investments by which the capital sum will be partitioned so as to determine an actual amount to be invested in each investment; andwherein the plurality of weight factors are determined by maximizing a ratio of a square of an expected portfolio gain to an overall risk;wherein the plurality of weight factors make up a weight factor vector;wherein the expected portfolio gain is obtained by multiplying a transpose of the weight factor vector with a vector of mean return values, andthe overall risk is obtained by first multiplying the transpose of the weight factor vector with a covariance matrix of a plurality of returns corresponding to the set of investments, and multiplying the result with the weight factor vector.

5. The method of claim 4 wherein if short selling is not allowed, the step of maximizing the ratio of the square of the expected portfolio gain to the overall risk is further carried out subject to nonnegativity constraint on the plurality of weight factors.

6. A method comprising determining a set of a plurality of return values for each of a plurality of investments;determining a vector of mean return values, one mean return value for each of the plurality of investments;determining a co-variance matrix and its inverse based on the sets of a plurality of return values, and the vector of mean return values;determining a vector of weight factors, one weight factor for each of the plurality of investments, by multiplying the inverse co-variance matrix times the vector of mean return values and normalizing them by dividing them by their sum;specifying a plurality of amounts to purchase, one amount for each of the plurality of investments determined by multiplying a total capital amount by a weight factor of the vector of weight factors for each of the plurality of investments.

7. A method for investing a given capital sum by distributing it among a set of investments comprising using a first numerical filter to un-correlate the set of investments; andusing a second numerical filter to maximally combine the un-correlated set of investments.

8. A method for investing a given capital sum by distributing it among a portfolio of a set of primary investments comprising determining a plurality of weight factors, one for each investment in the set of investments by which the capital sum will be partitioned so as to determine an actual amount to be invested in each investment; andwherein the plurality of weight factors are determined bymaximizing the ratio of the square of the expected portfolio gain of the set of primary investments to a modified risk;wherein the modified risk is defined as the sum of an original risk for the portfolio and a function of a covariance matrix of a secondary set of stocks that are sympathetic to the set of primary investments.

9. The method of claim 8 wherein if short selling is not allowed, the above said maximization is carried out subject to a nonnegativity constraint on the plurality of weight factors.

10. A method comprising selecting a primary set of investments;selecting a secondary set of investments which are related to the primary set of investments;determining returns for the primary set of investments;determining returns for the secondary set of investments;determining an expected mean returns vector for the primary set of investments;determining a covariance matrix for the primary set of investments;modifying the covariance matrix for the primary set of investments by adding a diagonal matrix generated from a covariance matrix for the secondary set of investments; anddetermining weighting factors a for investing in the primary set of investments iteratively from the below fraction:

11. The method of claim 10 wherein the scalar function generated from the covariance matrix of the secondary set of investments is a trace of the covariance matrix of the secondary set of investments.

12. The method of claim 10 wherein if short selling is not allowed, determining the weighting factors by treating σo2 as a free variable this time, incrementally increasing this free variable σo2 until all of the weighting factors become positive after a desired number of iterations.

13. The method of claim 10 wherein the primary set of investments includes a plurality of mutual funds.

14. The method of claim 10 wherein the primary set of investments includes a plurality of hedge funds.

15. The method of claim 10 wherein the primary set of investments includes a plurality of index funds.

16. The method of claim 10 wherein the primary set of investments includes a number of investments;wherein the number of investments in the primary set of investments is a free variable that can be used to fulfill a nonnegativity condition for the vector of weighting factors.

17. A method comprising selecting a primary set of investments;determining a returns vector for the primary set of investments;determining an expected mean returns vector for the primary set of investments;determining a covariance matrix for the primary set of investments;determining a vector of weighting factors aopt for the primary set of investments, wherein:

18. The method of claim 17 wherein the primary set of investments includes a plurality of mutual funds.

19. The method of claim 17 wherein the primary set of investments includes a plurality of hedge funds.

20. The method of claim 17 wherein the primary set of investments includes a plurality of index funds.

21. The method of claim 17 wherein if short selling is not allowed, further comprising selecting the primary set of investments from a large pool of available stocks or securities so that the inverse of the covariance for the primary set of investments is a nonnegative matrix, with all entries being nonnegative numbers.

22. The method of claim 17 wherein If short selling is not allowed, further comprising selecting the primary set of investments from a large pool of available stocks or securities in such a way that, for a given positive vector μ that represents the expected mean value for the period of investment of the returns for the primary set of investments so selected, the vector R−1μ must have all nonnegative entries.

23. The method of claim 17 wherein the primary set of investments includes a number of investments;

24. An apparatus comprising a computer processor; andwherein the computer processor is programmed to receive an identification of a primary set of investments;determine a returns vector for the primary set of investments;determine an expected mean returns vector for the primary set of investments;determine a covariance matrix for the primary set of investments;determine a vector of weighting factors a opt for the primary set of investments, wherein:

25. An apparatus comprising a computer processor;and wherein the computer processor is programmed to receive an identification of a primary set of investments;select a secondary set of investments which are related to the primary set of investments;determine returns for the primary set of investments;determine returns for the secondary set of investments;determine an expected mean returns vector for the primary set of investments;determine a covariance matrix for the primary set of investments;determine a covariance matrix for the secondary set of investments;modify the covariance matrix for the primary set of investments by adding a diagonal matrix generated from the covariance matrix for the secondary set of investments; anddetermine weighting factors a for investing in the primary set of investments iteratively from the below fraction:

26. The apparatus of claim 25 wherein the scalar function generated from the covariance matrix of the secondary set of investments is a trace of the covariance matrix of the secondary set of investments.