Abstract:
In one aspect a computer-implemented approach to portfolio selection (1) forecasts covariance matrices with explicit estimates of error, (2) propagates estimates to forecast with explicit error the variance of any linear combination of items (a portfolio), and (3) by augmenting an objection function, improves the realized performance of optimized investment portfolios.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Application No. 62/328,274, filed Apr. 27, 2016, which is incorporated herein by reference. 
     
    
     BACKGROUND 
       [0002]    This invention relates to a quantitative approach to use of an uncertain utility to improve a portfolio selection approach, and more particularly as applied to portfolio selection of financial instruments. 
         [0003]    In the portfolio selection problem, investors seek to construct a portfolio—allocate or reallocate their money across different assets—in a way that best
       1. profits from their beliefs about future performance—how much each asset will increase or decrease in value   2. meets constraints—e.g., limits on the amount invested in a particular asset, economic sector, or geographic region; limits on portfolio turnover   3. considers additional costs—e.g., transaction costs, taxes   4. prefers tower risk (downside or volatility) to greater.       
 
         [0008]    Referring to  FIG. 1 , conventional financial portfolio selection invokes examining current holdings (which can include cash)  110 , one&#39;s beliefs about how Investments will perform  120 , any constraints or rules  130 , beliefs about costs  140 , and beliefs about riskiness  150 . Considering these in conjunction, one decides  160  what combination of holdings seems best  170 . 
         [0009]    [Markowitz, 1952] introduced the notion of optimal investment portfolios, allocations that yield the highest expected return at their level of return variance. Common industry practice today is to find, by computer optimization, allocations that maximize the practitioner&#39;s believed utility, an aggregated consideration of the concerns previously mentioned—predicted return, variance, transaction costs, taxes, and other aspects subject to constraints, e.g., position limits, maximum turnover. 
         [0010]    Referring to  FIG. 2 , portfolio selection by optimization takes current holdings  210 , forecasts of future return  220 , constraints  230 , models of costs  240 , a covariance model to measure risk  250 , and a benchmark portfolio  260  serving as a reference. A computer optimizer  270  finds the portfolio that maximizes return less a penalty on (usually benchmark-relative) variance less consideration for costs subject to constraints. Outputs are the maximizing holdings  280  and reports  290 . 
         [0011]    Solving one problem, computer optimization, created another, identified over three decades ago, e.g., [Jobson and Korkie, 1980], and still present. With the ability to consider every possible configuration and weigh information in a way humans can&#39;t, computers follow inputs to find maxima in odd or extreme configurations. Structured around fully trusted, unavoidably inaccurate estimates, optimized portfolios don&#39;t achieve the criteria that, misperceived, led to their creation. 
         [0012]    Strategies are employed to mitigate the problem. In practice, most common is to pre-condition, then proceed with conditioned point estimates as if they were true, e.g., [Black and Litterman, 1992], [Ledoit and Wolf, 2003]. 
         [0013]    Another strategy, from U.S. Pat. No. 6,003,018 [Michaud and Michaud, 1990], is to 1—simulate data under the point estimated parameters (mean and covariance), 2—reinfer parameters from the simulated, data, 3—construct a portfolio from these parameters, 4—repeat the process many times, and in the end, 5—take some sort of average of the constructed portfolios. 
         [0014]    A third strategy, called robust optimization, e.g., [Goldfarb and Iyengar, 2003], ventures beyond point estimates: parameters fall, within a confidence range; optimization seeks the portfolio that has the best worst case. However, the complicated optimization—maximum of minimum—rigidly depends on special cases, e.g., see flowchart ‘ FIG. 3 ’ of U.S. Pat. No. 7,401,041 B2 [Goldfarb and Iyengar, 2008]. 
       Conventional Factor Model Covariance and Portfolio Selection 
       [0015]    Conventional practice models the covariance matrix of stock returns and prices that appears throughout the investment process—e.g., forecasting portfolio risk, hedging, algorithmic trading, constructing portfolios via computer optimization—via factors. 
         [0016]    Because there are too many securities to accurately infer the many pairwlse entries, covariance models are bulk on a skeleton of common factors, e.g., market, industry, and growth/value spread. Covariance between securities happens only through links to these factors. Movement uncorrelated with factors is called specific. The standard model has three parts, all estimated: covariance of factors, sensitivities (exposures) to factor movements, and variance of specific movements. 
         [0017]    A simple example in the flavor of CAPM: Suppose there were only 1 factor, market return. The covariance of the factors would be (1×1), the variance of the market. A stock&#39;s exposure to the factor would be the stock&#39;s beta, inferred, among other ways, by OLS regressing the stock returns against market returns, or allowing for dynamics, by Kalman filter. The residual errors unexplained by the market would be the stock&#39;s specific movement. 
         [0018]    More generally, covariance between N items is represented using K factors as: 
         [0000]      ( N×N ) covariance  C=EFE   T +Diag[ε 2 ]  (1)
       where F=(K×K) covariance between factors   E=(N×K) exposures to factors   ε 2 =(N×1) specific variances       
 
         [0022]    [Chan et. al, 1999] compares several standard methods of constructing these. All in the end yield a point estimate of the covariance matrix. 
         [0023]    A portfolio, a linear combination of items, has modeled variance: 
         [0000]      Var[ w   1 ×item 1+ . . . + w   N ×item  N]=w   T   Cw   (2)
 
         [0000]      = w   T ( EFE   T +Diag[ε 2 ]) w   (3)
       where w=(N×1) portfolio weights or holdings       
 
         [0025]    Referring to  FIG. 3 , in a conventional factor covariance model, a vector of portfolio holdings  310  is combined with the covariance model  320  to generate a portfolio variance number  340 . The calculations  330  are equation (3). 
         [0026]    Adding estimates of future returns, α, and a risk aversion, λ, leads to a basic form of portfolio selection by optimization, in this case solved by quadratic programming: 
         [0000]      maximize w  return ( w )−λ×variance( w )  (4)
 
         [0000]      =α T   w−λw   T ( EFE   T +Diag[ε 2 ]) w   (5)
 
         [0027]    Additional terms and constraints, depending on their shape, can change the solving algorithm, e.g., to nonlinear programming. 
         [0028]    Referring to  FIG. 4 , in conventional portfolio selection by optimization, current holdings, the utility function, and its ingredients—forecasts of mean future returns and transaction costs, perhaps a benchmark e.g., S&amp;P500—and constraints  410  are passed to an optimizer  430 . The optimizer maximizes utility, with the portfolio variance term  450  in the utility function being a point estimate from the covariance model  420  (a collapse of  FIG. 3 ) applied to the portfolio  440 . Optimization yields optimal holdings  460  and the value of optimal utility  470 . 
         [0029]    This conventional method ignores that inputs are imperfect forecasts. The present invention improves realized performance by altering the utility function to explicitly consider accuracy. 
       REFERENCES 
       [0030]    [Black and Litterman, 1992] Black, F. and Litterman, r. (1992). Global portfolio optimization.  Financial Analysts Journal,  48(5):28-43. 
         [0031]    [Chan et al., 1999] Chan, L. K., Karceski, J., and Lakonishok, J. (1999). On portfolio optimization: Forecasting covariances and choosing the risk model.  Review of Financial Studies,  12(5):937-974. 
         [0032]    [Goldfarb and Iyengar; 2003] Goldfarb, D. and Iyengar, G. (2003). Robust portfolio selection problems.  Mathematics of Operations Research,  28(1): 1-38. 
         [0033]    [Goldfarb and Iyengar; 2008] Goldfarb, D. and Iyengar, G. (2008). Systems and methods for providing robust investment portfolios. Patent. U.S. Pat. No. 7,401,041B2. 
         [0034]    [Jobson and Korkie, 1980] Jobson, J. D. and Korkie, B. (1980). Estimation for markowitz efficient portfolios.  Journal of the American Statistical Association,  75(371):544-554. 
         [0035]    [Ledoit and Wolf, 2003] Ledoit, O. and Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection.  Journal of empirical finance,  10(5):603-621. 
         [0036]    [Markowitz, 1952] Markowitz, H. (1952). Portfolio selection.  The Journal of Finance,  7(1):77-91. 
         [0037]    [Michaud and Michaud, 1999] Michaud, R. O. and Michaud, R. (1999). Portfolio optimization by means of resampled efficient frontiers. Patent. U.S. Pat. No. 6,003,018 A. 
         [0038]    [Shah, 2015] Shah, A. R. (2015). Uncertain covariance models (previously titled incorporating estimates of error into linear factor covariance models).  Available at SSRN  2616109. 
       SUMMARY 
       [0039]    The approach presented herein addresses the estimation error problem with a new tool: uncertain utility. The approach provides improvement over previous approaches, for instance as described in U.S. Pat. No. 6,003,018 [Michaud and Michaud, 1999], at least because it moves beyond point estimates and explicitly considers estimates of accuracy. The approach is an improvement over U.S. Pat. No. 7,401,041 B2 [Goldfarb and Iyengar, 2008], at least because it propagates uncertainty to the consequences of importance, portfolio variance and utility, which can be optimized beyond special cases. 
         [0040]    As introduced above, current techniques that assume that the estimates E, F, and ε that make up a covariance model can be treated as the true values in forecasting covariance and determining an optimal portfolio weighting essentially ignore the fact that there is uncertainty in these estimates, and the conventional techniques of the type described above do not take into account the uncertainty. 
         [0041]    Some quantities are forecasted more accurately than others, and considering accuracy improves decision making. There is a need to be able to account for the variability in a computationally efficient manner in order to be able to make timely and accurate predictions that are useful for selection of portfolios. 
         [0042]    In one aspect, in general, the approach to portfolio selection provides an easily constructed, and communicated means to model uncertain covariance between securities and forecast the mean and variance of portfolio variance. 
         [0043]    The ability to do this for any indeterminate portfolio permits penalizing the uncertainty of utility in optimization to better realize the skill in forecasting and mitigate the problem of errors in estimates. 
         [0044]    In one aspect, in general, a computerized system for portfolio selection uses quantifications of uncertainty of variance (risk) estimates. The system includes an input for accepting a specification of a plurality of financial instruments, the specification of each instrument including a first quantity representing a variance of a rate of return, and a second quantity representing an uncertainty in the first quantity, a computation module configured to determine weights to apply to the financial instruments to form a portfolio, wherein determining the weights includes optimizing a utility function that depends on both the first quantity and the second quantity for each of the financial instruments, and an output for providing the weights of the portfolio to a user. 
     
    
     
       DESCRIPTION OF DRAWINGS 
         [0045]      FIG. 1  is an example of the portfolio selection problem. 
           [0046]      FIG. 2  is an example of portfolio selection via portfolio optimization. 
           [0047]      FIG. 3  is a diagram demonstrating the inputs and outputs of portfolio variance calculation using a conventional covariance model. 
           [0048]      FIG. 4  is a conventional portfolio selection approach. 
           [0049]      FIG. 5  is a diagram demonstrating the inputs and outputs of portfolio variance calculation using an uncertain covariance model. 
           [0050]      FIG. 6  is a novel portfolio selection approach. 
           [0051]      FIG. 7  is a diagram demonstrating how to build a simple uncertain covariance model. 
           [0052]      FIG. 8  is a diagram of a portfolio selection system that considers uncertainty. 
       
    
    
     DETAILED DESCRIPTION 
       [0053]    In one embodiment, a computer-based system provides a user with a way to select a portfolio. In some implementations, the system is implemented on a computer that the user directly accesses (e.g., a Personal Computer), while in other implementations, the user accesses a computer that is remote from the user, for example, over a data network. In either case, the system makes use of a software-controlled, processor with instructions stored on a non-transitory machine-readable medium. As will be evident through the description below, the approach goes well beyond a mere automation of a conventional approach. That is, for instance, use of an uncertain covariance is not a long-prevalent or fundamental economic practice. Furthermore, as can be appreciated based on the description below, the approach cannot be performed mentally in a short enough time sufficient to make use of the result, and is therefore made possible via a computer-based implementation. That is, even if one could mentally perform the computational steps, such a “pencil and paper” mental process would not meet the need to select, a portfolio in a practical timeframe. 
         [0054]    While the conventional covariance model presented above yields only point estimates, uncertain covariance models include additional data for the uncertainty in the individual pieces making up the model. These data make it possible to calculate both the expected value and uncertainty variance of a portfolio&#39;s variance. 
         [0055]    The conventional model has assumed accurate exposures E, stock-specific standard deviations ε, and factor covariance F. The uncertain covariance model regards these as inferences and uses explicit estimates of inaccuracy: E has estimated mean Ê and covariance {circumflex over (X)}. ε has mean {circumflex over (ε)} and variance {circumflex over (ω)}. F is factored into F=M Diag[θ]M T  where θ has mean {circumflex over (θ)} and covariance {circumflex over (Ω)}, and M has mean {circumflex over (M)} and covariance {circumflex over (Γ)}. Setting a variance estimate to 0 makes the corresponding piece be regarded as certain, for example, if its uncertainty isn&#39;t available. 
         [0056]    Referring to  FIG. 5 , to estimate the variance of a portfolio in the uncertain covariance model—analogous to the conventional model&#39;s  FIG. 3 —a vector of portfolio holdings  510  is combined with the uncertain covariance model  520  to generate both the mean  540  and variance  550  of portfolio variance. The calculations  530  are equations (22) and (23) of the Appendix. 
         [0057]    Consider again as an example of portfolio utility for portfolio selection, the basic utility function: 
         [0000]        U ( w )=α( w )−λν( w )  (6)
 
         [0000]      where 
         [0000]      α( w )= E[r ( w )]  (7)
 
         [0000]      ν( w )=Var[ r ( w )]  (8)
 
         [0000]      w=absolute or benchmark relative portfolio weights  (9)
 
         [0000]      λ=investor&#39;s risk aversion  (10)
 
         [0000]        r ( w )=return of portfolio with weights  w   (11)
 
         [0058]    Assembling quantities known only through estimates, utility U(w) is uncertain. Uncertain covariance models&#39; ability to propagate uncertainty to the mean and variance of the variance of an indeterminate portfolio permits a new utility: 
         [0000]        E[U ( w )]−κ×Stdev[ U ( w )]  (12)
 
         [0000]    where κ is uncertainty aversion. With κ&gt;0, optimization seeks portfolios high in utility and low in uncertainty of utility. The less frequently one rebalances, the greater one&#39;s aversion to uncertainty. 
         [0059]    An approach of penalizing uncertainty is applied to the real-world conventional portfolio selection problem of  FIG. 4 . Referring to  FIG. 6 , all the conventional&#39;s information  610  is supplied. Additional data are the uncertain covariance model, an aversion to uncertainty  620 , and optional uncertainty information about any terms, beyond covariance, that should be modeled as inexact  630 . The optimizer  640  maximizes expected utility minus uncertainty aversion times the standard deviation of utility. This objective is made possible by the variance forecasts—expectation  652  and uncertainty variance  653 —of the portfolio  651  via the uncertain covariance model  650  (a collapse of  FIG. 5 ). Optimization yields optimal holdings  660  and the expected value and variance of optimal utility  670 . 
         [0060]    For the basic utility of equation (6) above, 
         [0000]      Var[ U ( w )]=λ 2 Var[ν( w )]+Var[α( w )]−2λCov[α( w ), ν( w )]  (13)
 
         [0000]      =λ 2 Var[ν( w )]+Var[α( w )]−2λρ[α( w ), ν( w )]Stdev[α( w )]Stdev[ν( w )]  (14)
 
         [0061]    The first term comes from equation (23) of the Appendix. The second term captures the different bets used to forecast the mean return. It can be ignored or, modeling α as Gaussian with covariance Σ: 
         [0000]      Var[α( w )]= w   T   Σw   (15)
 
         [0062]    The third term, unless modeled, would be assumed 0. 
         [0063]    Other terms added to the basic utility of equation (6)—e.g., for transaction costs—can be similarly incorporated: 
         [0000]    
       
         
           
             
               
                 
                   
                     
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         [0064]    Anything that has an explicit model of uncertainty variance—e.g., uncertain covariance, Gaussian, mean returns—is counted in the first set of terms. What lacks a model is assumed 0. The second set of terms, for covariance, are assumed 0 unless explicitly modeled. 
         [0065]    The uncertain utility function, equation (12), is continuous, nonlinear, non-concave, and with local maxima. A global maximum can be sought via general purpose appropriate optimization algorithms, e.g., simulated annealing, genetic algorithms, for instance available as a non-linear convex optimization software (e.g., in a programming language such as C++, Java, R, or Python). 
         [0066]    Ad-hoc, the uncertainty term can be turned off (κ=0), the simpler optimization solved by a specialized algorithm exploiting structure, e.g., quadratic programming, and the solution used, with the uncertainty term returned, as an initial search point for a local maximum. 
         [0067]    Operation of the computerized system for portfolio selection can be further understood based on the following example. First, a simple uncertain covariance model is built as follows: Referring to  FIG. 7 , time series of returns from N investments  710  are processed through principal components analysis  720  resulting in K orthonormal factors  730  and exposures to the factors and specific variances  740 . The empirical mean and covariance of exposures and of specific variances yields a prior  750 . Using the prior, the investment returns  710  are Bayes regressed against the factors  730  yielding expected mean and covariance of exposures and specific standard deviation  770 . (Here, {circumflex over (X)} is (N×K×K) rather than (N×N×K×K) because between investment exposure uncertainty covariance is 0.) To estimate the uncertainty of factor variances, all factors are assumed to have uncorrelated uncertainty of the same magnitude, which is estimated by the variance of market variance (proxied by the implied volatility index VIX squared) relative to its value L periods lagged  780 , where L matches the desired horizon of the model. Set {circumflex over (M)}=I since the fetors are already othonormal, assume {circumflex over (M)}&#39;s uncertainty {circumflex over (Γ)} is 0, and set {circumflex over (θ)} to 1. With  770  and  780 , this constitutes an uncertain covariance model  790 . 
         [0068]    The model can then be used to perform portfolio selection considering uncertainty. Referring to  FIG. 8 , the investment items to be analyzed  805  are collected to form a universe  810 . Item data with proxy rules for missing entries  815  is compared against the universe. Missing entries are proxied according to the rules—e.g., substitute 0. the universe median, or the record for a particular security; discard and reweight other securities—to form a foil organized set of necessary item data  820 . Non-Item data—e.g., the uncertainty variance of factors in the uncertain covariance model— 825  enters, and the terms appearing in the optimized objective are written as functions of an arbitrary portfolio and the Item and non-item data  830 . Constraints  840  are written as functions of the same and the objective function terms. Utility function parameters  845  are added, and expected utility is written as a function of the utility terms  850 . The standard deviation of utility is also written as a function of utility terms  860 . The uncertainty aversion parameter κ  865  determines the weight given to standard deviation of utility in the objective—expected utility—κ×standard deviation of utility—which is maximized  870  by changing the portfolio weights subject to constraints. The outputs are the optimizing portfolio weights  880  and reports  890 . 
         [0069]    The numerical approaches described in this document can be implemented, for example, using a programmable computing system executing suitable software instructions or they can be implemented in suitable hardware such as a field-programmable gate array (FPGA) or in some hybrid form. For example, in a programmed approach the software may include procedures in one or more computer programs that execute on one or more programmed or programmable computing systems (which may be of various architectures such as distributed, client/server, or grid) each including at least one processor, at least one data, storage system (including volatile and/or non-volatile memory and/or storage elements), at least one user interface (for receiving input using at least one input device or port, and for providing output using at least one output device or port). The software may include one or mom modules of a larger program, for example, that provides services related to the design, configuration, and execution of dataflow graphs. The modules of the program (e.g., elements of a dataflow graph) can be implemented as data structures or other organized data conforming to a data model stored in a data repository. 
         [0070]    The software may be stored in. non-transitory form, such as being embodied in a volatile or non-volatile storage medium, or any other non-transitory medium, using a physical property of the medium (e.g., surface pits and lands, magnetic domains, or electrical charge) for a period of time (e.g., the time between refresh periods of a dynamic memory device such as a dynamic RAM). In preparation for loading the instructions, the software may be provided on a tangible, non-transitory medium, such as a CO-ROM or other computer-readable medium (e.g., readable by a general or special purpose computing system or device), or may be delivered (e.g., encoded in a propagated signal) over a communication medium of a network to a tangible, non-transitory medium of a computing system where it is executed. Some or all of the processing may be performed on a special purpose computer, or using special-purpose hardware, such as coprocessors or field-programmable gate arrays (FFGAs) or dedicated, application-specific integrated circuits (ASICs). The processing may be implemented in a distributed manner in which different parts of the computation specified by the software are performed by different computing elements. Each such computer program is preferably stored on or downloaded to a computer-readable storage medium (e.g., solid state memory or media, or magnetic or optical media) of a storage device accessible by a general or special purpose programmable computer, for configuring and operating the computer when the storage device medium is read by the computer to perform the processing described herein. The inventive system may also be considered to be implemented as a tangible, non-transitory medium, configured with a computer program, where the medium so configured causes a computer to operate in a specific and predefined manner to perform one or more of the processing steps described herein. 
         [0071]    A number of embodiments of the invention have been described. Nevertheless, it is to be understood that the foregoing description is intended to illustrate and not to limit the scope of the invention, which is defined by the scope of the claims. Accordingly, other embodiments are also within the scope of the following claims. For example, various modifications may be made without departing from the scope of the invention. The claims are specifically limited to those embodiments that are within the ambit of 35 USC 101 as of the filing date of this application. To the extent claims are deemed to cover embodiments that are outside the ambit of 35 USC 101, those embodiments are specifically disclaimed. To the extent the claims are deemed to cover embodiments that are no more than software per se, those embodiments are specifically excluded from the claims.