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MONTE CARLO PORTFOLIO OPTIMIZATION FOR GENERALINVESTOR RISK-RETURN OBJECTIVES AND ARBITRARY

RETURN DISTRIBUTIONS:A SOLUTION FOR LONG-ONLY PORTFOLIOS

WILLIAM T. SHAW

Abstract. We develop the idea of using Monte Carlo sampling of random portfolios to solveportfolio investment problems. In this first paper we explore the need for more general optimizationtools, and consider the means by which constrained random portfolios may be generated. A practicalscheme for the long-only fully-invested problem is developed and tested for the classic QP application.The advantage of Monte Carlo methods is that they may be extended to risk functions that are morecomplicated functions of the return distribution, and that the underlying return distribution maybe computed without the classical Gaussian limitations. The optimization of quadratic risk-returnfunctions, VaR, CVaR, may be handled in a similar manner to variability ratios such as Sortinoand Omega, or mathematical constructions such as expected utility and its behavioural financeextensions. Robustification is also possible. Grid computing technology is an excellent platform forthe development of such computations due to the intrinsically parallel nature of the computation,coupled to the requirement to transmit only small packets of data over the grid. We give someexamples deploying GridMathematica, in which various investor risk preferences are optimized withdiffering multivariate distributions. Good comparisons with established results in Mean-Varianceand CVaR optimization are obtained when edge-vertex-biased sampling methods are employed tocreate random portfolios. We also give an application to Omega optimization.

Key words. Portfolio, Optimization, Optimisation, Random Portfolio, Monte Carlo, Simplex,VaR, CVaR, QP, Mean-Variance, Non-Gaussian, Utility, Variability Ratio, Omega, Sortino ratio,Sharpe ratio.

AMS subject classifications. 90A09, 90C26, 90C30, 91G10

You cant be serious, man. You can not be serious! John McEnroe, 1981.

1. Introduction. The management of financial portfolios by the methods ofquantitative risk management requires the solution of a complicated optimizationproblem, in which one decides how much of a given investment to allocate to each ofN assets - this is the problem of assigning weights. There are many different formsof the problem, depending on how large N is (2 2000+!), whether short-selling isallowed, investor preferences, and so on. In the simplest form of the problem, whichis still relevant to traditional fund management, the weights are non-negative (youcannot go short) and are normalized to add to unity. So if we have an amountof cash P to invest and N assets to choose from, the weights are wi, i = 1, . . . , Nsatisfying

wi 0 (1.1)

Ni=1

wi = 1 (1.2)

This long-only form of the problem is of considerable importance, but not the onlyone of interest. Additional constraints are often specified, and this will be allowed forin the following discussion. The development of optimized Monte-Carlo tools withthe addition of short-selling is under investigation.

The simplest and very ancient solution to this problem is to take wi = 1/N , whichis equal allocation, and has been understood for hundreds of years as a principle. The

Department of Mathematics, Kings College London, Strand, London WC2R 2LS(william.shaw@kcl.ac.uk).

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amount of cash invested in asset i is then, in general, Pwi and in the equal-allocationstrategy it is P/N . The oldest currently known statement of this principle is due toone Rabbi Isaac bar Aha, who in an Aramaic text from the 4th Century, stated hisequal allocation strategy as: A third in land, A third in merchandise, A third in cash(a third at hand). The usefulness of this robust strategy continues to be explored -see for example the beautiful study by DeMiguel et al [12] and references therein.

It has become an in-joke to state that the underlying mathematical theory did notmove on very quickly after the Rabbi. There were sporadic references in literature.Some proverbial mis-translation of Don Quixote is sometimes given as: Tis the partof wise man to keep himself today for tomorrow, and not venture all his eggs in onebasket, but there is in fact no evidence in the original text of eggs. Shakespeareillustrated the concept of diversification around the same time in The Merchant ofVenice, when Antonio told his friends he wasnt spending sleepless nights worryingover his commodity investment: Believe me, no. I thank my fortune for it, Myventures are not in one bottom trusted, Nor to one place; nor is my whole estate uponthe fortune of this present year. Therefore my merchandise makes me not sad. Ithelps to know that bottom refers to a ships hull, and that the diversification hereis intended to prevent the entire cargo going down with a single ship. But note thatShakespeare had quietly added temporal diversification.

There is also a contrarian thread of thought1: some people follow Mark Twainscontrarian, anti-portfolio investment strategy (from Puddnhead Wilson) and seek toconcentrate on the next big thing: Behold, the fool saith, Put not all thine eggs in theone basket - which is but a manner of saying, Scatter your money and your attention,but the wise man saith, Put all your eggs in the one basket and - watch that basket.A similar quote is also attributed to Andrew Carnegie, and it is often claimed thatWarren Buffett leans towards a similar philosophy, based on several observations andhis infamous quote: Concentrate your investments. If you have a harem of 40 womenyou never get to know any of them very well. In the aftermath of the financial crisis,the following anti-diversification theme seems even more pertinent2: If you are stuckon a desert island and need a drink of water, and know that one of the three streamsis poisoned, but not which one, would you take a third of your need from each stream?Such debates will continue.

Putting aide the cultural discussion, the mathematical theory moved on in thepost-WWII work of Markowitz and collaborators, (see for example [13, 14, 15]). Myown understanding is that Markowitz was in fact interested in the control of downsiderisk (the semivariance), but given the technology then available he used variance asa proxy for risk. where the idea was formulated as (in the simplest version), theminimization of risk subject to a return goal. This resulted in a problem to minimizefunctions of the form

Ni=1

Nj=1

wiwjCij Ni=1

Riwi (1.3)

where Cij is the covariance matrix of the assets and Ri are the expected returns. Withthe summation convention due to Einstein, this is usually written in either of the morecompact forms

wiwjCij Riwi = wT .C.w R.w (1.4)

and the minimization is subject to the constraints of equations (1,2) and indeed anyother additional conditions (e.g. sector exposure, upper and lower bounds etc.) The

1My thanks to Michael Mainelli for making me aware of this.2I apologise for not being able to quote the source of this idea - seen once on the web and not

found again - correction requested.

Monte Carlo Portfolio Optimization for General Objectives and Distributions 3

quantity is a Lagrange multiplier expressing an investors risk-return preference,and there are other ways of writing down the problem.

This form of the problem, known as constrained Quadratic Programming (QP), ishighly restrictive. First there is the underlying idea that the risk is characterized solelyby the variance. Implicit in this is the idea that the distribution of returns is essen-tially Gaussian. Second, if one thinks more generally in terms of maximizing expectedUtility, Eqn. (1.4) only emerges if one either approximates, or considers exponentialutility and again Gaussian returns. Third, the answers are extremely unstable withrespect to changes in covariance or expected return. Fourth, the dependency modelbetween returns is quite explicitly Gaussian, expressed by the covariance. Patholog-ical covariances with zero or near-zero eigenvalues cause an instability, and optimalconfigurations may have zero weights in some assets and not in others. It will beimportant to realize that interior-point solutions are quite rare in practical examples,and optima frequently sit on boundary planes and vertices.

There are other complications - investors might need more complex constraintsets and/or allow for short-selling, where some weights are negative, especially forhedge fund applications. These are fairly easily admitted into classical computationtechniques, but relaxing all the other restrictions is problematic. Recent work onRobust Optimization allows for some uncertainty in the parameters, for example,with a QP framework. See for example Goldfarb et al [7] and references therein, [22]for minimal eigenvalue management.

Investor preferences might not be expressed in terms of variance-return struc-tures. Expected Utility is a common theme in more mathematical studies, but thereare also specific investment functions that are written down without explicit referenceto utility. Value at Risk and coherent variations are often considered, but remain con-troversial. The measures of VaR and CVaR have attracted a lot of attention followingthe pioneering work of Rockafellar and Uryasev [20]. This work, which I shall referto collectively as the RU-method, opened up a whole new area of research into theoptimization of full distributional measures of risk (i.e. not just working with simplemoments). From our point of view a key feature is the modelling of the return distri-bution by Monte Carlo sampling of returns. This should be distinguished from what isbeing proposed now, in that