Stock portfolio selection with full-scale optimization and differential evolution

Full-Scale Optimization (FSO) is a utility maximization approach to portfolio choice problems that has theoretical appeal but that suffers from computational burden in large scale problems. We apply the heuristic technique differential evolution to solve FSO-type asset selection problems of 97 assets under complex utility functions rendering rough utility search surfaces. We show that this problem is computationally feasible and that solutions retrieved with random starting values are converging to one optimum. Furthermore, the study constitutes the first FSO application to stock portfolio optimization. The results indicate that when investors are loss averse, FSO improves stock portfolio performance compared to Mean Variance (MV) portfolios. This finding widens the scope of applicability of FSO, but it is also stressed that out-of-sample success will always be dependent on the forecasting ability of the input return distributions.

[1]  Dietmar Maringer.  Constrained Index Tracking under Loss Aversion Using Differential Evolution , 2008, Natural Computing in Computational Finance.

[2]  Anil K. Bera,et al.  Efficient tests for normality, homoscedasticity and serial independence of regression residuals , 1980 .

[3]  Philip A. Horvath,et al.  On The Direction of Preference for Moments of Higher Order Than The Variance , 1980 .

[4]  Michael W. Brandt.  Estimating Portfolio and Consumption Choice: A Conditional Euler Equations Approach , 1999 .

[5]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[6]  Richard C. Grinold,et al.  Mean-Variance and Scenario-Based Approaches to Portfolio Selection , 1999 .

[7]  Sébastien Page,et al.  Optimal Hedge Fund Allocations , 2005 .

[8]  Björn Hagströmer,et al.  Meanvariance Versus Full-Scale Optimization: Broad Evidence for the UK , 2007 .

[9]  M. E. Ellis.  Mean–Variance versus Full-Scale Optimisation: In and Out of Sample , 2007 .

[10]  W. Sharpe.  Expected Utility Asset Allocation , 2007 .

[11]  John Liechty,et al.  Portfolio selection with higher moments , 2004 .

[12]  Dietmar Maringer,et al.  Smooth Transition Autoregressive Models – New Approaches to the Model Selection Problem , 2006 .

[13]  R. Thaler,et al.  Myopic Loss Aversion and the Equity Premium Puzzle , 1993 .

[14]  Michael W. Brandt,et al.  Variable Selection for Portfolio Choice , 2001 .

[15]  Dietmar Maringer,et al.  Global optimization of higher order moments in portfolio selection , 2009, J. Glob. Optim..

[16]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[17]  S. Page,et al.  Portfolio formation with higher moments and plausible utility , 2003 .

[18]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[19]  C. Gouriéroux,et al.  The Econometrics of Efficient Frontiers , 1998 .

[20]  Dietmar Maringer,et al.  Index tracking with constrained portfolios , 2007, Intell. Syst. Account. Finance Manag..

[21]  Mark Kritzman,et al.  Mean-Variance Versus Full-Scale Optimization: In and Out of Sample , 2006 .

[22]  Dietmar Maringer,et al.  Index tracking with constrained portfolios: Research Articles , 2007 .

[23]  C. Gouriéroux,et al.  The econometrics of efficient portfolios , 2005 .

[24]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[25]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .