Generalized DEA model of fundamental analysis and its application to portfolio optimization

Abstract Fundamental analysis is used in asset selection for equity portfolio management. In this paper, a generalized data envelopment analysis (DEA) model is developed to analyze a firm’s financial statements over time in order to determine a relative financial strength indicator (RFSI) that is predictive of firm’s stock price returns. RFSI is based on maximizing the correlation between the DEA-based score of financial strength and the stock market performance. This maximization involves a difficult binary nonlinear program that requires iterative re-configuration of parameters of financial statements as inputs and outputs. We utilize a two-step heuristic algorithm that combines random sampling and local search optimization. The proposed approach is tested with 230 firms from various US technology-industries to determine optimized RFSI indicators for stock selection. Then, those selected stocks are used within portfolio optimization models to demonstrate the usefulness of the scheme for portfolio risk management.

[1]  R. Sickles,et al.  The Relationship Between Stock Market Returns and Technical Efficiency Innovations: Evidence from the US Airline Industry , 1998 .

[2]  Emmanuel Thanassoulis,et al.  A Comparison of Regression Analysis and Data Envelopment Analysis as Alternative Methods for Performance Assessments , 1993 .

[3]  Joe Zhu,et al.  Hedge fund performance appraisal using data envelopment analysis , 2005, Eur. J. Oper. Res..

[4]  Mark V. Cannice,et al.  The value-relevance of financial and non-financial information for Internet companies , 2002 .

[5]  Tong-Seng Quah,et al.  Improving returns on stock investment through neural network selection , 1999 .

[6]  Joseph C. Paradi,et al.  Financial performance analysis of Ontario (Canada) Credit Unions: An application of DEA in the regulatory environment , 2002, Eur. J. Oper. Res..

[7]  S. Penman,et al.  FINANCIAL STATEMENT ANALYSIS AND THE PREDICTION OF STOCK RETURNS , 1989 .

[8]  R. Färe,et al.  The measurement of efficiency of production , 1985 .

[9]  Torbjörn Larsson,et al.  Global Optimality Conditions for Discrete and Nonconvex Optimization - With Applications to Lagrangian Heuristics and Column Generation , 2004, Oper. Res..

[10]  Russell J. Lundholm,et al.  The Predictive Value of Expenses Excluded from 'Pro Forma' Earnings , 2002 .

[11]  N. C. P. Edirisinghe,et al.  Integrated risk control using stochastic programming ALM models for money management , 2008 .

[12]  Joseph D. Piotroski.  Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers , 2000 .

[13]  Panos M. Pardalos,et al.  Introduction to Global Optimization , 2000, Introduction to Global Optimization.

[14]  William T. Ziemba,et al.  Handbook of Asset and Liability Management - Set , 2007 .

[15]  M. S. Bazaraa,et al.  Nonlinear Programming , 1979 .

[16]  Angelos Kanas,et al.  Neural network linear forecasts for stock returns , 2001 .

[17]  X. Zhang,et al.  Portfolio selection under DEA-based relative financial strength indicators: case of US industries , 2008, J. Oper. Res. Soc..

[18]  Joe Zhu,et al.  Dual-role factors in data envelopment analysis , 2006 .

[19]  Jesús T. Pastor,et al.  Units invariant and translation invariant DEA models , 1995, Oper. Res. Lett..

[20]  Nikolaos V. Sahinidis,et al.  Global optimization of mixed-integer nonlinear programs: A theoretical and computational study , 2004, Math. Program..

[21]  Emmanuel Thanassoulis,et al.  Negative data in DEA: a directional distance approach applied to bank branches , 2004, J. Oper. Res. Soc..

[22]  Boaz Golany,et al.  Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions , 1985 .

[23]  A. Tamhane,et al.  Statistics and Data Analysis: From Elementary to Intermediate , 1999 .

[24]  William F. Bowlin,et al.  Financial analysis of civil reserve air fleet participants using data envelopment analysis , 2004, Eur. J. Oper. Res..

[25]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[26]  J. Beasley.  Determining Teaching and Research Efficiencies , 1995 .

[27]  Subhash C. Ray,et al.  Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research , 2004 .

[28]  W. F. Bowlin,et al.  An analysis of the financial performance of defense business segments using data envelopment analysis , 1999 .

[29]  Quey-Jen Yeh.  The Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation , 1996 .

[30]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[31]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .