An integrated Fuzzy-ANP model for value investing

As today's financial market is highly dynamics and comprised of complex factors, few robust models are available for supporting value investors and most of the investment decisions are mainly relied on the judgments of senior investment experts. Among accounting-based fundamental analysis, F-Score system has been a widely accepted model for value investing [1]. F-Score system proposed nine fundamental signals to measure three aspects of a firm's financial conditions: (1) profitability, (2) financial leverage/ liquidity, and (3) operating efficiency. However, the manner which is based on historical financial information would face the difficulty of acquiring implicit knowledge of senior investment experts and accumulating knowledge. To improve the applicability of F-Score system, this research integrates fuzzy set theory and analytic network process (ANP) to propose an innovative model for distinguishing strong financial prospect stocks among high book-to-market (B/M) stocks. Five firms ranked top 20% B/M ratio were benchmarked to test the Fuzzy-ANP model. The practicability of the proposed model is verified by real stocks' data collected from April 2008 to December 2009 in Taiwan.

[1]  Yi-Hsuan Chen,et al.  A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard , 2009, Expert Syst. Appl..

[2]  Ozden Ustun,et al.  Analytic network process and multi-period goal programming integration in purchasing decisions , 2009, Comput. Ind. Eng..

[3]  Andrew E. Kern,et al.  Fundamental Value Investors: Characteristics and Performance , 2008 .

[4]  T. Saaty.  The Analytic Network Process , 2001 .

[5]  C. Clubb,et al.  The Usefulness of Book-to-Market and ROE Expectations for Explaining UK Stock Returns , 2007 .

[6]  Wei Lo,et al.  Bargaining Strategies for Construction Joint Ventures by Fuzzy Logic , 2006, JCIS.

[7]  M. Bohanec,et al.  The Analytic Hierarchy Process , 2004 .

[8]  Min-Yuan Cheng,et al.  Hybrid use of AI techniques in developing construction management tools , 2003 .

[9]  Soung Hie Kim,et al.  Using analytic network process and goal programming for interdependent information system project selection , 2000, Comput. Oper. Res..

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

[11]  Miroslaw J. Skibniewski,et al.  A neuro-fuzzy computational approach to constructability knowledge acquisition for construction technology evaluation , 1999 .

[12]  J. Vu.  Value versus Growth: The International Evidence , 1999 .

[13]  Charles M. C. Lee,et al.  Accounting valuation, market expectation, and cross-sectional stock returns , 1998 .

[14]  F. T. Magiera.  Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings , 1997 .

[15]  Brian J. Bushee,et al.  Fundamental Analysis Future Earnings, and Stock Prices , 1997 .

[16]  Richard G. Sloan.  Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings , 1998 .

[17]  E. Fama,et al.  Multifactor Explanations of Asset Pricing Anomalies , 1996 .

[18]  Josef Lakonishok,et al.  Contrarian investment, extrapolation, and risk / 93-128 , 1993 .

[19]  B. Lev,et al.  Fundamental Information Analysis , 1993 .

[20]  E. Fama,et al.  The Cross‐Section of Expected Stock Returns , 1992 .

[21]  Stephen H. Penman,et al.  Accounting Measurement, Price-Earnings Ratio, and the Information Content of Security Prices , 1989 .

[22]  T. Saaty,et al.  Dependence and independence: From linear hierarchies to nonlinear networks , 1986 .

[23]  Ronald J. Lanstein,et al.  Persuasive evidence of market inefficiency , 1985 .

[24]  H. Hinderer.  How accounting measures. , 1962, Hospital progress.