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. 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.