Irrational fads, short‐term memory emulation, and asset predictability

Abstract

a b s t r a c tOpponents of the efficient markets hypothesis argue that predictability reflects the psychological factors and "fads" of irrational investors in a speculative market. In that, conventional time series analysis often fails to give an accurate forecast for financial processes due to inherent noise patterns, fat tails, and nonlinear components. A recent stream of literature on behavioral finance has revealed that boundedly rational agents using simple rules of thumb for their decisions under uncertainty provides a more realistic description of human behavior than perfect rationality with optimal decision rules. Consequently, the application of technical analysis in trading could produce high returns. Machine learning techniques have been employed in economic systems in modeling nonlinearities and simulating human behavior. In this study, we expand the literature that evaluates return sign forecasting ability by introducing a recurrent neural network approach that combines heuristic learning and short-term memory emulation, thus mimicking the decision-making process of boundedly rational agents. We investigate the relative direction-of-change predictability of the neural network structure implied by the Lee-White-Granger test as well as compare it to other well-established models for the DJIA index. Moreover, we examine the relationship between stock return volatility and returns. Overall, the proposed model presents high profitability, in particular during "bear" market periods.

A Rational Finance Explanation of the Stock Predictability Puzzle

“…Modeling and analyzing the stock return predictability is crucial for stock and risk 1See Kumar and Chaturvedul (2013). 2See for example, Kandel and Stambaugh (1996), Neely and Weller (2000), Malkiel (2003), Barberis and Thaler (2003), Shiller (2003), Avramov (2003), Wachter and Warusawitharana (2009), Pesaran (2010), Zhou (2010), andBekiros (2013). The models that have been used are (1) Conditional Capital Asset Pricing Model, (2) vector autoregressive models, (3) Bayesian statistical factor analysis, (4) posterior moments of the predictable regression coefficients, (5) posterior odds, (6) the information in stock prices, (7) business cycles effects, (8) stock predictability of future returns from initial dividend yields, (9) firm characteristics as stock return predictors, (10) anomalies, (11) predictive power of scaled-price ratios such as book-to-market and earnings-toprice, forward spread, and short rate, (12) variance risk premia and variance spillovers, (13) momentum, market memory and reversals, and (14) early announcements and others.…”

A rational finance explanation of the stock predictability puzzle

“…Finally, we showed that the difference between the implied variance and realized variance, which we called the variance risk premium, is an informative measure for predicting the market, in contrast to other volatility measures. ii See, for example, Kandel and Stambaugh (1996), Neely and Weller (2000), Malkiel (2003), Barberis and Thaler (2003), Shiller (2003), Avramov (2003), Wachter and Warusawitharana (2009), Pesaran (2010), Zhou (2010), andBekiros (2013). The models that have been used are (1) Conditional Capital Asset Pricing Model, (2) vector autoregressive models, (3) Bayesian statistical factor analysis, (4) posterior moments of the predictable regression coefficients, (5) posterior odds, (6) the information in stock prices, (7) business cycle effects, (8) stock predictability of future returns from initial dividend yields, (9) firm characteristics as stock return predictors, (10) anomalies, (11) predictive power of scaledprice ratios such as book-to-market and earnings-to-price, forward spread, and short rate, (12) variance risk premia and variance spillovers, (13) momentum, market memory, and reversals, and ( 14) early announcements, and others.…”

Behavioral Finance - Asset Prices Predictability, Equity Premium Puzzle, Volatility Puzzle: The Rational Finance Approach

“…3 See for example, Kandel and Stambaugh (1996), Neely and Weller (2000), Malkiel B.G. (2003), Barberis and Thaler (2003) , , Avramov (2004), Wachter and Warusawitharana (2009) , Pesaran (2010), Zhou (2010), Bekiros (2013) predictability 4 : (i) sentiment; (ii) overconfidence; (iii) optimism and wishful thinking; (iv) conservatism; euphoria and gloom; (v) self-deception; (vi) cursedness; (vii) belief perseverance;…”