آنالیز فنی و پیش بینی سود سهام: رویکرد هماهنگ شده Technical analysis and stock return predictability: An aligned approach

1. Introduction Changes in future excess stock returns affect many fundamental areas of finance, from portfolio theory to capital budgeting (e.g., Spiegel, 2008; Cochrane, 2011). Theoretically, the latent factors that drive the systematic variation of stock returns are not directly observable; therefore, researchers have proposed many predictors as proxies for these unobservable latent factors. Examples include valuation ratios, such as the dividend yield (Campbell and Viceira, 2002; Campbell and Yogo, 2006), the dividend payout ratio (Campbell and Shiller, 1988, 1998; Lamont, 1998), and book-to-market ratio (Kothari and Shanken, 1997; Pontiff and Schall, 1998), as well as nominal interest rates (Fama and Schwert, 1977; Ang and Bekaert, 2007), the inflation rate (Nelson, 1976; Campbell and Vuolteenaho, 2004), term spreads (Campbell, 1987; Fama and French, 1988), and stock market volatility (Guo, 2006). Welch and Goyal (2008), however, show that most of the economic predictors from the literature fail to generate consistently superior out-of-sample forecasts of the U.S. equity premium, and they attribute the weak predictability to their structural instability. Consequently, recent studies have devoted more attention to the application of technical indicators, a widely used strategy by market traders and investors for modern quantitative portfolio management and investment issues (e.g., Chincarini and Kim, 2006). Technical analysis, going back at least as early as Cowles (1933), uses past prices, trading volume, and other past available data to identify price trends believed to persist into the future.1 Brock, Lakonishok, and LeBaron (1992) and Lo, Mamaysky, and Wang (2000) find strong evidence of return predictability when using technical analysis, primarily based on a moving average strategy. Similarly, Neely et al. (2014) report that technical indicators and the popular macroeconomic variables from Welch and Goyal (2008) capture different types of information that is relevant for predicting aggregate market returns. Goh et al. (2013) also show that technical analysis can generate better performance in forecasting bond risk premiums than macroeconomic predictors. However, the predictability of technical indicators for aggregate stock market returns remains an open question. Indeed, Neely et al. (2014) show that only three of the Campbell and Thompson’s (2008) 2 ROS statistics for the 14 technical indicators are significantly greater than the historical average at the 5% level. Further, the forecasting power of the first principal component (PC) extracted from the technical indicators in out-of-sample periods is quite weak; the mean squared forecast error (MSFE) for the PC is marginally significantly less than the historical average MSFE at the 10% level according to the MSFE-adjusted statistics. Since out-of-sample forecasts are of great interest to practitioners for portfolio allocation and risk management, it is important to provide a method that can improve these forecasts substantially.