Secular Mean Reversion and Long‐run Predictability of the Stock Market

Abstract

The empirical financial literature reports evidence of mean reversion in stock prices and the absence of out‐of‐sample return predictability over horizons shorter than 10 years. Anecdotal evidence suggests the presence of mean reversion in stock prices and return predictability over horizons longer than 10 years, but thus far, there is no empirical evidence confirming such anecdotal evidence. The goal of this paper is to fill this gap in the literature. Specifically, using 141 years of data, this paper begins by performing formal tests of the random walk hypothesis in the prices of the real S&P Composite Index over increasing time horizons of up to 40 years. Although our results cannot support the conventional wisdom that the stock market is safer for long‐term investors, our findings speak in favor of the mean reversion hypothesis. In particular, we find statistically significant in‐sample evidence that past 15‐17 year returns are able to predict the future 15‐17 year returns. This finding is robust to the choice of data source, deflator, and test statistic. The paper continues by investigating the out‐of‐sample performance of long‐horizon return forecasting based on the mean‐reverting model. These latter tests demonstrate that the forecast accuracy provided by the mean‐reverting model is statistically significantly better than the forecast accuracy provided by the naive historical‐mean model. Moreover, we show that the predictive ability of the mean‐reverting model is economically significant and translates into substantial performance gains.

Mean reversion in international markets: evidence from G.A.R.C.H. and half-life volatility models

“…Thus, econometricians try to calculate the actual potential of undervalued stock by using different techniques. The efficient market hypothesis is also described in terms of a random walk; it is very complicated to explore the undervalued company stock and its future performance (Mishra, 2017;Tripathy, 2017;Wang, Zhang, & Zhang, 2015;Zakamulin, 2016).…”

“…This proposition does not follow any specific pattern; therefore, the returns cannot be predicted. The random walk is an alternative terminology for the efficient market hypothesis; it is very complicated to explore the undervalued stock, and to predict whether the stock can perform more strongly in the future (Mishra, 2017;Tripathy, 2017;Wang et al, 2015;Zakamulin, 2016). According to Al-Hajieh (2017) and Dupernex (2007), this unpredictability no longer exists for the equity returns.…”

“…and generalised autoregressive conditional heteroskedasticity (G.A.R.C.H.) (1,1) models (Gulay & Emec, 2017;Zakamulin, 2016). For the estimation of the speed of mean reversion and volatility shocks, we have used a half-life model (Chaudhuri & Wu, 2003;Goudarzi, 2013;Kuttu, 2017;Mohammadi, 2017).…”

Speed of Mean Reversion: An Empirical Analysis of Kse, Lse and Ise Indices

“…On the other hand, those companies, which have high fundamental value, may be driven low. This causes companies, who have low fundamental values to raise capital easily as compared to companies with high fundamental values (e.g., Zakamulin 2016;Vveinhardt et al 2016;Engle, Morris 1991;etc. ).…”

“…Therefore, these investors use various techniques' that help them for identifying these undervalued stocks. However, according to efficient market hypothesis also known as the random walk, it is impossible to identify the securities that can outperform (e.g., Zakamulin 2016;Wang et al 2015;Kühnlenz 2014;etc. ).…”