Predicting stock market returns in the US: evidence from an average correlation approach

Abstract

Purpose This study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA. Design/methodology/approach This study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s). Findings The study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner. Research limitations/implications The methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models. Practical implications Market practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession. Social implications The finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets. Originality/value The study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

Does gold–platinum price ratio predict stock returns? International evidence

“…Predicting stock market returns has long been of interest to academics, policy makers and practitioners in finance. Numerous studies have examined the predictability of stock market returns around the world, yet the robustness of predictions remains unresolved (see Li et al. , 2020; 2022; Wen and Li, 2020).…”

“…Predicting stock market returns has long been of interest to academics, policy makers and practitioners in finance. Numerous studies have examined the predictability of stock market returns around the world, yet the robustness of predictions remains unresolved (see Li et al, 2020;2022;Wen and Li, 2020). The need to predict changes in stock returns in international financial market has become essentially important in the wake of growing financial globalization, as robust predictions enable investors to minimize their risks and losses.…”