Forecasting the Equity Risk Premium in the Korean Stock Market: A Factor Analysis Approach
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Purpose: This article investigates stock return predictability in the Korean stock market using the methodology of dynamic factor analysis. Design/methodology/approach: This article collects monthly data on the equity risk premium on the KOSPI and twelve financial and macroeconomic variables spanning from October 2000 to December 2020 and evaluates the forecasting performance of the dynamic factor predictive regression model by comparing in-sample and out-of-sample predictability with those of individual predictors. Findings: The article finds that the dynamic factor predictive regression exhibits statistically and economically significant in-sample predictability for the future equity risk premium for the KOSPI, as strongly as the best individual predictor can do. Also, the dynamic factor approach can outperform the benchmark historical average in out-of-sample predictability. The detailed analysis of the diffusion indexes reveals that each factor captures different information from various financial and macroeconomic variables relevant for return prediction and the diffusion indexes can deliver better forecasts of the future equity risk premium. Research limitations/implications: There exist different regression methods to combine forecasts comparable to the dynamic factor predictive model such as the forecast combination method by Rapach et al. (2010) and the bagging method by Inoue and Kilian (2008) and Jordan et al. (2017). The study proposes to compare the performance of these models with that of the dynamic factor predictive model in the Korean stock market as future research. Originality/value: The article is the first attempt to apply the dynamic factor predictive regression model to a large set of financial and macroeconomic data in Korea and evaluate its in-sample and out-of-sample predictability in comparison to those of individual predictive variables.
Abstract
Purpose: This article investigates stock return predictability in the Korean stock market using the methodology of dynamic factor analysis. Design/methodology/approach: This article collects monthly data on the equity risk premium on the KOSPI and twelve financial and macroeconomic variables spanning from October 2000 to December 2020 and evaluates the forecasting performance of the dynamic factor predictive regression model by comparing in-sample and out-of-sample predictability with those of individual predictors. Findings: The article finds that the dynamic factor predictive regression exhibits statistically and economically significant in-sample predictability for the future equity risk premium for the KOSPI, as strongly as the best individual predictor can do. Also, the dynamic factor approach can outperform the benchmark historical average in out-of-sample predictability. The detailed analysis of the diffusion indexes reveals that each factor captures different information from various financial and macroeconomic variables relevant for return prediction and the diffusion indexes can deliver better forecasts of the future equity risk premium. Research limitations/implications: There exist different regression methods to combine forecasts comparable to the dynamic factor predictive model such as the forecast combination method by Rapach et al. (2010) and the bagging method by Inoue and Kilian (2008) and Jordan et al. (2017). The study proposes to compare the performance of these models with that of the dynamic factor predictive model in the Korean stock market as future research. Originality/value: The article is the first attempt to apply the dynamic factor predictive regression model to a large set of financial and macroeconomic data in Korea and evaluate its in-sample and out-of-sample predictability in comparison to those of individual predictive variables.
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