Stock return predictability and model instability: Evidence from mainland China and Hong Kong

Abstract

Highlights  This paper examines the predictability of the Shanghai Composite, Shenzhen Composite and the Hang Seng China Enterprise index returns, with emphasis on whether considering structural breaks in model parameters improves the stock return predictability.  Results are important for investors who are interested in investing in Mainland China and Hong Kong stock markets.  Results indicate higher linear stock return predictability for the Hong Kong market than for the Chinese markets.  Results differ when model instability is taken into consideration: the Shenzhen market is detected with structural breaks and its predictability varies across different subsamples defined by the breaks.

COVID-19 and instability of stock market performance: evidence from the U.S.

“…Together with the evidence of the BIC and the SM for the number of true breaks, we conclude with one-break prediction models for both S&P 500 and DJIA returns. These findings reinforces those in prior studies that predictive regression models are characterized by structural instability (e.g., Rapach and Wohar 2006 ; Paye and Timmermann 2006 ; Hong et al 2018 ).…”

“…More recent studies extend the prior research to allow for multiple breaks, unit root dynamics, heteroskedasticity and serial correlation (e.g., Bai and Perron 1997 , 1998 , 2003 ; Elliott and Muller 2004 ; Lee et al 2021 ). Empirical investigation based on those recent econometric techniques includes Paye and Timmermann ( 2006 ), Rapach and Wohar ( 2006 ), and Hong et al ( 2018 ). In this paper, we consider the methodology of Bai and Perron ( 1998 , 2003 ) because it allows us to determine the confidence intervals for the timing of break occurrence as well as the coefficients around the breakpoints.…”

“…In order to establish reasonable regression models for stock return predictions, we employ the widely-adopted stepwise methodology embedded with Bayesian Information Criterion (BIC) to select predictor variables (e.g., Hsu et al 2010 ; Hong et al 2018 ). It should be noted that we aim to establish regression models that can be used to predict stock returns rather than choose the best statistical models for predictions.…”

“…This paper thus provides a comprehensive analysis on the association between COVID-19 and the instability of the U.S. stock market performance (including both return predictability and price volatility), which serves the interests of investors when making investment decisions during difficult times (Kou et al 2014 , 2021 ). Facts in the past prove that crises induce risks but also create investment opportunities: stock return predictability and price volatility substantially increase in bad times (e.g., Schwert 1989 ; Cujean and Hasler 2017 ; Hong et al 2018 ; Liu et al 2020a ; Narayan 2020a , 2020b ).…”

Does macroeconomic uncertainty really matter in predicting stock market behavior? A comparative study on China and USA

“…Moreover, the results of GARCH models with dummy variables also show that DUM 1 and DUM 2 are significant for the SSEC return in all sets of GARCH models which means that the Asian financial crisis and the global financial crisis influence the stock market return significantly. For China, Hong et al (2018) have also found structural breaks in the Shenzhen stock market and have documented significant improvement in the forecasting of stock market return. For the macroeconomic variables, the impact of global financial crisis is more stern as compared to the Asian financial crisis.…”