Predictability of China’s Stock Market Returns Based on Combination of Distribution Forecasting Models

Abstract

No consensus exists in the literature on whether stock prices can be predicted, with most existing studies employing point forecasting to predict returns. By contrast, this study adopts the new perspective of distribution forecasting to investigate the predictability of the stock market using the model combination strategy. Specifically, the Shanghai Composite Index and the Shenzhen Component Index are selected as research objects. Seven models – GARCH-norm, GARCH-sstd, EGARCH-sstd, EGARCH-sstd-M, one-component Beta-t-EGARCH, two-component Beta-t-EGARCH, and the EWMA-based nonparametric model – are employed to perform distribution forecasting of the returns. The results of out-of-sample forecasting evaluation show that none of the individual models is “qualified” in terms of predictive power. Therefore, three combinations of individual models were constructed: equal weight combination, log-likelihood score combination, and continuous ranked probability score combination. The latter two combinations were found to always have significant directional predictability and excess profitability, which indicates that the two combined models may be closer to the real data generation process; from the perspective of economic evaluation, they may have a predictive effect on the conditional return distribution in China’s stock market.

Daily return distribution forecast incorporating intraday high frequency information in China’s stock market

“…Furthermore, the Continuous Ranking Probability Score (CRPS) proposed by Gneiting et al (2007), a scoring metric that considers sharpness, is also commonly used in the literature. Referring to Yao et al (2020), this article adopts three scoring indicators: the number of Bayesian winners, average log score, and CRPS. Table 4 presents the relevant results.…”

“…As shown above, the marginal calibrations of different models are inconsistent, especially for the nonparametric models, which are different from the parametric models. Following Yao et al (2020), this study considers three linear dynamic combination strategies: equal weight combination (EW), logarithmic score combination (SW), and CRPS combination (CW). Such combinations are all 'single-period' weighting, that is, the weight is calculated based on the forecast effect of one period only.…”

Are technical indicators helpful to investors in china’s stock market? A study based on some distribution forecast models and their combinations

“…Three score evaluations: the times of Bayesian winner, average logarithmic score, and average CRPS were performed according to Yao et al (2020) (see Table 5). LASSO-EGARCH is the best Bayesian winner.…”

“…We then conduct an economic evaluation. Referring to Yao et al (2020), three combinations were considered as equal weight combination (EW), logarithmic score combination (SW), and CRPS combination (CW). Two-point forecasts are derived from the forecasted distribution of the returns.…”

“…Otherwise, when the forecast direction is non-negative, continue to be short if there is no asset at hand, and sell if there is a unit of the asset at hand. Similarly to Yao et al (2020), the ratio of the strategic mean transaction return to the ideal mean transaction return is calculated. This is the strategy-ideal ratio.…”