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.

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The energy-saving effect of financial development is directly related to the formulation and implementation of financial policies. Considering the inertial characteristics of energy consumption, this study tested the energy-saving effect of financial development and examined its heterogeneity in terms of low-carbon cleaning and policy change. The results were as follows: First, when energy consumption was at the lower quantile, as consumption increased, the promoting impact of financial development on energy consumption decreased. When energy consumption was at the upper quantile, as consumption increased, the restraining impact of financial development on energy consumption increased. Second, an increase in the quantile level showed that financial development exerted an increasingly stronger influence on promoting clean energy consumption. When non-clean energy consumption was at the upper quantile, financial development exerted an increasingly strong inhibitory effect on non-clean energy consumption. Third, before green credit policy changed, the energy-saving effect of financial development was not widespread and obvious. After green credit policy changed, the restraining impact of financial development on energy consumption increased with the level of consumption. Fourth, after green credit policy changed, compared with the increase of financial development toward promoting clean energy consumption, the inhibitory effect of financial development on non-clean energy consumption significantly improved relative to the second case.