We have developed two novel salience factors — PMOR and PMOV based on the stock’s salient return and salient trading volume (as proposed by Cosemans and Frehen, 2021, and Sun et al., 2023). Notably, these factors cannot be accounted for by existing factor models in China. When we integrate the salience trading volume factor — PMOV into Liu et al. (2019)’s Chinese three-factor model, the resulting four-factor model outperforms other models including the Chinese four-factor model in explaining 33 significant anomalies in China.

Deep learning technology is rapidly adopted in financial market settings. Using a large data set from the Chinese stock market, we propose a return-risk trade-off strategy via a new transformer model. The empirical findings show that these updates, such as the self-attention mechanism in technology, can improve the use of time-series information related to returns and volatility, increase predictability, and capture more economic gains than other nonlinear models, such as LSTM. Our model employs Shapley additive explanations (SHAP) to measure the “economic feature importance” and tabulates the different important features in the prediction process. Finally, we document several economic explanations for the TF model. This paper sheds light on the burgeoning field on asset allocation in the age of big data.

Using land transaction data in China, we investigate whether and how political connections penetrate through headquarter-subsidiary relationships. The results show that even though the headquarters of politically connected listed firms pay comparable land prices as other firms, their subsidiaries pay 12.1-13.2% less. The price discount, driven by corruption, is exacerbated when the land is for commercial or residential use and is disposed of through informationally opaque supply methods. The anti-corruption campaign has successfully mitigated such price distortions. Our findings also show that better legal protection and private sector development are crucial for fair markets.

This paper examines the effect of political favoritism on economic inequality in the short run and political polarization in the long run. We exploit the sudden death of an authoritarian leader – President Chiang Ching-Kuo of Taiwan – in 1988 to generate plausibly exogenous variation in partiality. We find that Chiang’s nationalist regime conducted political favoritism broadly toward political immigrants via cronyism (allocating public sector positions) and also differentially toward specific subgroups of political immigrants via wage discrimination (offering higher wages to these subgroups within the public sector). Favoritism led to a 7.2 percent immigrant wage premium, which accounted for nearly three quarters of the immigrant-native wage gap at the time. This in turn propelled overall income inequality by 4.5 percent. Moreover, political favoritism breeds political polarization in the long run by pulling apart the political views of immigrants and natives. Compared with natives, immigrants who were exposed to favoritism tend to adopt political positions that are aligned with the nationalist party today: they are more likely to support unification with China, and are more inclined to trust the mainland Chinese government and its citizens. Exposed immigrant (native) swing voters are also more (less) likely to vote for the nationalist party today.

We provide the first systematic analysis of the stock return lead-lag effect among firms connected through shared analyst coverage in China’s A-share markets. We measure the shared analysts-weighted average returns of connected firms (CF) and show that CF return is a significant positive predictor of future returns of the focal firms in the following one to 12 months. The CF-based long-short portfolio earns an abnormal return of 10% to 12% per year. The effect is robust to controls for the industry and geographic momentum effects. Further evidence shows that the CF momentum spillover effect is stronger when the focal firm shares more analysts with connected firms, is covered by more non-star analysts or analysts with lower levels of education, or is held by more stress-resistant institutional investors. Our findings contribute to the cross-asset momentum literature by documenting a new, strong, and long-lasting momentum spillover effect in the Chinese stock markets.

This paper selects Chinese A-share listed manufacturing companies from 2011 to 2020 as a research sample and analyzes the impact of economic policy uncertainty on business performance. We find that economic policy uncertainty is detrimental to the improvement of business performance; service transformation can significantly offset the adverse effect of economic policy uncertainty on business performance, and this positive effect will increase with the depth of service transformation, Furthermore, embedded service transformation is more effective than mixed service transformation in weaking the adverse effects of economic policy uncertainty on business performance. The results of the heterogeneity test show that the negative relationship between economic policy uncertainty and firm business performance, as well as the positive effect of servitization transformation, is more noticeable in non-state-owned enterprises and low sale growth firms. The results are robust after various specifications of variables, possible endogeneity issues, and more control variables are considered.

This paper examines the impact of the carbon emissions trading scheme (ETS) on directed technological change in the context of Chinese pilot schemes. We focus on firms’ heterogeneity in driving innovation and explore policy variations across pilots. Using a matched difference-in-differences design with a zero-inflated Poisson model, we find that the low-carbon innovation is driven by firms at the intensive margin. On average, a firm files 0.16 additional low-carbon patents annually at the intensive margin. In addition, when looking across pilots, the effect on low-carbon innovation is significant in two pilots, Beijing and Shanghai. We further find that, when looking at firms with different productivity levels measured by output per worker, the pilot ETS encourages low-carbon innovation at the intensive margin but reduces entry into low-carbon innovation at the extensive margin for the more productive firms. Our results suggest that innovation inertia matters, and future policies should encourage smaller firms covered by ETS to start innovation in low-carbon technologies.

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