We construct a daily Chinese Housing Market Sentiment Index by applying GPT-4o to Chinese news articles. Our method outperforms traditional models in several validation tests, including a test based on a suite of machine learning models. Applying this index to household-level data, we find that after monetary easing, an important group of homebuyers (who have a college degree and are aged between 30 and 50) in cities with more optimistic housing sentiment have lower responses in non-housing consumption, whereas for homebuyers in other age-education groups, such a pattern does not exist. This suggests that current monetary easing might be more effective in boosting non-housing consumption than in the past for China due to weaker crowding-out effects from pessimistic housing sentiment. The paper also highlights the need for complementary structural reforms to enhance monetary policy transmission in China, a lesson relevant for other similar countries. Methodologically, it offers a tool for monitoring housing sentiment and lays out some principles for applying generative AI models, adaptable to other studies globally.

The relationship between corporate Environmental, Social, and Governance (ESG) performance and its value has garnered increasing attention in recent times. However, the utilization of ESG scores by rating agencies, a critical intermediary in the linkage between ESG performance and value, presents challenges to ESG research and investment as a result of inherent subjectivity, hysteresis, and discrepant coverage. Fortunately, news can provide an objective, timely, and socially relevant perspective to augment prevailing rating frameworks and alleviate their shortcomings. This study endeavors to scrutinize the influence of ESG-related news on the Chinese stock market, to showcase its efficacy in supplementing the appraisal of ESG performance. The study's findings demonstrate that (1) the stock market is significantly impacted by ESGrelated news; (2) ESG-related news with different attributes (sentiments and sources) have notably diverse effects on the stock market; and (3) the heterogeneity among enterprises (industries and ownership structures) affects their ability to withstand ESGrelated news shocks. This study contributes novel insights to the comprehensive and objective assessment of corporate ESG performance and the management of its media image by providing a vantage point on ESG-related news.

This paper improves the expected return variable and the corresponding trend factor documented by Han, Zhou, and Zhu (2016) and reveals the incremental predictability of this novel expected return measure on stock returns in the Chinese stock market. Portfolio analyses and firm-level cross-sectional regressions indicate a significantly positive relation between the improved expected return and future returns. These results are robust to the short-, intermediate-, and long-term price trends and other derived expected returns. Our improved trend factor also outperforms all trend factors constructed by other expected returns. Additionally, we observe that lottery demand, capital states, return synchronicity, investor sentiment and information uncertainty can help explain the superior performance of the improved expected return measure in the Chinese stock market.

Developing an early warning model for company financial crises holds critical significance in robust risk management and ensuring the enduring stability of the capital market. Although the existing research has achieved rich results, the disadvantages of insufficient text information mining and poor model performance still exist. To alleviate the problem of insufficient text information mining, we collect related financial and annual report data from 820 listed companies in mainland China from 2018 to 2023 by using sophisticated web crawlers and advanced text sentiment analysis technologies and using missing value interpolation, standardization, and data balancing to build multi-source datasets of companies. Ranking the feature importance of multi-source data promotes understanding the formation of financial crises for companies. In the meantime, a novel Attention-based Fuzzy Neural Network (AFNN) was proposed to parse multi-source data to forecast financial crises among listed companies. Experimental results indicate that AFNN exhibits significantly improved performance compared to other advanced methods.

The general financial theory predicts a carbon premium, as brown stocks bear greater uncertainty under climate transition. However, a contrary green premium has been identified in China, as evidenced by the return spread between green and brown sectors. The aggregated climate transition sentiment, measured from news data using a large language model, explains 12%-33% of the variability in the anomalous alpha. This factor intensifies after China announced its national commitments. The sentiment-driven green premium is attributed to speculative trading by retail investors targeting green “concept stocks.” Additionally, the discussion highlights the advantages of large language models over lexicon-based sentiment analysis.

This paper shows that a New Year’s gambling preference of individual investors impacts prices and returns of assets with lottery features. January call options, especially the out-of-the-money calls, have higher retail demand and are the most expensive and actively traded. Lottery-type stocks outperform their counterparts in January but tend to underperform in other months. Retail sentiment is more bullish in lottery-type stocks in January than in other months. Furthermore, lottery-type Chinese stocks outperform in the Chinese New Year’s Month but not in January. This New Year effect pro- vides new insights into the broad phenomena related to the January effect.

Behavioral theories predict that firm valuation dispersion in the cross-section (‘‘dispersion’’) measures aggregate overpricing caused by investor overconfidence and should be negatively related to expected aggregate returns. This paper develops and tests these hypotheses. Consistent with the model predic- tions, I find that measures of dispersion are positively related to aggregate valuations, trading volume, idiosyncratic volatility, past market returns, and current and future investor sentiment indexes. Disper- sion is a strong negative predictor of subsequent short- and long-term market excess returns. Market beta is positively related to stock returns when the beginning-of-period dispersion is low and this rela- tionship reverses when initial dispersion is high. A simple forecast model based on dispersion signifi- cantly outperforms a naive model based on historical equity premium in out-of-sample tests and the predictability is stronger in economic downturns.

Although sentiment-driven investors are believed to play an important role in the Chinese stock market, there are very few sentiment measures at the individual stock level based on their trading activities. Due to the unique “T+1” trading rule in China, the low overnight return of stocks reflects intensified trading activities from short-term speculators. Therefore, we construct a sentiment measure for individual stocks based on the close-to-open return (CTO). We find that CTO positively predicts future stock returns in the cross-section, supporting the idea that low CTO, as an indicator of sentiment-driven excess demand, leads to lower subsequent returns. This finding is not driven by firm-specific news and alternative explanations based on risks, investor attention, or investor underreaction. Further analyses suggest that investors overpay for low-CTO stocks because of their inherent preference for this type of stock.

Owing to the liberalisation of financial markets, the impact of international capital flows on the Chinese stock market has become substantial. This study investigates the effects of economic policy uncertainty (EPU), geopolitical risk (GPR), consumer sentiment (CCI), macroeconomic fundamentals (MECI), and money supply (M2) on the correlations between the stock and exchange rate markets. The negative correlation between these two markets has become more pronounced in recent years. Moreover, EPU, GPR, CCI, and MECI negatively impact long-term stock-exchange rate correlations, while M2 has a positive impact. Portfolios of stock-exchange rates effectively reduce risk, especially when considering structural breaks.

The rise of automated trading systems has made stock trading more accessible and convenient, reducing the link between traditional illiquidity measures and stock returns. However, empirical data in China’s stock market shows conflicting results. We find a significantly positive correlation between intraday illiquidity and future returns in China’s stock market. We offer that the pricing ability of this intraday illiquidity originates from the correlation between trading activity and intraday return. This finding provides compelling out-of-sample evidence for the debate regarding the pricing of the Amihud (2002) measure in the U.S. market. Additionally, we create an intradayreturn illiquidity factor that outperforms Liu, Stambaugh, and Yuan (2019) sentiment factors in China’s stock market.