In this paper, we investigate whether those enterprises adopting digital finance exhibit higher values. On the basis of the constructed fintech-related lexicon developed by the machine learning-based Word2Vec model, we employ the frequency of fintech-related words (phrases) in the management discussion sections of annual reports as a proxy variable for the degree to which enterprises apply digital finance. We utilize panel data regression and mediation models based on data of Chinese A-share listed companies from 2016 to 2022 and explore the impact of this degree of digital finance application on enterprise value. We find that the degree to which enterprises apply digital finance elevates their values. The in-depth integration of digital technology and finance directly enhances enterprise value by reducing financing costs. Additionally, the effects are more evident among small-scale firms and enterprises located in regions with lower marketization levels. However, in the face of the impact of the COVID-19 pandemic, the positive effects on enterprises are relatively low.

This study explores how Chinese mutual fund managers’ degrees of disagreement (DOD) on stock market returns affect investor capital allocation decisions using a novel textbased measure of expectations in fund disclosures. In the time series, the DOD negatively predicts market returns. Cross-sectional results show that investors correctly perceive the DOD as an overpricing signal and discount fund performance accordingly. Flow-performance sensitivity (FPS) is diminished during high dispersion periods. The effect is stronger for outperforming funds and funds with substantial investments in bubble and high-beta stocks, but weaker for skilled funds. We also discuss ffnancial sophistication of investors and provide evidence that our results are not contingent upon such sophistication.

This paper selects 346 green bonds issued in China from 2016 to 2021 as the sample, and the Propensity Score Matching (PSM) method is employed to confirm the existence of ‘green premium’ in the Chinese bond market. On this basis, data on internet media sentiment and print media sentiment are collected from ‘Sina Weibo’ and ‘China Important Newspaper Full Text Database’ by both Web Crawler Technology and Textual Analysis Methods to explore the impact and the mechanism of heterogeneous media sentiments on the ‘green premium’. The results show that both the optimism of internet media and print media can significantly promote the ‘green premium’ of green bonds, and the influence of print media sentiment on the ‘green premium’ is greater than that of internet media sentiment. In addition, the Bootstrap method verifies the mediating effect of print media sentiment in the influence of internet media sentiment on ‘green premium’, indicating that print media sentiment is an important transmission path. Moreover, the results of the heterogeneity test show that the more optimistic the media is, the more significant the ‘green premium’ effect is in the regions with higher institutional environments and financial subsidy policies. The ‘green premium’ of green bonds is most pronounced for higher levels of institutional environment and green bond preferential policies.

Fund managers’ ability to evaluate risk has important implications for their portfolio management and performance. We use a state-of-the-art deep learning model to measure fund managers’ forward-looking risk assessments from their narrative discussions. We validate that managers’ negative (positive) risk assessments lead to subsequent decreases (increases) in their portfolio risk-taking. However, only managers who identify negative risk generate superior risk-adjusted returns and higher Sharpe ratios, and have better intraquarter trading skills, suggesting that cautious, skilled managers are less subject to overconfidence biases. interestingly, only sophisticated investors respond to the narrative-based risk assessment measure, consistent with limited attention by retail investors.

We study how professional fund managers’ growth expectations affect the actions they take with respect to equity investment and in turn the effects on prices. Using novel data on China’s mutual fund managers’growth expectations, we show that pessimistic managers decrease equity allocations and shift away from more-cyclical stocks. We identify a strong short-run causal effect of growth expectations on stock returns, despite statistically significant delays in price discovery from short-sale constraints. Finally, we find that an earnings-based measure of price informativeness is increasing in fund investment.

Referring to the newspaper textual analysis method by Baker et al. (2016), this study constructs a monthly Chinese Real Estate Policy Uncertainty (REPU) index from 2001 to 2018. The index increases significantly near the promulgation of major policies. We also conduct evaluation of the index with the vector autoregression (VAR) model, which reveals that the rise of REPU indicates the decline in the growth rate of commodity housing development investment, sales area, and real estate industry added value. The REPU index is helpful to expand the understanding of policy uncertainty, and the accurate measurement of REPU is the basis for further research of its impact on China's real estate market.

In this paper, we argue that investor sentiment is a more direct determinant for asset pricing than information, thus we propose the Sentiment Efficient Markets Hypothesis (S-EMH), complementary to the traditional Efficient Market Hypothesis (EMH), to provide a powerful instrument to interpret financial facts and anomalies inconsistent with the traditional EMH. Besides the theoretical argument, we also verify the hypothesis with a brand-new systematic index of investor sentiment, Gubasenti, derived from textual analysis on more than 200 million posts from an online Chinese stock forum. The examinations are implemented in both market-level and firm-level, and results show that investor sentiment has a significant impact on asset pricing in both levels. It demonstrates the proposed hypothesis.