This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

This study identifies an incentive-compatible mechanism to foster individual environmental engagement. Utilizing a dataset comprising 100,000 randomly selected users of Ant Forest—a prominent personal carbon accounting platform embedded within Alipay, China's leading BigTech super-app—we provide causal evidence that individuals strategically engage in eco-friendly behaviors to enhance their credit limits, particularly when approaching borrowing constraints. These behaviors not only illustrate the green nudging effect of BigTech but also generate value for the platform by leveraging individual green actions as soft information, thereby improving the efficiency of credit allocation. Using a structural model, we estimate an annual green value of 427.52 million US dollars generated by linking personal carbon accounting with BigTech credit. We also show that the incentive-based mechanism surpasses green mandates and subsidies in improving consumer welfare and overall societal welfare. Our findings highlight the role of an incentive-aligned approach, such as integrating personal carbon accounts into credit reporting frameworks, in addressing environmental challenges.

We construct firm-level indicators to capture the engagement in the Belt and Road Initiative (BRI, henceforth) via textual analysis. We find that higher firm engagement in BRI predicts higher stock returns in the subsequent 12 months. The top 10% high-BRI firms have 12.42% higher annual returns than bottom 10% low-BRI firms in China A-Share market. Additionally, two fundamental channels of increased earnings and reduced liabilities explain the higher expected returns of high-BRI firms. Furthermore, we reveal that the phenomenon is more pronounced among non-state-owned enterprises. For large-cap firms, BR Report is a more effective indicator for predicting future stock returns, while BR Beta performs better for small-cap firms. These findings contribute to the measurement of firm engagement in BRI and its impact on the 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.

This article proposes a semi-martingale approximation to a fractional Lévy process that is capable of capturing long and short memory in the stochastic process together with fat tails. The authors use the semi-martingale process in option pricing and empirically compare its performance to other option pricing models, including a stochastic volatility Lévy process. They contribute to the empirical literature by being the first to report the implied Hurst index computed from observed option prices using the Lévy process model. Calibrating the implied Hurst index of S&P 500 option prices in a period that covers the 2008 financial crisis, they find that the risk-neutral measure is characterized by a short memory in turbulent markets and a long memory in calm markets.

Many corporate executives believe blockchain technology is broadly scalable and will achieve mainstream adoption, yet there is little evidence of significant shareholder value creation associated with corporate adoption of blockchain technology. We collect a broad sample of firms that invest in blockchain technology and examine the stock price reaction to the “first” public revelation of this news. Initial reac- tions average close to +13% and are followed by reversals over the next 3 months. However, we report a striking differ- ence based on the credibility of the investment. Blockchain investments that are at an advanced stage or are con- firmed in subsequent financial statements are associated with higher initial reactions and little or no reversal. The results suggest that credible corporate strategies involving blockchain technology are viewed favorably by investors.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

It remains unclear whether state shareholding (SS) truly enhances firms’ fulfillment of their corporate social responsibility (CSR) or merely motivates them to strategically release “enhanced” CSR reports. Utilizing the reform that permits state–owned equity to participate in privately–owned enterprises (POEs) in China, we find that the participation of SS enhances POEs’ access to resources and alleviates their needs for legitimacy, leading to disparities in CSR disclosure and substantive CSR activities for POEs, consistent with the notion of greenwashing. The greenwashing behavior is particularly pronounced in the presence of large state-owned shareholder and when CSR disclosure is compulsory.

This study examines the influence of ESG report textual similarity on stock price synchronicity within the Chinese A-share market. Using advanced textual analysis methods, including TF-IDF and LDA, we measure the textual similarity of ESG reports among industry peers. Our results reveal a positive association between ESG report textual similarity and stock price synchronicity, suggesting that ESG reports with high textual resemblance may not convey distinct market information. This research underscores the importance of textual distinctiveness in ESG reports and offers a fresh perspective on the role of non-financial information, particularly related to CSR, in stock pricing dynamics. By emphasizing the significance of ESG report textual distinctiveness, we contribute to the broader discourse on ESG disclosure behaviors and their implications for capital market efficiency.

Exploiting a unique market-oriented media reform initiated in 1996 in China, we investigate the role of media environment in affecting firm behaviour. We find robust evidence that market-oriented media environment is conductive to firm innovation, with the reform promoting patent quantity and quality substantially. The effect is more pronounced for firms with higher information asymmetry. Matching firm data with 1.3 million news reports, we find the market-oriented media reform significantly improves the criticalness and unbiasedness of news coverage and shapes an innovation-friendly environment. Our findings highlight economic outcomes of relaxing media control and underline substantial gains from deepening the reform.