This study reconsiders the timing of the equity risk factors by using the flexible neural networks specified for image recognition to determine the timing weights. The performance of each factor is visualized to be standardized price and volatility charts and `learned' by flexible image recognition methods with timing weights as outputs. The performance of all groups of factors can be significantly improved by using these ``deep learning--based'' timing weights. In addition, visualizing the volatility of factors and using deep learning methods to predict volatility can significantly improve the performance of the volatility-managed portfolio for most categories of factors. Our further investigation reveals that the timing success of our method hinges on its ability in identifying ex ante regime switches such as jumps and crashes of the factors and its predictability on future macroeconomic risk.

This study empirically investigates the impact of supply chain network centrality on corporate greenwashing behavior. By constructing supply chain networks of Chinese A-share listed companies, we find a strong positive correlation between supply chain network centrality and corporate greenwashing behavior, with an increase of approximately 6.20%. The paper identifies the underlying mechanism as the contagion of the green bandwagon effect within the supply chain, which is observed specifically in the downstream network, particularly among corporate-customers. Additionally, we observe that the positive effects are more pronounced in companies with lower information asymmetry, as well as in labor- and capital-intensive industries and regions with disadvantaged economic conditions. These findings offer important insights for improving corporate environmental responsibility and curbing greenwashing practices.

This paper develops a network to analyze inter-industry risk spillovers during high and low volatility periods. Our findings indicate that China's Industrials and Consumer Discretionary exhibit the greatest levels of spillovers in both high and low volatility states. Notably, our results demonstrate the "event-driven" character of structural changes to the network during periods of pronounced risk events. At the same time, the economic and financial network exhibits clear "small world" characteristics. Additionally, in the high volatility stage, the inter-industry risk contagion network becomes more complex, featuring greater connectivity and direct contagion paths. Furthermore, concerning the spillover connection between finance and the real sector, the real economy serves as a net exporter of risk. The study's findings can assist government agencies in preventing risk contagion between the financial market and the real economy. The empirical evidence and policy lessons provide valuable insights for effective risk management.

Using Chinese publicly listed firms from 2007 to 2020, this study empirically explores the impact of non-controlling shareholders’ network on the corporate excess goodwill. We find that the centrality of non-controlling shareholders’ network significantly decreases the excess goodwill from mergers and acquisitions, indicating that non-controlling shareholders’ network can restrain the goodwill bubbles. Moreover, the inhibitory effect of non-controlling shareholders’ network on excess goodwill stems from pressure-resistant institutional investors and individual investors. This effect is achieved through the information effect, resource effect, and governance effect. Furthermore, this inhibitory effect is more pronounced in firms located in less developed regions and legal environments, and firms with lower audit quality. In conclusion, non-controlling shareholders’ network plays a positive role in the restriction of excess goodwill in listed companies.

Financial technology, also known as Fintech, is transforming our daily life and revolutionizing the financial industry. Yet at present, consensus regarding the effect of Fintech on green bonds market is lacking. With novel data from China, this study documents robust evidence showing that Fintech development can significantly boost green bonds issuance. Further analysis suggests that this promotion effect occurs by empowering intermediary institutions and increasing social environmental awareness. Additionally, we investigate the heterogeneous effect and find that the positive relation is more pronounced for bonds without high ratings and in cities connected with High-Speed Railways network. The results call for the attention from policymakers and security managers to take further notice of Fintech utilization in green finance products.

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.

Beyond a bias toward local stocks, investors prefer companies in certain cities over others. This study uses the geographic network of investor-followed stocks from stock watchlists to identify intercity investment preferences in China. We measure the city-pair connectivity by its likelihood of sharing an investor in common whose stock watchlist is highly concentrated in the firms of that city pair. We find that a higher connectivity-weighted aggregate stock demand-to-supply ratio across connected cities is associated with higher stock valuations, higher turnover, better liquidity, and lower cost of equity for firms in the focal city. The effects are robust to controls for geographic proximity and the broad investor base, are stronger among small firms, extend to stock return predictability, and imply excess intercity return comovement. Our results suggest that city connectivity revealed on the stock watchlist helps identify network factors in asset pricing.

Beyond a bias toward local stocks, investors prefer companies in certain cities over others. This study uses the geographic network of investor-followed stocks from stock watchlists to identify intercity investment preferences in China. We measure the city-pair connectivity by its likelihood of sharing an investor in common whose stock watchlist is highly concentrated in the firms of that city pair. We find that a higher connectivity-weighted aggregate stock demand-to-supply ratio across connected cities is associated with higher stock valuations, higher turnover, better liquidity, and lower cost of equity for firms in the focal city. The effects are robust to controls for geographic proximity and the broad investor base, are stronger among small firms, extend to stock return predictability, and imply excess intercity return comovement. Our results suggest that city connectivity revealed on the stock watchlist helps identify network factors in asset pricing.

We study the effects of a direct high-speed rail (HSR) service between two cities on investors and firms in China’s A-share markets. After an HSR introduction, retail investors make more cross-city web searches and block stock purchases of firms in connected cities. An HSR introduction also leads to less comovement among local stocks and more comovement between stocks in connected cities. Firms located in more central cities in the HSR network enjoy higher firm valuation, lower cost of equity, higher turnover, and better liquidity, in part through the channel of increased investor recognition. The HSR effects on capital market outcomes are more pronounced among small firms and when the connected city-pair distance is below 1,500 km, for which HSR is faster than flying. The findings highlight the importance of in-person interactions in financial markets.

Using text data from Reddit, we construct inter-firm linkages based on shared discussions and common authors on social media. We find that firms linked on social media have similar fundamentals characteristics. The positive predictability of the returns of their Reddit peer stocks on focal stocks’ future returns suggests a sluggish dissemination of information. Our findings show that social media activities capture the collective cognition of the public, effectively reflecting the financial network in an implicit way.