Although the history of China's venture capital (VC) market is relatively short, it has already become the second largest VC market in the world and produced the second largest number of “unicorns” (startups with a valuation over $1 billion) after the US. Despite the remarkable growth of both China's tech sector and venture capital market, academic research in this area remains sparse. Two broad issues hinder the efforts of researchers studying this market: choosing the right data sources and understanding evolving institutional details. To address these two issues, I first describe available data sources, accompanied with filters aimed at improving the quality of the data. I then review institutional details unique to the Chinese setting and recent regulatory changes that have direct impacts on the Chinese venture capital market. I conclude by listing some open research questions.

The wisdom of crowds suggests that groups with more diversely informed individuals reach more informed decisions because their members are collectively more knowledgeable. I study this idea in the context of the market's response to earnings announcements by examining how information diversity across investors affects the efficiency of the price response to earnings news. I measure investors' information diversity based on their geographic dispersion, which I estimate using the locations of the requests for firms' filings to EDGAR. Greater geographic dispersion is associated with greater trading during the announcement period; this supports the use of geographic dispersion as a measure of information diversity. Consistent with my predictions, the price response to a firm's earnings news is more efficient when the firm's investors have greater information diversity. In further analysis, I find that the initial heightened trading for firms with more diversely informed investors subsides quickly after the announcement period.

This paper notes a simple connection between synthetic control and online learning. Specifically, we recognize synthetic control as an instance of Follow‐The‐Leader (FTL). Standard results in online convex optimization then imply that, even when outcomes are chosen by an adversary, synthetic control predictions of counterfactual outcomes for the treated unit perform almost as well as an oracle weighted average of control units' outcomes. Synthetic control on differenced data performs almost as well as oracle weighted difference‐in‐differences, potentially making it an attractive choice in practice. We argue that this observation further supports the use of synthetic control estimators in comparative case studies.

We evaluate how a positive, technology-driven shock to bank liquidity affects small business dynamics across different size distributions. We first show that banks receiving positive liquidity shocks increase lending to relatively larger SMEs, not to the smallest firms. This finding is consistent with the view that a positive liquidity shock enhances bank charter values, thereby reducing risk-taking incentives. Moreover, such disproportionate credit allocation leads to a crowding-out effect on micro firms. When larger SMEs grow faster and exit less because of better access to credit, their expansion stifles the development of micro firms, whose access to credit remains unchanged.

We propose a naïve model averaging (NMA) method that averages the OLS out-of-sample forecasts and the historical means and produces mostly positive out-of-sample R2s for the variables significant in sample in forecasting market returns. Surprisingly, more sophisticated weighting schemes that combine the predictive variable and historical mean do not consistently perform better. With unstable economic relations and a limited sample size, sophisticated methods may lead to overfitting or be subject to more estimation errors. In such situations, our simple methods may work better. Model misspecification, rather than declining return predictability, likely explains the predictive performance of the NMA method.

This study examines the information content of stock option-implied volatility. We measure the arrival intensities and magnitudes of scheduled and unscheduled news as well as fundamental and non-fundamental news. Most of these news measures exhibit strong and positive associations with contemporaneous stock return volatility, and many of them can be predicted by implied volatility. Approximately one third of the predictive power of implied volatility on future realized volatility can be attributed to its ability to predict these news measures, with the majority of the predictive power arising from its capacity to predict the arrival intensities of both scheduled and unscheduled news. The predictive power is higher for fundamental news than for non-fundamental news.

We perform portfolio-level analyses to understand insurance firms’ preferred habitat behavior in the government bond market. Based on portfolio durations and portfolio weights across maturities, we find that interest rate risk exposures of insurers’ portfolios are related to their operating liabilities and financing constraints. We show that this habitat behavior significantly affects bond pricing. During the “quantitative easing” era, bond purchases by the Federal Reserve have a larger impact on the yields of Treasury bonds with a higher habitat demand.

We provide new causal evidence for the impact of equity financing incentive on firms’ voluntary disclosure decisions by exploring the 2008 seasoned equity offering deregulation, which exogenously facilitates small firms’ access to public equity financing and increases their equity issuance incentives without changing their business and information environments. We argue that the heightened equity financing incentive due to the deregulation can motivate a firm to increase disclosures even in the period without actual equity issuance, because such disclosures, by signaling a commitment to disclosure, could reduce the cost of equity in case the firm issues equity in the future. Consistent with this argument, we find that, benchmarking against control firms that are not affected by the deregulation, an average treatment firm that is affected by the deregulation but does not issue equity provides more management earnings forecasts in the post-deregulation period. The effect is mainly driven by repeated forecasters and is more pronounced for firms with greater equity financing needs and firms with higher information asymmetry in the equity market.