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We study how derivatives (with nonlinear payoffs) affect the underlying asset’s liquidity. In a rational expectations equilibrium, informed investors expect low conditional volatility and sell derivatives to the others. These derivative trades affect different investors’ utility differently, possibly amplifying liquidity risk. As investors delta hedge their derivative positions, price impact in the underlying drops, suggesting improved liquidity, because informed trading is diluted. In contrast, effects on price reversal are ambiguous, depending on investors’ relative delta hedging sensitivity (i.e., the gamma of the derivatives). The model cautions of potential disconnections between illiquidity measures and liquidity risk premium due to derivatives trading.

We show that managerial learning from stock prices can lead to feedback loop vulnerability: corrective actions based on perceived negative market signals reduce the sensitivity of asset payoffs to stock market information. Less sensitivity discourages liquidity provision and increases the price impact of liquidity shocks. Interestingly, overconfident managers who disregard stock price information may be less vulnerable to the adverse price impact of nonfundamental liquidity shocks. Our empirical evidence strongly supports the model’s underlying premises and predictions: First, investment decisions of overconfident CEOs are significantly less responsive to stock price fluctuations. Second, the price impact of liquidity shocks, for example, mutual fund fire sales, is substantially smaller for firms with overconfident CEOs.

We re-examine the puzzling pattern of lead-lag returns among economically-linked firms. Our results show that investors consistently underreact to information from lead firms that arrives continuously, while information with the same cumulative returns arriving in discrete amounts is quickly absorbed into price. This finding holds across many different types of economic linkages, including shared-analyst-coverage. We conclude that the ǣfrog in the panǥ (FIP) momentum effect is pervasive in co-momentum settings, suggesting that information discreteness (ID) serves as a cognitive trigger that reduces investor inattention and improves inter-firm news transmission.

We develop a data-sales model to study the implications of alternative data for financial markets. Investors acquire skills to process the purchased raw data, and developing such skills is costly and involves considerable uncertainty. The data vendor controls the size of the data sample to influence the precision of the information investors can extract from the purchased data. Price informativeness is hump-shaped in skill-acquisition costs although the cost of capital and return volatility are U-shaped in skill-acquisition costs. Similar patterns can arise for skill mean and volatility. Our analysis suggests that the funds and data industries foster each other.

We provide a psychological explanation for the delayed price response to news about economically linked firms. We show that the return predictability of economically linked firms depends on the nearness to the 52-week high stock price. The interaction between news about economically linked firms and the nearness to the 52-week high can partially explain the underreaction to news about customers, geographic neighbors, industry peers, or foreign industries. We also find that analysts react to news about economically linked firms but the 52-week high effect reduces such reactions, providing direct evidence that the 52-week high affects the belief-updating process.

We study the transmission of financial news and opinions through social interactions among retail investors in the United States. We identify a series of plausibly exogenous shocks, which cause “treated investors” to trade abnormally. We then trace the “contagion” of abnormal trading activity from the treated investors to their neighbors and their neighbors’ neighbors. Coupled with methodology drawn from epidemiology, our setting allows us to estimate the rate of communication and how it varies with the characteristics of the underlying investor population.

While some criticises that traditional ETFs are too passive in reflecting market signals, research from Dr. Shiyang Huang, Associate Professor in Finance, HKU Business School and his team shows that Industry ETFs are able to hedge the industry and play a great role in improving market efficiency in the US market. The Industry ETF was proven as a positive financial innovation for both investors and the market and therefore should be encouraged by regulators.

香港大學金融學副教授黃詩楊聯同多名教授發表研究報告，建議監管機構應鼓勵金融機構發行更多行業ETF(交易所買賣基金)，藉以為金融市場和投資者帶來更多金融創新。

We investigate the effect of pre-IPO investments by public market institutional investors (institutions) on the exit of venture capitalists (VCs). Results indicate that institutions’ pre-IPO investments reduce IPO underpricing by mitigating VCs’ reliance on all-star analysts to boost market liquidity. We conclude that institutions facilitate VC exits in the secondary market. Supporting this view, our analysis reveals that the presence of institutions allows VCs to exit with a reduced price impact in the secondary market. Consistent with the ease of exit, VCs offer fewer shares at the IPO and are more likely to invest in institutionally backed startups.