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.
- Ph.D., London School of Economics and Political Science
- M.A., Tsinghua University
- B.A., Tsinghua University
Dr. Shiyang HUANG received his Ph.D. degree in finance from the London School of Economics in 2015. He also holds a master degree and a bachelor degree in economics from Tsinghua University. He joined The University of Hong Kong in 2015.
Shiyang’s research agenda focuses on financial economics and empirical asset pricing. He has published research papers in several academic journals including Journal of Financial Economics, Management Science and Journal of Economic Theory. He also won the best paper awards at academic conferences, including Best Paper Award at 7th Melbourne Asset Pricing Meeting, Conference Best Paper Award at China International Conference in Finance of 2019, Best Paper Award at 14th Annual Conference in Financial Economics Research by Eagle Labs (IDC) of 2017, Yihong Xia Best Paper Award at hina International Conference in Finance of 2015, Conference Best Paper Award at Paris December Finance Meeting of 2014, IdR QUANTVALLEY / FdR Quantitative Management Initiative Research Award of 2013.
For a full and up-to-date profile, please visit http://www.fbe.hku.hk/~huangsy/
- Financial Economics
- Asset Pricing
- Information Economics
- “Skill Acquisition and Data Sales” (with Yan Xiong and Liyan Yang), Management Science, 68(8), 2022, 6116-6144.
- “A Frog in Every Pan: Information Discreteness and the Lead-lag Returns Puzzle” (with Charles M.C. Lee, Yang Song and Hong Xiang), Journal of Financial Economics, 145(2), 2022, 83-102.
- “Informed Trading in Government Bond Markets” (with Robert Czech, Dong Lou and Tianyu Wang), Journal of Financial Economics, 142(3), 2021, 1253-1274
- “Psychological Barrier and Cross-firm Return Predictability” (with Tse-Chun Lin and Hong Xiang), Journal of Financial Economics, 142(1), 2021, 338-356
- “The Rate of Communication” (with Byoung-Hyoun Hwang and Dong Lou), Journal of Financial Economics, 141(2), 2021, 533-550
- “Speed Acquisition” (with Bart Zhou Yueshen), Management Science, 67(6), 2021, 3492-3518
- “Public Market Players in the Private World: Implications for the Going-Public Process” (with Yifei Mao, Cong (Roman) Wang and Dexin Zhou), The Review of Financial Studies, 34(5), 2021, 2411-2447
- “Innovation and Informed Trading: Evidence from Industry ETFs” (with Maureen O’Hara and Zhuo Zhong), The Review of Financial Studies, 34(3), 2021, 1280-1316
- “Offsetting Disagreement and Security Prices” (with Byoung-Hyoun Hwang, Dong Lou and Chengxi Yin), Management Science, 66(8), 2020, 3444-3465
- “Institutionalization, Delegation, and Asset Prices” (with Zhigang Qiu and Liyan Yang), Journal of Economic Theory, 186, 2020, 104977
- “Attention Allocation and Return Co-movement: Evidence from Repeated Natural Experiments” (with Yulin Huang and Tse-Chun Lin), Journal of Financial Economics, 132(2), 2019, 369-383
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 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.
We empirically examine the impact of industry exchange-traded funds (IETFs) on informed trading and market efficiency. We find that IETF short interest spikes simultaneously with hedge fund holdings on the member stock before positive earnings surprises, reflecting long-the-stock/short-the-ETF activity. This pattern is stronger among stocks with high industry risk exposure. A difference-in-difference analysis on the ETF inception event shows that IETFs reduce post-earnings-announcement drift more among stocks with high industry risk exposure, suggesting that IETFs improve market efficiency. We also find that the short interest ratio of IETFs positively predicts IETF returns, consistent with the hedging role of IETFs.