Shiyang Huang
Dr. Shiyang HUANG
金融学
Associate Professor

3917 8564

KK 834

Academic & Professional Qualification
  • Ph.D., London School of Economics and Political Science
  • M.A., Tsinghua University
  • B.A., Tsinghua University
Biography

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/

Research Interest
  • Financial Economics
  • Asset Pricing
  • Information Economics
Selected Publications
  • “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
Recent Publications
A Frog in Every Pan: Information Discreteness and the Lead-lag Returns Puzzle

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.

Skill Acquisition and Data Sales

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.

公司股价的心理关卡与投资收益预测

本文就市场对于经济关联公司的新闻所出现延迟价格反应提出了一个基于心理学的新解释。我们发现经济关联公司的股价收益预测,取决于其目前股价与52周最高股价之间有多接近。经济关联公司的新闻与公司股价是否接近其52周高位,部份解释了为何市场对于消费者、地理邻居、同业或外国行业的新闻反应较为迟缓。研究亦发现股票分析师会因公司股价接近52周高位,亦对关经济关联公司的新闻产生了延迟反应。这些发现直接证明了公司股价接近52周高位对于投资者信念更新过程的影响。

传讯的速率

我们研究美国散户投资者如何通过社交传播财经新闻和投资意见。我们首先找出一系列会导致某些投资者进行异常交易的外生事件。基于这些事件,我们追踪投资者的交易行为,尤其是被这些事件影响投资者的邻居。这样样本选择有利于我们研究投资行为在左邻右舍之间的「传染性」。结合流行病学的方法,此研究的情景设置让我们可以估算传讯速率,以及它如何随着潜在投资者群体的特征而变化。

打破传统ETF的迷思 - 了解卖空ETF如何有利于股票市场

有声音批评传统的ETF过于被动,未能有效反应市场讯息,然而港大经管学院金融学副教授黄诗杨博士及其研究团队却发现,行业ETF在美国市场中能有效规避风险,并能提升市场效率。

港大教授倡监管机构 鼓励开发行业ETF

香港大学金融学副教授黄诗杨联同多名教授发表研究报告,建议监管机构应鼓励金融机构发行更多行业ETF(交易所买卖基金),藉以为金融市场和投资者带来更多金融创新。

Public Market Players in the Private World: Implications for the Going-Public Process

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.

The Cost of Distraction

What could be the result if some compelling opportunities, like lottery jackpots, were potentially lucrative enough to distract the investors' attention from monitoring the stock market?

Innovation and Informed Trading: Evidence from Industry ETFs

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.

Are Smart Beta ETFs Set Up To Fail?

The concept of smart beta has a lot of data to draw on. Many so-called factors such as value, size, low volatility and momentum appear to have delivered decades of positive risk-adjusted returns, on average, for investors.