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.
3917 8564
KK 834
- 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.hkubs.hku.hk/~huangsy/
- Financial Economics
- Asset Pricing
- Information Economics
- “The Smart Beta Mirage” (with Yang Song and Hong Xiang), Journal of Financial and Quantitative Analysis, forthcoming.
- “The Booms and Busts of Beta Arbitrage” (with Xin Liu, Dong Lou and Christopher Polk), Management Science, 70(8), 2024, 5367-5385.
- “Derivatives and Market (Il)liquidity” (with Bart Zhou Yueshen and Cheng Zhang), Journal of Financial and Quantitative Analysis, 59(1), 2024, 157-194.
- “Managerial Overconfidence and Market Feedback Effects” (with Suman Banerjee, Vikram Nanda and Steven Chong Xiao), Management Science, 69(12), 2023, 7285-7305.
- “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
我們建立一個數據銷售模型來研究另类數據對金融市場的影響。投資者需要特别的技术以準確分析購買的原始數據,但建立這項技術成本高同時存在相當大的不確定性。數據供應商透過控制數據樣本數量去影響投資者從購買的數據中提取信息的準確性。我們的模型分析發現數據分析技術的成本對資本成本以及資產收益波動率的影響均呈U型關係,但是對市場信息量則呈駝峰型關係。同時,數據分析的技術平均水平和不確定性亦出現類似影響。我們的分析同時發現使用另類數據的基金和數據行業存在著相互促進的關係。
透過分析英國的官方管理的全面數據,我們研究政府債券市場中不同類型投資者的交易行為。我們的數據涵蓋英國二級市場幾乎所有國債交易,同時我們可以觀測到每筆交易的詳細信息,包括交易雙方的身份。我們發現,對沖基金的每日交易可預測到未來一至五天的英國國債回報率,然而這個預測在一個月之後出現反轉。這種短期回報的可預測性,部分成因在於對沖基金能預測其他投資者的投資行為。我們同時發現公募基金的交易也可預示英國國債券的回報率,這個預測率時間可持續達一至兩個月並且不會出現發現轉。根據我們的深入研究,我們發現公募基金的預測率部分原因是由於公募基金能夠預測短期利率的變化。
本文就市場對於經濟關聯公司的新聞所出現延遲價格反應提出了一個基於心理學的新解釋。我們發現經濟關聯公司的股價收益預測,取決於其目前股價與52週最高股價之間有多接近。經濟關聯公司的新聞與公司股價是否接近其52週高位,部份解釋了為何市場對於消費者、地理鄰居、同業或外國行業的新聞反應較為遲緩。研究亦發現股票分析師會因公司股價接近52週高位,亦對關經濟關聯公司的新聞產生了延遲反應。這些發現直接證明瞭公司股價接近52週高位對於投資者信念更新過程的影響。
我們研究美國散戶投資者如何通过社交傳播財經新聞和投资意見。我們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,我們追踪投资者的交易行为,尤其是被这些事件影響投資者的鄰居。这样样本选择有利于我们研究投资行为在左鄰右舍之間的「傳染性」。結合流行病學的方法,此研究的情景設置讓我們可以估算傳訊速率,以及它如何隨著潛在投資者群體的特徵而變化。
有聲音批評傳統的ETF過於被動,未能有效反應市場訊息,然而港大經管學院金融學副教授黃詩楊博士及其研究團隊卻發現,行業ETF在美國市場中能有效規避風險,並能提升市場效率。
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.
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?
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.