团队

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.

我们建立一个数据销售模型来研究另类数据对金融市场的影响。投资者需要特别的技术以准确分析购买的原始数据，但建立这项技术成本高同时存在相当大的不确定性。数据供应商透过控制数据样本数量去影响投资者从购买的数据中提取信息的准确性。我们的模型分析发现数据分析技术的成本对资本成本以及资产收益波动率的影响均呈U型关系，但是对市场信息量则呈驼峰型关系。同时，数据分析的技术平均水平和不确定性亦出现类似影响。我们的分析同时发现使用另类数据的基金和数据行业存在着相互促进的关系。

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。

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。

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。

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。

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.

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.