團隊

We demonstrate that a broad set of asset pricing factors/anomalies are significantly exposed to “noise trader risk,” and the noise trader risk is priced in factor premia. We first confirm that mutual funds’ flow-induced trading of factors are uninformed, as they generate a large price impact on factor returns, followed by a complete reversal. We then show that asset pricing factors are subject to flow-driven noise trader risk in that expected variation (covariation) of flow-induced noise trading strongly forecasts variance (covariance) of factor returns. Importantly, factor premia are higher when flow-driven noise trader risk is expected to be more salient.

We analyze a single-factor setting in which there is private information regarding cash flows as well as their betas. Private information about betas, together with market makers’ risk aversion and mean betas’ nonnegativity, implies a nonlinear price schedule whose stochastic slope covaries positively with order flow when the expected factor payoff is positive and vice versa. We predict a negative relation between the covariance and expected returns and an attenuation of the beta anomaly in asset returns after accounting for this relation. Empirical tests confirm these predictions.

Mutual funds investing in illiquid corporate bonds actively manage Treasury positions to buffer redemption shocks. This liquidity management practice can transmit non-fundamental fund flow shocks onto Treasuries, generating excess return volatility. Consistent with this hypothesis, we find that Treasury excess return volatility is positively associated with bond fund ownership, and this pattern is more pronounced among funds conducting intensive liquidity management. Causal evidence is provided by exploiting the U.S. Securities and Exchange Commission’s 2017 Liquidity Risk Management Rule. Evidence also suggests that the COVID-19 Treasury market turmoil was attributed to intensified liquidity management, an unintended consequence of the 2017 Liquidity Risk Management Rule.

We document and explain the sharp performance deterioration of smart beta indexes after the corresponding smart beta ETFs are launched for investment. While smart beta is purported to deliver excess returns through factor exposures, the market-adjusted return of smart beta indexes drops from about 3% “on paper” before ETF listings to about −0.50% to −1% after ETF listings. This performance decline cannot be explained by variation in factor premia, strategic timing, or diminishing returns to scale. Instead, we find strong evidence of data mining in the construction of smart beta indexes, which helps ETFs attract flows, as investors respond positively to backtests.

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。