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The last 60 years of research striving to explain the post-earnings announcement drift (PEAD) have resulted in numerous potential explanations. This ”zoo” of explanations, limited academic consensus, and a literature relying on thousands of earnings announcement make researchers able to detect subtle and complex effects with little practical significance. This paper exploits new capabilities of inference via machine learning to systematically examine leading variables explaining the PEAD. First, we identify a small set of variables associated with momentum, liquidity, and limited arbitrage that directly and consistently affect the PEAD. Secondly, we demonstrate the danger of hand-picking a small set of control variables, which can lead to unreliable results, overestimation of coefficients, and underestimation of their standard errors. Finally, we explore multiple variables related to general equity risk premia and find a more prominent role for price trends than otherwise suggested. We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inferences. As the post-earnings announcement drift (PEAD) literature is governed by a "zoo" of explanations, limited academic consensus on model design, and the reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that directly and consistently explain PEAD. The framework can be applied broadly in finance.

We analyze bubble formations in house prices under the covid-19 pandemic in 382 metropolitan areas in the U.S. with a special attention to the role of population density. We use a robust sieve-bootstrap version of the right-tailed unit root test procedure to identify periods of explosiveness in price-rent ratios across metro areas when controlling for fundamentals such as mortgage debt financing costs and the wealth of households. The bubble tests reveal evidence of explosive house prices during the covid-19 pandemic but with important cross-area differences. In contrast to earlier bubble episodes, we find that the frequency and size of bubble formations under the pandemic have been stronger in low-density metro areas compared to high-density metro areas.

This paper uses a novel and direct retail investor attention proxy and provide evidence that retail investor attention is important for information efficiency around earnings announcements. The main finding is the presence of asymmetric patterns in subsequent responses to earnings surprises when retail investors are the most attentive. If earnings news is bad, a strong reversal is present, which is consistent with an attention-induced buying pressure. If the news is good, then even though the announcement must also have been attention-grabbing, no subsequent buying pressure is observed. The effects are intensified for stocks with higher limits to arbitrage, and when institutional investors are the least attentive.

This paper uses a novel and direct retail investor attention proxy to show that retail investor attention is important for information efficiency around earnings announcements. The data reveals asymmetric patterns in subsequent market responses to earnings surprises where retail investors are the most attentive. If earnings surprises are negative, a distinct drift is present. The drift is consistent with attention-induced buying, as attention predicts subsequent attention, both retail and general trading, and long-term underperformance. If the surprises are positive, then even though the announcements are attention-grabbing, no drift is observed. The effects are intensified for stocks with higher limits to arbitrage and when institutional investors are the least attentive.