A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.

• Shi, M. (2024). Monitoring for Waste: Evidence from Medicare Audits*. The Quarterly Journal of Economics, 139, 993–1049.

  This article examines the trade-offs of monitoring for wasteful public spending. By penalizing unnecessary spending, monitoring improves the quality of public expenditure and incentivizes firms to invest in compliance technology. I study a large Medicare program that monitored for unnecessary health care spending and consider its effect on government savings, provider behavior, and patient health. Every dollar Medicare spent on monitoring generated $24–$29 in government savings. The majority of savings stem from the deterrence of future care, rather than reclaimed payments from prior care. I do not find evidence that the health of the marginal patient is harmed, indicating that monitoring primarily deters low-value care. Monitoring does increase provider administrative costs, but these costs are mostly incurred up-front and include investments in technology to assess the medical necessity of care.

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• Wachter, J. A., & Kahana, M. J. (2024). A Retrieved-Context Theory of Financial Decisions*. The Quarterly Journal of Economics, 139, 1095–1147.

  Studies of human memory indicate that features of an event evoke memories of prior associated contextual states, which in turn become associated with the current event’s features. This retrieved-context mechanism allows the remote past to influence the present, even as agents gradually update their beliefs about their environment. We apply a version of retrieved-context theory, drawn from the literature on human memory, to explain three types of evidence in the financial economics literature: the role of early life experience in shaping investment choices, occurrence of financial crises, and the effect of fear on asset allocation. These applications suggest a recasting of neoclassical rational expectations in terms of beliefs as governed by principles of human memory.

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• Addoum, J. M., Delikouras, S., Ke, D., & Korniotis, G. M. (2024). Industry Clusters and the Geography of Portfolio Choice. Journal of Financial and Quantitative Analysis, 59, 1031–1063.

  Using detailed data on U.S. households’ locations, employment, and financial portfolios, we document that individuals employed in locally clustered industries are more likely to invest in risky assets. This pattern is strongest among individuals with high labor income, employed in skilled occupations, and with strong cognitive skills. Our overall evidence suggests the relation between industry clusters and investment decisions is best explained by clusters enhancing human capital among local industry workers, in turn amplifying their effective risk tolerance. Our findings highlight the important role of local labor market composition in generating household portfolio patterns within and across geographies.

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• Bali, T. G., Nichols, D. C., & Weinbaum, D. (2024). Inferring Aggregate Market Expectations from the Cross Section of Stock Prices. Journal of Financial and Quantitative Analysis, 59, 1064–1099.

  We introduce a new approach to estimating long-term aggregate discount rates using the cross section of earnings and book values to explain current stock prices and extract expected market returns. The proposed discount rate measure is countercyclical. Shocks to it account for nearly half of historical market return variation; in contrast, shocks to other discount rate measures account for no more than 2%. It dominates other measures in explaining time-series variation in returns on duration-sorted portfolios and delivers out-of-sample predictability that exceeds that afforded by other expected return measures and predictive variables. It also performs well in international equity markets.

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• Derrien, F., Garel, A., Romec, A., & Weisskopf, J.-P. (2024). Online Reputation and Debt Capacity. Journal of Financial and Quantitative Analysis, 59, 1100–1140.

  We explore the effects of online customer ratings on financial policy. Using a large sample of Parisian restaurants, we find a positive and economically significant relationship between customer ratings and restaurant debt. We use the locally exogenous variations in customer ratings resulting from the rounding of scores in regression discontinuity tests to establish causality. Favorable online ratings reduce cash flow risk and increase resilience to demand shocks. Consistent with the view that good online ratings increase the debt capacity of restaurants and their growth opportunities, restaurants with good ratings use their extra debt to invest in tangible assets.

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• Dong, X., Liu, Q., Lu, L., Sun, B., & Yan, H. (2024). Anomaly Discovery and Arbitrage Trading. Journal of Financial and Quantitative Analysis, 59, 933–955.

  We analyze a model in which an anomaly is unknown to arbitrageurs until its discovery, and test the model implications on both asset prices and arbitrageurs’ trading activities. Using data on 99 anomalies documented in the existing literature, we find that the discovery of an anomaly reduces the correlation between the returns of its decile-1 and decile-10 portfolios. This discovery effect is stronger if the aggregate wealth of hedge funds is more volatile. Finally, hedge funds increase (reverse) their positions in exploiting anomalies when their aggregate wealth increases (decreases), further suggesting that these discovery effects operate through arbitrage trading.

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• Filippou, I., Taylor, M. P., & Wang, Z. (2024). Media Sentiment and Currency Reversals. Journal of Financial and Quantitative Analysis, 59, 1401–1429.

  Analyzing 48 foreign exchange (FX) rates and 1.2 million FX-related news articles over a 35-year period, using digital textual analysis, we find that a currency reversal investment strategy that buys (sells) currencies with low (high) media sentiment offers strong positive and statistically significant returns and Sharpe ratios. The results are robust and the strategy adds value over other currency premia determinants. Analysts’ forecasts systematically mispredict the reversal strategy. This is the first article to show that price reversals based on media sentiment are a well-defined feature of the FX market.

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• Flynn, S. J., & Ghent, A. (2024). Does Main Street Benefit from What Benefits Wall Street? Journal of Financial and Quantitative Analysis, 59, 1300–1336.

  Yes. We show that aggregate stock returns predict aggregate U.S. employment, despite the industrial composition of publicly traded firms differing markedly from that of all firms, and the representativeness of public firms declining over time. We also show that appropriately reweighted stock returns predict industry and local labor market outcomes. We find the strongest evidence of an alignment of interests between shareholders and workers in the manufacturing sector, despite its declining labor share of output. Our findings suggest that at quarterly frequencies, product demand shocks are more important drivers of industry- and city-level stock returns than technology shocks.

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• Füllbrunn, S., Huber, C., Eckel, C., & Weitzel, U. (2024). Heterogeneity of Beliefs and Trading Behavior: A Reexamination. Journal of Financial and Quantitative Analysis, 59, 1337–1361.

  Combining experimental data sets from seven individual studies, including 255 asset markets with 2,031 participants, and 36,326 short-term price forecasts, we analyze the role of heterogeneity of beliefs in the organization of trading behavior by reproducing and reconsidering earlier experimental findings. Our results confirm prior evidence that price expectations affect trading behavior. However, heterogeneity in beliefs does not seem to drive overpricing and asset market bubbles, as suggested by earlier studies, and we find no indication of short-term beliefs being better determinants of trading behavior than longer-term beliefs.

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• Hansen, J. H., & Siggaard, M. V. (2024). Double Machine Learning: Explaining the Post-Earnings Announcement Drift. Journal of Financial and Quantitative Analysis, 59, 1003–1030.

  We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and 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 explain PEAD directly and consistently, and the framework can be applied broadly in finance.

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