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on Financial Markets |
| By: | Patrick Luo; Enrichetta Ravina; Marco C. Sammon; Luis M. Viceira |
| Abstract: | Using a large and representative panel of U.S. brokerage accounts, we show that retail investors trade as contrarians after large earnings surprises, especially for loser stocks, and that such contrarian trading contributes to price momentum and post earnings announcement drift (PEAD). We show that extreme return streaks and surprises are not enough for stocks to exhibit PEAD and momentum and that the intensity of contrarian retail trading plays a key role: the PEAD of loser stocks with bad earnings surprises becomes increasingly more negative as retail buying pressure increases, and he PEAD of the stocks with the highest past returns and largest earnings surprises is the most positive for the stocks with the biggest net retail outflow. Finer sorts confirm the results, as do sorts by firm size and institutional ownership level. Younger and more attentive individuals are more likely to be contrarian, and a firm’s dividend yield, leverage, size, book to market, and analyst coverage are associated with the fraction of contrarian trades they face around earnings announcements. The disposition effect and stale limit orders, while present in our sample, do not explain our results. Our findings are consistent with investors’ conservatism, sticky beliefs, and cognitive uncertainty, as well as an incorrect belief in the Law of Small Numbers. |
| JEL: | G0 G11 G12 G4 G41 G5 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34086 |
| By: | Noam Michelson (Bank of Israel) |
| Abstract: | This paper evaluates the impact of the Bank of Israel’s corporate bond purchase program initiated in July 2020 amid the COVID-19 pandemic. The analysis reveals that the announcement led to significant reductions in credit spreads for eligible and noneligible bonds, and particularly for A-rated bonds, while actual purchases had an additional, yet limited, effect. Additionally, the program reopened the primary market for noninvestment grade issuances. Finally, the inclusion of bonds issued by commercial banks as eligible for purchasing, a unique feature of the program, had a positive effect on commercial banks' credit supply. This study provides new insights into central bank interventions during financial crises and presents novel evidence for innovative crisis management tools intended to stimulate credit in times of distress. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:boi:wpaper:2025.07 |
| By: | Minghong Yuan; Wen Wen; Indranil Bardhan |
| Abstract: | What is the impact of private equity (PE) investment on healthcare value? Does PE investment hurt or improve healthcare value, and if so, can its effect be mitigated through the use of health information technologies (IT)? Given the significant investments by PE firms in the healthcare sector in recent years, these are important research questions. Stakeholders, including policy makers, care providers, and patients, need to understand their likely impact and whether PE ownership is aligned with their interests. Using a staggered difference-in-differences approach and data from US hospitals from 2008-2020, we observe that the overall value of healthcare delivered by hospitals declines after PE investment. However, our empirical evidence reveals that IT-enabled, health information sharing plays an important moderating role. Hospitals with stronger information-sharing capabilities exhibit greater cost efficiencies and improvements in care quality, leading to higher healthcare value after PE investment. Furthermore, we find that the type of health information sharing matters. Specifically, we observe that improvements in care quality are primarily driven by information sharing between hospitals and ambulatory care providers, instead of simply hospital-to-hospital sharing of patient health data. Our research also identifies the underlying mechanisms through which health information sharing improves care value by reducing hospital-acquired infections and readmission rates, thereby improving care quality, and enhancing labor productivity by reducing operating costs. Our results highlight the critical role of policies and common data standards needed to promote IT-enabled information sharing between healthcare providers, which, in turn, can align incentives of PE firms with the goals of value-based care. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14717 |
| By: | Andrew W. Lo; Egor V. Matveyev; Stefan Zeume |
| Abstract: | Using tax filings from 374, 351 U.S. nonprofit organizations from 2008 to 2020, we provide the first large-scale analysis of endowment prevalence, function, asset allocation, and returns. Endowment use varies systematically across sectors and revenue models. Organizations with endowments scale mission-related spending more effectively and hedge revenue risk through asset allocation. Yet most endowments underperform passive benchmarks, with the weakest performance concentrated among smaller, self-managed funds. Advisory fees are positively correlated with gross returns but negatively with net returns, suggesting overpayment for investment services. Stronger governance, lower discretionary spending, and the use of outsourced CIOs are associated with better performance. |
| JEL: | G11 G2 G20 G23 G29 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34078 |
| By: | Hoyoung Lee; Junhyuk Seo; Suhwan Park; Junhyeong Lee; Wonbin Ahn; Chanyeol Choi; Alejandro Lopez-Lira; Yongjae Lee |
| Abstract: | In finance, Large Language Models (LLMs) face frequent knowledge conflicts due to discrepancies between pre-trained parametric knowledge and real-time market data. These conflicts become particularly problematic when LLMs are deployed in real-world investment services, where misalignment between a model's embedded preferences and those of the financial institution can lead to unreliable recommendations. Yet little research has examined what investment views LLMs actually hold. We propose an experimental framework to investigate such conflicts, offering the first quantitative analysis of confirmation bias in LLM-based investment analysis. Using hypothetical scenarios with balanced and imbalanced arguments, we extract models' latent preferences and measure their persistence. Focusing on sector, size, and momentum, our analysis reveals distinct, model-specific tendencies. In particular, we observe a consistent preference for large-cap stocks and contrarian strategies across most models. These preferences often harden into confirmation bias, with models clinging to initial judgments despite counter-evidence. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.20957 |
| By: | Xuesong Huang; Todd Keister |
| Abstract: | We ask whether imposing fees on redeeming investors can prevent runs on money market mutual funds (MMFs) and related intermediation arrangements. We first show that imposing a fee only in extraordinary times often leaves the fund susceptible to a preemptive run where investors rush to redeem before the fee applies. We then show how a policy that imposes a fee when current redemption demand is above a threshold, even in normal times, can make the fund run proof. We characterize the best policy of this type, which is immune to a run of any size. We show that the reform adopted in the U.S. in 2023 leaves funds vulnerable to runs in some market conditions and imposes an inefficiently large fee in others. |
| Keywords: | financial stability policy; preemptive runs; shadow banking |
| JEL: | G28 G23 D82 |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:101378 |
| By: | Isaac Otchere; Zia Mohammed; Witness Simbanegavi |
| Abstract: | In this paper we examine the relationship between fintech formations and the default risk and performance of incumbent financial institutions in South Africa. We find that the development of fintech startups is associated with lower bankruptcy risk, credit risk and stock return volatility among banks and other financial institutions. Fintech startup formations are also associated with improvement in incumbent institutions performance. Further analysis shows that the risk reduction effect of fintech development is more pronounced for smaller banks. Overall, our results are consistent with the assertion that fintech formations generally improve risk management efficiency and reduce incumbent financial institutions default risk. However, the relationship is nonlinear, suggesting that the initial collaboration, which reduces default risk, can turn into increased competition as more fintech startups enter the market. From a policy standpoint, efforts to promote more collaboration should be encouraged, but regulators need to be cautious of potential systemic risk. |
| Date: | 2025–08–04 |
| URL: | https://d.repec.org/n?u=RePEc:rbz:wpaper:11082 |
| By: | Bahram Adrangi; Arjun Chatrath; Saman Hatamerad; Kambiz Raffiee |
| Abstract: | This study investigates the relationship between the market volatility of the iShares Asia 50 ETF (AIA) and economic and market sentiment indicators from the United States, China, and globally during periods of economic uncertainty. Specifically, it examines the association between AIA volatility and key indicators such as the US Economic Uncertainty Index (ECU), the US Economic Policy Uncertainty Index (EPU), China's Economic Policy Uncertainty Index (EPUCH), the Global Economic Policy Uncertainty Index (GEPU), and the Chicago Board Options Exchange's Volatility Index (VIX), spanning the years 2007 to 2023. Employing methodologies such as the two-covariate GARCH-MIDAS model, regime-switching Markov Chain (MSR), and quantile regressions (QR), the study explores the regime-dependent dynamics between AIA volatility and economic/market sentiment, taking into account investors' sensitivity to market uncertainties across different regimes. The findings reveal that the relationship between realized volatility and sentiment varies significantly between high- and low-volatility regimes, reflecting differences in investors' responses to market uncertainties under these conditions. Additionally, a weak association is observed between short-term volatility and economic/market sentiment indicators, suggesting that these indicators may have limited predictive power, especially during high-volatility regimes. The QR results further demonstrate the robustness of MSR estimates across most quantiles. Overall, the study provides valuable insights into the complex interplay between market volatility and economic/market sentiment, offering practical implications for investors and policymakers. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.05552 |
| By: | Antonino Castelli; Paolo Giudici; Alessandro Piergallini |
| Abstract: | The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility. To address the challenge of managing dynamic portfolios in such an environment, this paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations. Using data on daily frequencies of the ten most capitalized cryptocurrencies from 2020 to 2025, we compare two automated investment strategies. These are a static equal weighting strategy and a rolling-window optimization strategy, both implemented to maximize the evaluation metrics of the Modern Portfolio Theory (MPT), such as Expected Return, Sharpe and Sortino ratios, while minimizing volatility. Each step of the process is handled by dedicated agents, integrated through a collaborative architecture in Crew AI. The results show that the dynamic optimization strategy achieves significantly better performance in terms of risk-adjusted returns, both in-sample and out-of-sample. This highlights the benefits of adaptive techniques in portfolio management, particularly in volatile markets such as cryptocurrency markets. The following methodology proposed also demonstrates how multi-agent systems can provide scalable, auditable, and flexible solutions in financial automation. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.20468 |
| By: | Matteo Crosignani; Emilio Osambela; Matthew Pritsker |
| Abstract: | Are carbon emissions priced in equity markets? The literature is split with different approaches yielding conflicting results. We develop a stylized model showing that, if emissions are priced, stock returns depend on expected emissions and the product of the innovation in emissions and the price-dividend ratio. Building on this insight, we derive and test new predictions. We find that emissions are priced in equity markets, but the magnitude of such pricing is highly sensitive to the inclusion of a few “super emitters” (mostly operating in electric power generation). Our theoretical insight also helps reconcile seemingly divergent results in the literature. |
| Keywords: | carbon emissions; stock returns; cost of capital; ESG |
| JEL: | D62 G11 G12 Q54 |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:101379 |
| By: | Mikhail Chernov; Magnus Dahlquist; Lars A. Lochstoer |
| Abstract: | Characteristic-based factors embed large unpriced components that depress Sharpe ratios and deviate from the mean-variance efficient (MVE) frontier. We discuss how to decompose tradable factor returns into priced (MVE) and unpriced components, showing that hedging unpriced variation realigns factors with efficiency. We outline theoretical conditions for characteristic portfolios to span the MVE and describe practical hedge-portfolio construction. In some asset classes—currencies and sovereign bonds—real-time estimation of the MVE is feasible. In the case of equities, one can hedge unpriced risks from characteristic-based factors. Empirically, unpriced risks account for 30–99% of factor return variance, and hedging can more than double Sharpe ratios. |
| JEL: | F31 G12 G13 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34009 |