Market Vibes
The Psychological Externalities of Investing: Evidence from Stock Returns and Crime
John Huck
Review of Financial Studies, forthcoming
Abstract:
This paper investigates the psychological effects from stock market returns. Using an FBI database of over 55 million daily reported crime incidents across the United States, crime is proposed as a measure of psychological well-being. The evidence suggests that stock returns affect the well-being of not only investors but also noninvestors. Specifically, a contemporaneous negative (positive) relationship between daily stock market returns and violent crime rates is found for investors (noninvestors). A similar relationship is also found between local earnings surprises and violent crime. The contrasting relationships for investors and noninvestors suggests that relative wealth may influence well-being.
Washington Policy Analysts and the Propagation of Political Information
Daniel Bradley et al.
Management Science, forthcoming
Abstract:
Washington policy research analysts (WAs) monitor political developments and produce research to interpret the impact of these events. We find institutional clients channel more commissions to brokerages providing policy research and commission-allocating institutional clients generate superior returns on their politically sensitive trades. We find that WA policy research reports are associated with significant price and volume reactions. Finally, we find sell-side analysts with access to WA issue superior stock recommendations on politically sensitive stocks. These effects are particularly acute during periods of high political uncertainty. Overall, we uncover a unique and an important conduit through which political information filters into asset prices.
Informed options trading before FDA drug advisory meetings
Zekun Wu, Paul Borochin & Joseph Golec
Journal of Corporate Finance, February 2024
Abstract:
Months before the Food and Drug Administration (FDA) decides to approve or reject a new drug, it often asks committees of drug experts for their recommendations. The experts receive nonpublic technical reports from drug firms and FDA staff. We find significant abnormal options trading before the final reports are created and the committees meet, particularly for small drug firms. These options trades appear to be informed because there are more calls (puts) purchased before approvals (rejections), and a majority have maturities covering the dates when the reports are publicly released. Our results imply that securities regulators should consider monitoring trades early, well before FDA drug approval decisions.
Disseminating Information on Twitter: Evidence from Investment Advisers
Seyed Mohammad Kazempour
Louisiana State University Working Paper, September 2023
Abstract:
I show that investment advisers disseminate valuable stock information on their Twitter accounts. A one standard deviation increase in the sentiment of their tweets predicts a 12 bps increase in abnormal returns over the next week. This informativeness is not a result of pump-and-dump strategies or ex-post window-dressing. Advisers' tweets disseminate and interpret public news, especially analyst revisions and earnings announcements. Furthermore, they identify which recent trends in stock prices are fundamentally justified. Advisers offering financial planning services post more informative tweets. My results highlight the value of Twitter for connecting advisers and investors.
Smartphone Trading Technology, Investor Behavior, and Mutual Fund Performance
Xiao Cen
Management Science, forthcoming
Abstract:
Using proprietary individual-level trading data around a natural experiment -- the release of a smartphone trading app by a large investment advisor -- this study investigates how smartphone trading technology affects retail investor behavior and mutual fund performance. App adoption by retail investors leads to an increase in investor attention and trading volume. App adopters' flows become more sensitive to short-term fund returns and market sentiment, resulting in higher aggregate flow volume among adopters. The funds more exposed to the shock experience a greater decline in abnormal returns, likely attributable to higher fund flow volume and liquidity costs. As a result, both adopters and nonadopters experience a decline in their mutual fund investment returns.
Gone with the big data: Institutional lender demand for private information
Jung Koo Kang
Journal of Accounting and Economics, forthcoming
Abstract:
I explore whether big-data sources can crowd out the value of private information acquired through lending relationships. Institutional lenders have been shown to exploit their access to borrowers' private information by trading on it in financial markets. As a shock to this advantage, I use the release of the satellite data of car counts in store parking lots of U.S. retailers. This data provides accurate and near-real-time signals of firm performance, which can undermine the value of borrowers' private information obtained through syndicate participation. I find that once the satellite data becomes commercially available, institutional lenders are less likely to participate in syndicated loans. The effect is more pronounced when borrowers are opaque or disseminate private information to their lenders earlier and when the data predicts borrower performance more accurately. I also show that institutional lenders' reduced demand for private information leads to less favorable loan terms for borrowers.
Option Momentum
Steven Heston et al.
Journal of Finance, December 2023, Pages 3141-3192
Abstract:
This paper investigates the performance of option investments across different stocks by computing monthly returns on at-the-money straddles on individual equities. We find that options with high historical returns continue to significantly outperform options with low historical returns over horizons ranging from 6 to 36 months. This phenomenon is robust to including out-of-the-money options or delta-hedging the returns. Unlike stock momentum, option return continuation is not followed by long-run reversal. Significant returns remain after factor risk adjustment and after controlling for implied volatility and other characteristics. Across stocks, trading costs are unrelated to the magnitude of momentum profits.
What Do Impact Investors Do Differently?
Shawn Cole et al.
NBER Working Paper, November 2023
Abstract:
In recent years, impact investors -- private investors who seek to generate simultaneously financial and social returns -- have attracted intense interest and controversy. We analyze a novel, comprehensive data set of impact and traditional investors to assess how the non-financial characteristics of impact portfolios differ from their traditional counterparts. First, we document that they are more likely to invest in disadvantaged areas and nascent industries and exhibit more risk tolerance and patience. We then examine the degree to which impact investors expand the financing frontier, versus investing in companies that could have attracted traditional private financing. Utilizing a variety of network theoretic and event study analyses, we find limited support for the assertion that impact investors expand the financing frontier, either in the deal-selection stage or the post-investment stage.
AI-Powered Trading, Algorithmic Collusion, and Price Efficiency
Winston Wei Dou, Itay Goldstein & Yan Ji
University of Pennsylvania Working Paper, October 2023
Abstract:
The integration of algorithmic trading and reinforcement learning, known as AI-powered trading, has significantly impacted capital markets. This study utilizes a model of imperfect competition among informed speculators with asymmetric information to explore the implications of AI-powered trading strategies on speculators' market power, information rents, price informativeness, and market liquidity. Our results demonstrate that informed AI speculators, even though they are "unaware" of collusion, can autonomously learn to employ collusive trading strategies. These collusive strategies allow them to achieve supra-competitive profits by strategically under-reacting to information, even in the absence of explicit communication or coordination that might breach conventional antitrust regulations. Algorithmic collusion emerges from two distinct mechanisms. The first mechanism is collusion via price-trigger strategies ("artificial intelligence"), while the second stems from learning biases ("artificial stupidity") and homogenization. The former is evident only when there is limited price efficiency and information asymmetry. In contrast, the latter persists even under conditions of high price efficiency or severe information asymmetry. As a result, in a market with prevalent AI-powered trading, both price informativeness and market liquidity can suffer, reflecting the influence of both artificial intelligence and stupidity.