Good Trades
The Value of Arbitrage
Eduardo Dávila, Daniel Graves & Cecilia Parlatore
Journal of Political Economy, June 2024, Pages 1947-1993
Abstract:
This paper studies the social value of closing price differentials in financial markets. We show that arbitrage gaps exactly correspond to the marginal social value of executing an arbitrage trade. Moreover, arbitrage gaps and price impact measures are sufficient to compute the total social value from closing an arbitrage gap, which may emerge for different reasons, including nonpecuniary benefits of holding particular assets. Theoretically, we show that, for a given arbitrage gap, the total social value of arbitrage is higher in more liquid markets. We compute the welfare gains from closing arbitrage gaps for covered interest parity violations.
Tech-Enabled Financial Data Access, Retail Investors, and Gambling-Like Behavior in the Stock Market
Taha Havakhor et al.
Management Science, forthcoming
Abstract:
Advancements in technology have reduced information acquisition costs, creating an improved information environment for retail investors. Specifically, new technologies, such as application programming interface (API), deliver high-volume, institutional-like raw data directly to Main Street investors. Although greater availability of information can be beneficial, it may also exacerbate retail investors’ existing trading deficiencies. Exploiting the sudden shutdown of Yahoo! Finance API, the largest free API for retail investors, this study examines how access to tech-enabled raw financial data affects retail investment. We find that retail trading volumes in stocks favored by active retail investors dropped by 8.6%–10.5% within one month of the API shutdown. The remaining retail trades collectively became more predictive of future returns, suggesting less gambling-like behavior after the API shutdown. Moreover, our randomized controlled experiment affirms the underlying mechanism: tech-enabled access to high-volume historical price data increases individuals’ overconfidence, which further leads them to engage in excessive trading. The study reveals an unintended consequence of technology-led, wider data access for retail investors.
How free is free? Retail trading costs with zero commissions
Samuel Adams, Connor Kasten & Eric Kelley
Journal of Banking & Finance, August 2024
Abstract:
We examine the economics that underlie retail trading costs around discount brokers’ widespread adoption of zero commission trading in October 2019. Our analysis of participating brokers’ Rule 606 filings and financial statements reveals little change in payment for order flow, which suggests brokers absorbed the cost of eliminating commissions in a competitive environment. We then perform a difference-in-differences analysis of effective spreads and report economically trivial changes in retail execution costs around the commission change. Finally, we assess the total trading costs of an aggregate retail portfolio compared to a host of counterfactuals. We find that following the zero-commission change, total retail transaction costs dropped substantially even under the extreme counterfactual that these traders pay exchange quoted spreads and receive zero price improvement. Our findings support the brokerage industry's claim that dropping commissions helped retail investors and should ease regulators’ concerns to the contrary.
Insider Trading Restrictions and Informed Trading in Peer Stocks
Prachi Deuskar, Aditi Khatri & Jayanthi Sunder
Management Science, forthcoming
Abstract:
Using a uniquely constructed data set of trades by corporate insiders in all stocks, we find that, after insider trading regulations become stricter, insiders are 20% more likely to trade in peer stocks and that such trades become more profitable. The increase in both the probability and profitability of peer-stock trades is driven by the insider’s information that is fungible to industry peers. Stricter insider trading laws are designed to improve liquidity and price informativeness in capital markets. We show that peer trading dampens these intended benefits of the insider trading regulation.
The passive ownership share is double what you think it is
Alex Chinco & Marco Sammon
Journal of Financial Economics, July 2024
Abstract:
Each time a stock gets added to or dropped from an index, we ask: “How much money would have to be tracking that index to explain the huge spike in rebalancing volume we observe on reconstitution day?” While index funds held 16% of the US stock market in 2021, we put the overall passive ownership share at 33.5%. Our headline number is twice as large because it reflects index funds as well as other kinds of passive investors, such as institutional investors with internally managed index portfolios and active managers who are closet indexing.
Number Processing Constraints and Earnings News
Stephen Karolyi, Thomas Ruchti & Phong Truong
Management Science, forthcoming
Abstract:
Neuroscience shows that human brains are neurologically constrained to process small numbers linearly and large numbers logarithmically, leading to underreactions to larger numbers as their perceived difference becomes smaller. We test this hypothesis in the context of earnings announcements and find that investors respond less in the short term to earnings news for stocks with high earnings per share magnitudes, exacerbating postearnings announcement drift for these stocks. These findings are distinct from and incremental to several risk-based and behavioral explanations, attenuated by robot presence and present in a quasi-experimental design using stock splits. Our evidence suggests that number processing constraints have implications for stock price efficiency.
Financial Statement Analysis with Large Language Models
Alex Kim, Maximilian Muhn & Valeri Nikolaev
University of Chicago Working Paper, May 2024
Abstract:
We investigate whether an LLM can successfully perform financial statement analysis in a way similar to a professional human analyst. We provide standardized and anonymous financial statements to GPT4 and instruct the model to analyze them to determine the direction of future earnings. Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle. Furthermore, we find that the prediction accuracy of the LLM is on par with the performance of a narrowly trained state-of-the-art ML model. LLM prediction does not stem from its training memory. Instead, we find that the LLM generates useful narrative insights about a company's future performance. Lastly, our trading strategies based on GPT's predictions yield a higher Sharpe ratio and alphas than strategies based on other models. Taken together, our results suggest that LLMs may take a central role in decision-making.
The Effect of Dispersion on the Informativeness of Consensus Analyst Target Prices
Asa Palley, Thomas Steffen & Frank Zhang
Management Science, forthcoming
Abstract:
Consensus analyst target prices are widely available online at no cost to investors. In this paper, we examine how the amount of dispersion in the individual target prices comprising the consensus affects the predictive association between the consensus target price and future returns. We find that returns implied by consensus target prices and realized future returns are positively correlated when dispersion is low, but they become highly negatively correlated when dispersion is high. Further analyses suggest that the differing effect of dispersion stems from incentive-driven staleness in price targets by some analysts after bad news. As a stock performs poorly and some analysts are slow to update their target prices, dispersion increases, and the consensus target price becomes too high. This has important implications for how consensus analyst target prices should inform investment decisions. We show that a hedge strategy taking a long (short) position in stocks with the highest predicted returns among stocks with the lowest (highest) dispersion earns more than 11% annually. Finally, we show that the negative correlation between consensus-based predicted returns and future realized returns for high-dispersion stocks exists mainly for stocks with high retail interest, suggesting that unsophisticated investors are misled by inflated target prices that are available freely online.
The Double-Edged Sword of Exemplar Similarity
Majid Majzoubi et al.
Organization Science, forthcoming
Abstract:
We investigate how a firm’s positioning relative to category exemplars shapes security analysts’ evaluations. Using a two-stage model of evaluation (initial screening and subsequent assessment), we propose that exemplar similarity enhances a firm’s recognizability and legitimacy, increasing the likelihood that it passes the initial screening stage and attracts analyst coverage. However, exemplar similarity may also prompt unfavorable comparisons with exemplar firms, leading to lower analyst recommendations in the assessment stage. We further argue that category coherence, distinctiveness, and exemplar typicality influence the impact of exemplar similarity on firm evaluation. Leveraging natural language processing (NLP) techniques to analyze a sample of 7,603 U.S. public firms from 1997 to 2022, we find robust support for our predictions. By highlighting the intricate role of strategic positioning vis-à-vis category exemplars in shaping audience evaluations, our findings have important implications for research on positioning relative to category exemplars, category viability, optimal distinctiveness, and security analysts.