Market Factors
Price Agnostic Demand
Samuel Hartzmark & Abigail Sussman
University of Chicago Working Paper, April 2025
Abstract:
We document that investors lack conviction as to what the market price should be (absent seeing it). This is at odds with standard asset pricing models, which assume that investors are uncertain about the future, but understand what the current market price should be and whether the actual price is different from this. We find that investors are unable to identify extreme deviations from actual market prices and are thus willing to accept a wide range of prices as the actual market price, even holding information about fundamentals constant. Professional return forecasts, trading strategies and investment advice are largely agnostic about the market price level. This suggests that many decisions are made without conviction as to the prevailing market price level, a channel we call price agnostic demand. This can help explain a number of puzzling empirical patterns in asset prices.
Who Negotiates? The Political Psychology of Price Negotiations
Archer Yue Pan & Manoj Thomas
Personality and Social Psychology Bulletin, forthcoming
Abstract:
Price negotiation is often a zero-sum interaction where one party’s gain is another’s loss. In such contexts, a buyer’s willingness to negotiate can depend on the perceived justifiability of negotiation. This research examines how political ideology shapes these perceptions. Two archival studies (N = 56,615) and four preregistered studies (N = 3,157) show that conservative buyers are more likely to negotiate prices for houses and used cars. Conservatives also hold stronger beliefs that buyers should negotiate prices regardless of the seller’s identity -- be it a professional dealer, an ordinary seller, a stranger, or a friend. This heightened propensity to justify price negotiation is rooted in conservatives’ endorsement of free-market ideology, which motivates and even moralizes the pursuit of economic self-interest in marketplace interactions. These findings offer a nuanced account of interactions in the marketplace, demonstrating that marketplace behaviors are influenced not only by economic considerations but also by ideological beliefs.
Social Audience Size as a Reference Point: Evidence from a Field Experiment
Xingchen Xu et al.
Management Science, forthcoming
Abstract:
In the dynamic landscape of the digital economy, social trading platforms are experiencing rapid growth. Our study delves into the impact of changes in social audience size -- measured by the number of followers -- on traders’ performance and behaviors. Utilizing data from a company-led randomized field experiment conducted on a prominent cryptocurrency social trading platform, we unearth intriguing findings. Traders garnering increased social audience size exhibit tendencies to trade more frequently, utilize higher leverage, and, surprisingly, attain poorer performance. Notably, these adverse effects intensify among traders who previously excelled, suggesting a link to overconfidence. Interestingly, our research uncovers a reference point effect associated with social audience size. Removing accumulated social audience size does not alleviate the negative consequences; instead, they persist. Additionally, we observe an extra adverse effect when traders experience a reduction in the digit magnitude of follower counts, supporting our hypothesis about social audience size serving as a reference point. Our study carries significant implications for the design of social trading platforms. It serves as a crucial reminder for both traders and platform managers to carefully navigate the interplay between social audience size dynamics and trading decisions.
Generative AI Use by Capital Market Information Intermediaries: Evidence from Seeking Alpha
Mark Bradshaw et al.
Harvard Working Paper, April 2025
Abstract:
We study the use of generative AI for firm-specific financial analysis on the Seeking Alpha platform. We find that, after the initial launch of ChatGPT in November 2022, the share of AI-generated articles rose sharply to 13.4% of all articles, then declined in late 2023 after Seeking Alpha equated the use of AI to plagiarism and announced a prohibition on its use. Compared to human articles, AI articles elicit smaller trading volume and abnormal return responses, suggesting they are less informative to capital market participants. However, authors who adopt AI exhibit increased productivity, publishing more articles and covering more new firms than non-adopters. Moreover, the expansion of AI coverage is associated with improved liquidity for historically undercovered firms, but provides no incremental benefit for firms that are already well-covered. Our findings suggest that while AI-generated articles are currently perceived as less informative than human-written articles, their comparatively low cost may enable broader coverage of, and capital market benefits for, firms traditionally overlooked by capital market intermediaries.
Betting on my enemy: Insider trading ahead of hedge fund 13D filings
Truong Duong, Shaoting Pi & Travis Sapp
Journal of Corporate Finance, July 2025
Abstract:
Corporate insiders often become aware of hedge fund attention prior to a 13D filing. We find abnormal buying activity by insiders in the months leading up to hedge fund 13D filings. Whereas 13D announcement abnormal returns are 7.72%, profits to insiders who buy average 12.09%. Insider buying is not linked to common firm characteristics that predict activist targeting. Our findings indicate that insiders are benefiting from private knowledge that their firm has become the focus of hedge fund activism, and sometimes this knowledge comes directly from the activist. However, insiders largely refrain from trading when there is formal communication with the activist. Profits to insiders who buy when there are no talks prior to the 13D filing are 14.49%, triple the amount for insiders who have had early talks with the hedge fund. Insider trading is linked to indicators of poor corporate culture, but not related to outcomes of activism campaigns.
Do hedge funds still manipulate stock prices?
Xinyu Cui & Olga Kolokolova
Journal of Corporate Finance, June 2025
Abstract:
We find no evidence of stock price manipulation by hedge funds from 2011 to 2019, despite confirming the portfolio-pumping pattern documented between 2000 and 2010. In the more recent period, the magnitude, frequency, and persistence of manipulation by hedge funds appear to have declined. This decrease is linked to reduced rewards, as fund flows no longer react positively to the end-of-quarter returns of hedge fund portfolios. Proactive regulatory actions, measured by SEC litigation cases involving hedge funds, and increased press attention to hedge fund fraud also contribute to reduced manipulation during both periods.
The value of privacy and the choice of limited partners by venture capitalists
Rustam Abuzov, Will Gornall & Ilya Strebulaev
Journal of Financial Economics, July 2025
Abstract:
We study how information disclosure concerns shape the choice of limited partners (LPs) by venture capitalists (VCs). Late-2002 court rulings prevented public LPs from providing confidentiality to investment managers. The best-performing VCs, but not other managers, responded by excluding public LPs from their new funds. Lost access reduced public LP returns by $1.6 billion relative to $14 billion of their VC commitments. Legislation reducing disclosure, contracts limiting information access, and added fund-of-funds intermediaries helped restore access. These changes focused on protecting portfolio company information, highlighting the importance of proprietary information for VC investing and the potential costs of transparency.
Combining Proxies and Narrative Sign Restrictions: Revisiting the Effects of Technology Shocks
Yang Yang & Ren Zhang
Journal of Applied Econometrics, forthcoming
Abstract:
This paper proposes a novel integration of the proxy structural vector autoregression approach with narrative sign restrictions to identify multiple shocks with multiple instrumental variables. We show that this combination leads to informative inferences on shock effects through both analytical demonstrations and Monte Carlo simulations. Applying this method to identify anticipated and unanticipated technology shocks, we find that anticipated shocks account for over 45% of stock price variation but only about 13% of total factor productivity (TFP) fluctuations. The majority of TFP variations are driven by unanticipated technology shocks and measurement errors.
The Structural Transformation of Innovation
Diego Comin, Danial Lashkari & Martí Mestieri
NBER Working Paper, May 2025
Abstract:
We document the structural transformation of innovation using historical patent data since the 1850s, along with R&D expenditure and TFP growth for the post-war period. Over time, innovation has shifted from agricultural sectors to manufacturing, and, more recently, to services. We develop and quantify a multi-sector semi-endogenous growth model of structural change in innovation and production, incorporating the classical demand-pull and technology-push drivers of innovation. Sectors differ in their innovation technologies, and the extent to which they benefit from knowledge spillovers (technology-push). Nonhomothetic demand shifts the market shares toward income-elastic sectors along the growth process (demand-pull). A calibrated version of our model replicates the structural transformations of innovation and production observed in the US data. Using the model, we evaluate the future impact of Baumol’s disease on aggregate productivity and find it to be minimal. Our results suggest that aggregate productivity growth may recover in the coming decades as the service sector becomes increasingly innovation-driven.Market factors