Trading Subjectivity
Moving the Goalposts? Mutual Fund Benchmark Changes and Relative Performance Manipulation
Kevin Mullally & Andrea Rossi
Review of Financial Studies, forthcoming
Abstract:
We analyze changes to mutual funds’ self-declared benchmarks using hand-collected data from funds’ prospectuses. Under existing rules, funds can freely change their benchmark indexes and, implicitly, the historical returns to which they compare their past performance. Funds exploit this loophole by adding (dropping) indexes with lower (higher) past returns, thereby materially improving the appearance of their benchmark-adjusted returns. High-fee funds, broker-sold funds, and funds experiencing poor performance and outflows are more likely to engage in this behavior. These funds subsequently attract additional flows despite continuing to underperform their peers.
Transmission Bias in Financial News
Khaled Obaid & Kuntara Pukthuanthong
University of Missouri Working Paper, August 2024
Abstract:
We examine how financial news is distorted as it spreads across different news outlets, akin to the "telephone game". Using a sample of exclusive articles from the Wall Street Journal, we use ChatGPT-4 to quantify transmission bias as competing news outlets retell these stories. We find strong evidence that retelling articles tend to be more opinionated and negative and less factual and appealing compared to the original story. Less specialized news outlets, the more time-lapse between the original story and its retelling, and the presence of competing narratives from other news outlets contribute to the distortion. Media distortion negatively predicts the following month's abnormal returns, suggesting that transmission bias creates a stronger and more pessimistic public perception of the original story. Transmission bias mostly influences retail traders, and leads to heightened disagreement among sophisticated and unsophisticated traders.
Memory Moves Markets
Constantin Charles
Review of Financial Studies, forthcoming
Abstract:
I show that memory-induced attention can distort prices in financial markets. I exploit rigid earnings announcement schedules to identify which firms are associated in investors’ memory. Firms with randomly overlapping earnings announcements are associated in memory because many investors experience them in the same context. Months later, when only one of the two firms announces earnings, this context is cued, and triggers the recall of the other, associated firm. On such days, I find that memory-induced attention leads to buying pressure in the associated firm’s stock. The strength of this effect varies as predicted by associative memory theory.
Timing Complex News to Target Attention
Vicente Cuñat & Moqi Groen-Xu
Management Science, forthcoming
Abstract:
Investors have limited and time-varying attention. These constraints are heterogeneous across investors, which can create asymmetric information and adverse selection problems. We show how firms take these constraints into account: They release harder-to-process news in periods when investor attention is higher. We use an institutional discontinuity within the U.S. corporate filing system to measure these effects. Filings before 5:30 p.m. become available immediately, whereas filings after 5:30 p.m. only become visible the next morning and attract less attention. Firms release longer and more complex news just before the cutoff, giving investors the longest possible period to absorb the information before markets open. Firms experience faster price convergence and more liquidity after precutoff news despite their complexity, which is consistent with the additional attention that they attract. We outline a framework in which the need for investors to spread their attention across different ideas induces firms to file their more complex filings at times when investor attention is higher. Our results are consistent with an equilibrium in which investors pay more attention to complex news and in which firms with complex news time them to target investor attention.
Categorical Processing in a Complex World
Marco Sammon, Thomas Graeber & Christopher Roth
Harvard Working Paper, November 2024
Abstract:
In real-world news environments, quantitative information is rarely presented in isolation; it is characterized through qualitative comparisons with various reference levels. Company earnings, for example, are commonly compared to analyst forecasts, previous earnings, and other benchmarks. We propose comparative noisy processing as a cognitive simplification strategy according to which people accurately incorporate qualitative comparisons to form category priors but integrate numerical signals imprecisely. In our framework, processing constraints induce excess sensitivity around category boundaries and insensitivity everywhere else. We study stock market reactions to about 200,000 earnings announcements, documenting that proxies for processing noise are associated with a more pronounced S shape around comparison points. The behavioral mechanism is corroborated by naturalistic experiments that exogenously manipulate processing constraints. We test two theories of what determines processing noise in the field. First, more common surprises might be processed with less noise as in models of efficient coding. We find that regions with more frequent surprises exhibit far higher return sensitivity and lower medium-term price corrections. Second, surprise may capture attention, leaving less capacity to process the numerical signal. We find that surprising qualitative realizations, e.g., a profit when a loss was expected, are associated with diminished sensitivity to quantitative information.
Is Information Risk Priced? New Evidence from Outer Space
Xianfeng Hao et al.
Management Science, forthcoming
Abstract:
Satellite images of the parking lots of U.S. retail firms provide information about the firms’ future earnings, and the limited access to these images produces information asymmetry between sophisticated and unsophisticated investors. We construct a cloud-based information risk (CIR) measure to capture this satellite information risk and investigate its asset pricing performance. We find that CIR positively predicts future stock returns of retail firms in the cross-section and that the predictability cannot be explained by weather reasons. We provide evidence that CIR indeed captures the information asymmetry among investors. The profitability of short selling is more pronounced on clear days. The return predictability of CIR is more pronounced in preannouncement periods. During cloudy days, parking lot traffic data from satellite images are harder to obtain, and the retail store sales estimated using parking lot data are noisier. We further show that high CIR is associated with low liquidity and that the decrease in liquidity purchases is greater than the decrease in liquidity sales. Our empirical analyses suggest that information asymmetry is priced and that CIR affects equity premiums through the liquidity channel, which are consistent with the theoretical predictions from a noisy rational expectations equilibrium model under imperfect competition.
Tyranny of the Personal Network: The Limits of Arm’s Length Fundraising in Venture Capital
Sabrina Howell, Dean Parker & Ting Xu
NBER Working Paper, October 2024
Abstract:
The central tension in securities regulation is between protecting investors and enabling broad capital formation. Focusing on VC fund managers, we study key tools of investor protection in private markets: enforcing relationship-based fundraising and restricting eligible investors. A new policy permitting public advertising is disproportionately used by less well-networked, underrepresented fund managers and is less sensitive to local conditions. Yet it has limited take-up because track record matters at arm’s length while strong networks matter in relationship financing; underrepresented managers more often have neither. Arm’s length fundraising also imposes costs to accessing the “crowd” and verifying investors, inducing negative signaling.