Immaterial Information
Tangibility bias in investment risk judgments
Özgün Atasoy et al.
Organizational Behavior and Human Decision Processes, July 2022
Abstract:
The most popular ways of holding wealth include tangible investments such as real estate and gold, and intangible investments such as stocks and mutual funds. Five experiments revealed a tangibility bias whereby the tangibility of an investment or tangibility cues linked to an investment provides a false sense of financial safety. When focusing on avoiding risk, investors indicated a higher willingness to sell the stocks of companies that invest in intangible versus tangible assets (Study 1). The greater perceived permanence of tangible versus intangible assets appeared to underlie the difference in market risk assessments. Respondents judged the same asset as riskier when it was framed as intangible (Study 2), and differences in perceived permanence mediated this effect. Increasing perceived permanence independently of tangibility led to lower market risk assessments of commodity futures (Study 3). Tangibility prompts that leave asset tangibility unchanged were sufficient to lower risk judgments (studies 4 and 5). The differences in market risk assessments were not due to a general preference for tangible assets (Study 4) or differences in familiarity, complexity, or understanding of the asset types (studies 2 and 5).
Predictably Bad Investments: Evidence from Venture Capitalists
Diag Davenport
University of Chicago Working Paper, July 2022
Abstract:
Do institutional investors invest efficiently? To study this question I combine a novel dataset of over 16,000 startups (representing over $9 billion in investments) with machine learning methods to evaluate the decisions of early-stage investors. There is an inference problem because I only observe outcomes for the subset of startups that are funded; I address this one-sided inference problem by quantifying the returns forgone by investing in a given startup (observed) relative to an investment available on a public market (counterfactual). By comparing investor choices to an algorithm’s predictions, I show that up to half of the investments were predictably bad — based on information known at the time of investment, the predicted return of the investment was less than readily available outside options. The cost of these poor investments is 1,000 basis points, totaling over $900 million in my data. I provide suggestive evidence that over-reliance on the founders’ background is one mechanism underlying these choices. Together the results suggest that high stakes and firm sophistication are not sufficient for efficient use of information in capital allocation decisions.
The Economic Limits of Bitcoin and Anonymous, Decentralized Trust on the Blockchain
Eric Budish
University of Chicago Working Paper, June 2022
Abstract:
Satoshi Nakamoto invented a new form of trust. This paper presents a three equation argument that Nakamoto’s new form of trust, while undeniably ingenious, is extremely expensive: the recurring, 'flow' payments to the anonymous, decentralized compute power that maintains the trust must be large relative to the one-off, 'stock' benefits of attacking the trust. This result also implies that the cost of securing the trust grows linearly with the potential value of attack — e.g., securing against a $1 billion attack is 1000 times more expensive than securing against a $1 million attack. A way out of this flow-stock argument is if both (i) the compute power used to maintain the trust is non-repurposable, and (ii) a successful attack would cause the economic value of the trust to collapse. However, vulnerability to economic collapse is itself a serious problem, and the model points to specific collapse scenarios. The analysis thus suggests a 'pick your poison' economic critique of Bitcoin and its novel form of trust: it is either extremely expensive relative to its economic usefulness or vulnerable to sabotage and collapse.
Do retail traders destabilize financial markets? An investigation surrounding the COVID-19 pandemic
Ahmed Baig et al.
Journal of Banking & Finance, forthcoming
Abstract:
Existing research suggests that retail trading is associated with volatility in financial markets. To extend the literature, we study the dynamic effects of retail trading on volatility during the COVID-19 pandemic. Using marketable retail trades identified from the Boehmer et al. (2021) algorithm and novel empirical methods discussed in Jordá (2005), we document a negative, persistent impact of retail trading on the stability of stock prices that is particularly stronger during the pandemic than during the pre-pandemic period. These results highlight how periods of crises – like the pandemic – affect the destabilizing influence of retail trading. To provide additional evidence, we replicate our empirical exercise during the 2008-09 financial crisis. Consistent with the COVID-19 period, we again find that retail trading leads to more volatility during the financial crisis vis-á-vis the pre-crisis period. These results again support the idea that periods of crises strengthen the link between retail trading and volatility.
Hedge Funds and Public Information Acquisition
Alan Crane, Kevin Crotty & Tarik Umar
Management Science, forthcoming
Abstract:
Hedge funds actively acquire publicly available financial disclosures. Funds acquiring such information subsequently earn 1.5% higher annualized abnormal returns than nonacquirers. Trades by the same fund in the same quarter are more profitable when accompanied by public information acquisition. Acquiring public filings is relatively less profitable when macrouncertainty is high. Funds employ a wide range of strategies for acquiring public filings. Those that systematically scrape large volumes of information, specialize in certain filing types, acquire filings with more content changes, or access information immediately outperform other funds.
Face-to-face Social Interactions and Local Informational Advantage
Robin Young-hye Lee
University of Southern California Working Paper, June 2022
Abstract:
To identify the causal impact of face-to-face communication on local informational advantage, I exploit variation in social interactions driven by COVID-19 lockdowns. Using stay-at-home orders, SafeGraph footprint data, and the number of Covid cases to identify constraints on in-person interactions, I find that during lockdowns, mutual fund managers’ performance on local stocks declined relative to non-local stocks. I show that this effect is driven by changes in local managers’ timing of investments, not variation in local stock returns. These results show that fund managers rely on rich information shared through face-to-face social interactions to create an informational advantage.
Exchange-Traded Funds and Real Investment
Constantinos Antoniou et al.
Review of Financial Studies, forthcoming
Abstract:
We investigate the link between exchange-traded funds and real investment. Cross-sectionally, higher ETF ownership is associated with an increased sensitivity of real investment to Tobin's q and a heightened ability of stock returns to forecast future earnings. Inclusion of stocks in industry ETFs enhances investment-q sensitivity and implies greater incorporation of earnings information into prices prior to public releases. Greater nonmarket ETF ownership leads to increased (reduced) reliance of real investment on own (peers') stock prices. Overall, the evidence is consistent with ETFs positively affecting real investment efficiency via greater flows of information.
Risking or Derisking: How Management Fees Affect Hedge Fund Risk-Taking Choices
Chengdong Yin & Xiaoyan Zhang
Review of Financial Studies, forthcoming
Abstract:
Hedge fund managers’ risk-taking choices are influenced by their compensation structure. We differ from most studies that focus on incentive fees and the high-water mark by examining how management fees affect managers’ risk-taking. Our simple model shows that managers’ risk-taking is negatively related to their future management fees. Using fund-level data, we find that future management fees are the dominant component of managers’ total compensation. When the contribution of future management fees increases, managers reduce risk-taking to increase survival probabilities. Moreover, funds with higher decreasing returns to scale are more sensitive to future management fees and reduce risk-taking even more.
Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability
Doron Avramov, Si Cheng & Lior Metzker
Management Science, forthcoming
Abstract:
This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs because of high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk.