A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.
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Results 230 resources
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Empirical asset pricing models with possibly unnecessary risk factors are increasingly common. Unfortunately, they can yield misleading statistical inferences. Unlike previous studies, we estimate the identified set of SDFs and risk prices compatible with a given model’s asset pricing restrictions. We also propose tests that detect problematic situations with economically meaningless SDFs unrelated to the test assets. Empirically, we estimate linear subspaces of SDFs compatible with popular extensions of the traditional and consumption versions of the CAPM, which are typically two-dimensional. Moreover, we often find that all the SDFs in those linear spaces are uncorrelated with the test assets’ returns.
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We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net‐zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.
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We study the speed with which investors learn about managers’ skills by examining how quickly investor and managers’ beliefs converge. After showing our measure proxies for the change in the dispersion of beliefs, we find that hedge fund investors learn as fast as suggested by Bayes’ rule. However, we find mutual fund investors learn more slowly than suggested by Bayes’ rule. Mutual fund investors’ slow learning is not due to the use of different performance measures, institutional frictions, or lack of sophistication, but could be due to a low payoff from learning. Our results indicate learning speed depends on financial participants’ incentives.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
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We study optimal government support following a rare disaster that creates heterogeneous firm liquidity needs. Firms’ increase in debt reduces their output due to moral hazard. Banks are subject to a minimum capital requirement that limits deposit insurance costs upon bad aggregate shocks. Without government support, firms’ moral hazard and banks’ funding frictions reinforce each other amplifying output losses. Optimal support is implemented with firm-specific transfers combined with the provision of aggregate risk insurance through a capital requirement relaxation and a public preferred equity stake in banks. Our results shed light on suboptimality features in the actual policy responses to Covid-19 lockdowns.
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Variable annuities, the largest liability of U.S. life insurers, are investment products containing long-dated minimum return guarantees. I show that guarantees with similar economic risks are treated differently by regulation and these differences impact insurers’ hedging behavior. When the regulatory regime recognizes certain risks, insurers start to hedge these risks in a substantial way. For some guarantees, this involves hedging both interest rate and equity market risks. However, for others, it involves hedging only equity market risk. As the regulatory regime still does not recognize the interest rate risk of all guarantees, insurers remain exposed to substantial interest rate risk.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
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We hypothesize that the well-documented negativity bias, the psychological tendency to asymmetrically emphasize negative over positive aspects, can help explain several financial market phenomena: why most individuals hold strongly bearish views of both short- and long-term equity return distributions, why individuals exhibit heterogeneous beliefs, and the stock market participation puzzle. Using variation in the perceived risk of mortality from the swine flu pandemic as our primary proxy for an individual's negativity bias, we find strong support for our hypothesis even when controlling for alternative mechanisms including optimism, risk aversion, ambiguity aversion, and anxiety.
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We examine decentralization of digital platforms through tokenization as an innovation to resolve the conflict between platforms and users. By delegating control to users, tokenization through utility tokens acts as a commitment device that prevents a platform from exploiting users. This commitment comes at the cost of not having an owner with an equity stake who, in conventional platforms, would subsidize participation to maximize the platform's network effect. This trade‐off makes utility tokens a more appealing funding scheme than equity for platforms with weak fundamentals. The conflict reappears when nonusers, such as token investors and validators, participate on the platform.
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I examine how the investment and financing of innovation are affected by the contractual allocation of intellectual property rights using a Federal Circuit ruling that strengthened firms’ property rights to employee patents. I find that treatment firms’ total debt-to-assets ratio and R&D spending increase by 18% and 9%, respectively, as the residual control over patents increases firms’ incentives to innovate. These effects are more pronounced when ex ante holdup exposure is high. Furthermore, I find a positive marginal effect of asset complementarity as it limits the decline in employee incentives. Consistently, I show that firms’ ex post asset complementarity improves.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
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This paper examines investors’ retirement savings allocation using a hand-collected dataset on 401(k) plans. We find that 83% of investors in our sample hold only 39% of total assets and follow a return-chasing strategy. In contrast, the remaining 17% of wealthy investors with relatively higher financial literacy follow CAPM alpha. This difference between the two investor groups explains why fund flows respond to returns at the plan level but to CAPM alpha at the aggregated fund level. Return-chasing by unwealthy investors is not optimal, as it significantly underperforms a strategy that passively invests in the existing funds in their plans.
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Wang, C. (2023). The Limits of Multi Dealer Platforms. Journal of Financial Economics, 149, 434–450.
On many important multi-dealer platforms, customers mostly request quotes from very few dealers. I build a model of multi-dealer platforms, where dealers strategically choose to respond to or ignore a request. If the customer contacts more dealers, each dealer responds with a lower probability and offers a stochastically worse price when responding. Dealers’ strategic avoidance of competition overturns the customer’s benefit from potentially receiving more quotes, worsening her best-overall price. In equilibrium, the customer contacts only two dealers. Multi-dealer platforms have limited ability to promote price competition: No design of information disclosure can improve the customer’s payoff above this outcome.
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