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Atlanta Marriott Marquis, L508
Hosted By:
American Economic Association
Asset Pricing
Paper Session
Saturday, Jan. 5, 2019 10:15 AM - 12:15 PM
- Chair: Caleb Stroup, Davidson College
Carbon Risk
Abstract
The costs of addressing climate change will be borne by firms through their investments, supply-chains, and products and services. Financial markets play a role in aggregating firm-level information on the costs of the transition but also on pricing these risks. We construct a carbon risk factor for 1,600 global firms with carbon risk data from four major ESG databases. This factor can be used as a straightforward measure of carbon beta absent firm-specific carbon emissions information. We compute the carbon beta of 39,000 global firms. Our factor can be used by firms, regulators and investors to better understand carbon risk.Does Perception Matter in Asset Pricing? Modeling Returns and Volatility Jumps Using Twitter-Based Sentiment Indices
Abstract
Do consumer and investor perceptions matter in asset pricing? I find that, contrary to the literature's expectations, it is possible to forecast high-frequency stock returns and volatility jumps using consumer and investor sentiment indicators. Using tweets that I scraped from Twitter, I perform textual analysis to construct daily sentiment indices. While other scholars have relied on third-party companies like Stocktwits to complete these tasks, doing so reduces transparency and limits the potential for customization. The sentiment indices I constructed are numerical scores, not dichotomous variables, which allows me to control for sentiment strength (e.g., good vs. great) and not just positive/negative overall feelings. Results indicate that sentiment indices can not only be used to obtain out-of-sample forecasts of daily returns, but can also forecast volatility jumps. Using a simple Markov-switching framework, I find that, as overall sentiments shift from positive to negative (or vice versa), volatility jumps occur.Geographic Spillover of Dominant Firms' Shocks
Abstract
This paper shows that productivity shocks to the 100 largest U.S. firms (by revenue) contain systematic information. Specifically, shocks to the top-100 firms predict future shocks to geographically close firms. Intra-sector trade links are an important economic channel for spillover effects. However, these spillovers are not restricted to firms' trade links only. Knowledge externalities and state income tax payments are other economic channels through which shocks propagate. Market participants do not fully incorporate the information contained in shocks to the top-100 firms. Consequently, a trading strategy that exploits the slow diffusion of information generates an annual risk-adjusted return of 5.4%.Restrictions on Asset-Price Movements Under Rational Expectations: Theory and Evidence
Abstract
How restrictive is the assumption of rational expectations in asset markets? We provide two contributions to address this question. First, we derive restrictions on the admissible variation in asset prices in a general class of rational-expectations equilibria. The challenge in this task is that asset prices reflect both beliefs and preferences. We gain traction by considering market-implied, or risk-neutral, probabilities of future outcomes, and we provide a mapping between the variation in these probabilities and the minimum curvature of utility — or, more generally, the slope of the stochastic discount factor — required to rationalize the marginal investor’s beliefs. Second, we implement these bounds empirically using S&P 500 index options. We find that very high utility curvature is required to rationalize the behavior of risk-neutral beliefs, and in some cases, no stochastic discount factor in the class we consider is capable of rationalizing these beliefs. This provides evidence of overreaction to new information relative to the rational benchmark. We show further that this overreaction is strongest for beliefs over prices at distant horizons, and that our findings cannot be explained by factors specific to the option market.JEL Classifications
- G1 - General Financial Markets