Research
Research
Journal of Financial Economics, Volume 138, Issue 3, Pages 754-776 (December 2020).
We consider an economy populated by investors with heterogeneous preferences and beliefs who receive non-pledgeable labor incomes. We study the effects of collateral constraints that require investors to maintain sufficient pledgeable capital to cover their liabilities. We show that these constraints inflate stock prices, give rise to clusters of stock return volatilities, and produce spikes and crashes in price-dividend ratios and volatilities. Furthermore, the mere possibility of a crisis significantly decreases interest rates and increases Sharpe ratios. The stock price has a large collateral premium over non-pledgeable incomes. Asset prices are in closed form, and investors survive in the long run.
European Finance Association Best Conference Paper Award, 2016
SFS Finance Cavalcade Best Paper Award in Asset Pricing, 2016
European Winter Finance Symposium Best Paper Prize in Memory of S. Bhattacharya, 2016
Revise and resubmit, Journal of Finance
This paper studies the dynamics of information acquisition and uncertainty in a noisy rational expectations model. Investors choose to acquire most information at times when uncertainty and risk premia are high; this choice feeds back and endogenously reduces subsequent uncertainty. Within the model, uncertainty is measured directly from risk-neutral variance---analogous to the VIX index---so this translates into the concrete prediction that risk-neutral variance mean-reverts rapidly following spikes in volatility, as is observed empirically. The cyclicality of information acquisition depends on the skewness of the underlying asset: if the market is negatively skewed, market-level information acquisition is countercyclical. Conversely, information acquisition and risk premia are high following good news for positively skewed assets such as individual stocks, which gives rise to momentum in the stock market.
Finance Theory Group Best Paper in Finance Theory on the Job Market, 2019
We develop a model featuring risk-averse investors with multidimensional heterogeneous beliefs on the content of news and the frequency of news arrivals. They trade both derivatives and the underlying asset, with those that are proven correct ex-post accumulating more wealth and exerting a greater influence on asset prices. When there is no disagreement about news arrival rate, asset prices and realized volatility can rise simultaneously, as extreme optimists and pessimists suffer substantial wealth losses amid intense market swings. In contrast, when disagreement over news arrival rate is pronounced, “no news” becomes positive for risky asset valuations, as volatility sellers gain wealth and extreme payoffs carry less weight in the market consensus.
Firms are increasingly adopting predictive artificial intelligence to improve decision-making by combining advanced data analysis with human judgment. While AI enhances managers' ability to predict project success probabilities, we show that its adoption can unintentionally backfire, leading to suboptimal decisions that diminish shareholder profits. Specifically, the additional information provided by AI raises the likelihood that the manager's estimate of the project's success probability falls within a range where the sensitivity of managerial compensation to their decision decreases significantly, incentivizing the pursuit of private benefits over shareholder interests. To address this, we propose AI-contingent contracts that link managerial compensation to AI predictions, thereby mitigating AI-induced agency conflicts. Our study highlights the necessity of updated corporate governance structures to effectively navigate the challenges posed by AI-augmented decision-making.
The paper develops a model of bubbles that can be taken to the data and explain the behavior of asset prices and their statistics. We depart from the rational expectations framework and assume that investors are only boundedly rational. They observe the price process, but do not fully understand how its volatility and expected returns are determined in equilibrium. Investors learn about the market by looking at past prices. When they observe unexpectedly high returns, they infer that the asset must currently have a high Sharpe ratio, and therefore, allocate a higher share of their wealth to the asset, further increasing the asset price. The interaction of this feedback effect with investors' wealth effect determines the price dynamics and evolution of investors' beliefs in the model. We fit the model to cryptocurrency markets and show that it can successfully explain many empirical facts in these markets.
We study the disagreement of major foreign exchange (FX) dealers using proprietary survey data on dealers' price quotes of short- and long-tenor currency derivatives. Dispersion among dealers is the highest at short tenors where heterogeneous information is of great relevance, and much lower at long tenors where heterogeneous beliefs dominate. This downward sloping term structure of dealer dispersion is most steep for risk reversals that capture asymmetric tail risk, and flattens considerably for forwards, strangles, and straddles that capture mean, symmetric tail risk, and volatility. Furthermore, dealer dispersion on risk reversals positively predicts currency returns in the cross section, with strong economic and statistical significance at short horizons but weak significance at long horizons. Dealer dispersion on the other three FX derivatives has no return predictive power.
Investors use a range of imperfectly observed predictor variables to form dividend expectations and update the sensitivities of dividend growth to these predictors. Investors are more certain about their prediction model after large predicted dividend changes. Significant dividend surprise, however, raises model uncertainty and volatility of future returns. Unpredicted comovement of stocks with similar characteristics increases these stocks’ risk premia. Information acquisition tends to focus on variables useful for short-term forecasts, allowing the buildup of model uncertainty for other predictors. These learning behaviors also generate correlated movements of expectation and subjective uncertainty of future dividend growth, inducing correlations in cash flow and discount rate components of asset returns.