The Cross Section of Monetary Policy Announcement Premium (with Hengjie Ai, Xuhui Pan and Lai Xu) [Online Appendix] [Readme] [Data] [Codes][Slides], 2022, Journal of Financial Economics, 143(1), 247-276.
Abstract: We show that monetary policy announcements require a significant risk compensation in the cross-section of equity returns. Empirically, we use the expected reduction in implied variance upon FOMC announcements to measure the sensitivity of stock returns with respect to monetary policy announcement surprises. A long-short portfolio formed on our monetary policy sensitivity measure produces a statistically and economically significant average announcement-day return of 31.67 bps and this pattern is robust to controlling for standard risk factors. We develop an equilibrium model to account for the dynamics of implied variances and the cross-section of excess returns on expected variance reduction sorted portfolios around FOMC announcements.
Presented at: Tsinghua University PBCSF, University of Hong Kong, Federal Reserve Board*, University of Southern California*, University of Houston*, Tulane University*, Midwest Finance Association 2020, European Finance Association 2020*, Canadian Derivatives Institute Conference 2020*, Western Finance Association 2019*, Adam Smith Workshop 2020 (accepted), 6th University of Connecticut Finance 2020 (accepted).
Abstract: We show that monetary policy announcements require a significant risk compensation in the cross-section of equity returns. Empirically, we use the expected reduction in implied variance upon FOMC announcements to measure the sensitivity of stock returns with respect to monetary policy announcement surprises. A long-short portfolio formed on our monetary policy sensitivity measure produces a statistically and economically significant average announcement-day return of 31.67 bps and this pattern is robust to controlling for standard risk factors. We develop an equilibrium model to account for the dynamics of implied variances and the cross-section of excess returns on expected variance reduction sorted portfolios around FOMC announcements.
Presented at: Tsinghua University PBCSF, University of Hong Kong, Federal Reserve Board*, University of Southern California*, University of Houston*, Tulane University*, Midwest Finance Association 2020, European Finance Association 2020*, Canadian Derivatives Institute Conference 2020*, Western Finance Association 2019*, Adam Smith Workshop 2020 (accepted), 6th University of Connecticut Finance 2020 (accepted).
Ambiguity, Information Processing, and Financial Intermediation (with Kenneth Kasa and Yulei Luo) Revise & Resubmit at the Journal of Economic Theory
Abstract: This paper incorporates ambiguity and information processing constraints into the He and Krishnamurthy (2012) model of intermediary asset pricing. Financial intermediaries possess greater information processing capacity than households. In response, households optimally choose to delegate their investment decisions. The contractual relationship between households and intermediaries is subject to a moral hazard friction, which results in a financial constraint. We show that ambiguity aversion not only amplifies households' incentives to delegate but also tightens the financial constraint. The calibrated model can quantitatively explain both the unconditional and time-varying moments of observed asset prices, while endogenously generating an empirically consistent crisis frequency.
Presented at: SFS Cavalcade Asia-Pacific 2019, China International Conference in Macroeconomics 2019, Summer Institute of Finance Conference 2019, American Economic Association Poster Session 2019, Asian Meeting of the Econometric Society 2019, 31st Australasian Finance and Banking Conference 2018, New York University, University of Hong Kong.
Abstract: This paper incorporates ambiguity and information processing constraints into the He and Krishnamurthy (2012) model of intermediary asset pricing. Financial intermediaries possess greater information processing capacity than households. In response, households optimally choose to delegate their investment decisions. The contractual relationship between households and intermediaries is subject to a moral hazard friction, which results in a financial constraint. We show that ambiguity aversion not only amplifies households' incentives to delegate but also tightens the financial constraint. The calibrated model can quantitatively explain both the unconditional and time-varying moments of observed asset prices, while endogenously generating an empirically consistent crisis frequency.
Presented at: SFS Cavalcade Asia-Pacific 2019, China International Conference in Macroeconomics 2019, Summer Institute of Finance Conference 2019, American Economic Association Poster Session 2019, Asian Meeting of the Econometric Society 2019, 31st Australasian Finance and Banking Conference 2018, New York University, University of Hong Kong.
Information-Driven Volatility (with Hengjie Ai and Lai Xu) [Slides] Revise & Resubmit at the Journal of Finance
Abstract: Standard asset pricing models with stochastic volatility predict a robust positive relationship between past realized volatility and future expected returns. Empirical work typically finds this relationship to be negative. We develop an asset pricing model where stock market volatility dynamics are driven by information. We show that under strong generalized risk sensitivity of preferences, information-driven volatility induces a negative correlation between past realized volatility and future expected returns. Using FOMC announcements and stock market jump days to identify information events, we provide empirical evidence for the unique implications of the information-driven volatility channel.
Presented at: Macro Finance Society 2023, SITE New Frontiers in Asset Pricing 2023, Western Finance Association 2022, Society for Economic Dynamics 2022*, Midwest Finance Association 2022, Canadian Derivatives Institute 2021*, 8th SAFE Asset Pricing Workshop*, 2021 Conference and JEDC Special Issue on Markets and Economies with Information Frictions, China International Risk Forum 2021, Sun Yat-sen University, University of Washington*, UT Dallas*, University of Minnesota*, University of Wisconsin–Madison*, Tsinghua University PBCSF*, University of Oklahoma*, University of Manitoba*.
Abstract: Standard asset pricing models with stochastic volatility predict a robust positive relationship between past realized volatility and future expected returns. Empirical work typically finds this relationship to be negative. We develop an asset pricing model where stock market volatility dynamics are driven by information. We show that under strong generalized risk sensitivity of preferences, information-driven volatility induces a negative correlation between past realized volatility and future expected returns. Using FOMC announcements and stock market jump days to identify information events, we provide empirical evidence for the unique implications of the information-driven volatility channel.
Presented at: Macro Finance Society 2023, SITE New Frontiers in Asset Pricing 2023, Western Finance Association 2022, Society for Economic Dynamics 2022*, Midwest Finance Association 2022, Canadian Derivatives Institute 2021*, 8th SAFE Asset Pricing Workshop*, 2021 Conference and JEDC Special Issue on Markets and Economies with Information Frictions, China International Risk Forum 2021, Sun Yat-sen University, University of Washington*, UT Dallas*, University of Minnesota*, University of Wisconsin–Madison*, Tsinghua University PBCSF*, University of Oklahoma*, University of Manitoba*.
Announcements, Expectations, and Stock Returns with Asymmetric Information [Slides] Reject & Resubmit at the Journal of Monetary Economics
Awards: 2021 Northern Finance Association Meetings Best Ph.D Student Paper Award
2020 WFA Cubist Systematic Strategic Ph.D Candidate Award for Outstanding Research
Abstract: Revisions of consensus forecasts for macroeconomic variables positively predict announcement-day forecast errors, whereas stock market returns on forecast revision days negatively predict announcement-day returns. A dynamic noisy rational expectations model with periodic macroeconomic announcements quantitatively accounts for these findings. Under asymmetric information, informed investors' forecast revisions positively predict forecast errors of the uninformed, causing average beliefs to underweight new information and positively predict belief errors. Additionally, stock prices are partially driven by noise. Noise impact accumulates into stock prices on revision days but gets corrected upon announcements. Therefore, price changes on revision days negatively predict announcement-day returns.
Presented at: American Economic Association 2021, Western Finance Association 2020, Financial Intermediation Research Society Conference 2021, Northern Finance Association 2021, Econometric Society World Congress 2020, European Economic Association 2020, European Finance Association Poster 2020, China International Risk Forum 2020, Carnegie Mellon University, New York Fed, University of Toronto, University of California San Diego, University of Wisconsin–Madison, Boston University, University of Warwick, Central European University, Bank for International Settlements, Singapore Management University, New York University Shanghai, City University of London Cass Business School, Erasmus University Rotterdam, CEIBS, University of Exeter, Renmin University of China, Fudan University, Shanghai JiaoTong University, University of Hong Kong, WHU.
Awards: 2021 Northern Finance Association Meetings Best Ph.D Student Paper Award
2020 WFA Cubist Systematic Strategic Ph.D Candidate Award for Outstanding Research
Abstract: Revisions of consensus forecasts for macroeconomic variables positively predict announcement-day forecast errors, whereas stock market returns on forecast revision days negatively predict announcement-day returns. A dynamic noisy rational expectations model with periodic macroeconomic announcements quantitatively accounts for these findings. Under asymmetric information, informed investors' forecast revisions positively predict forecast errors of the uninformed, causing average beliefs to underweight new information and positively predict belief errors. Additionally, stock prices are partially driven by noise. Noise impact accumulates into stock prices on revision days but gets corrected upon announcements. Therefore, price changes on revision days negatively predict announcement-day returns.
Presented at: American Economic Association 2021, Western Finance Association 2020, Financial Intermediation Research Society Conference 2021, Northern Finance Association 2021, Econometric Society World Congress 2020, European Economic Association 2020, European Finance Association Poster 2020, China International Risk Forum 2020, Carnegie Mellon University, New York Fed, University of Toronto, University of California San Diego, University of Wisconsin–Madison, Boston University, University of Warwick, Central European University, Bank for International Settlements, Singapore Management University, New York University Shanghai, City University of London Cass Business School, Erasmus University Rotterdam, CEIBS, University of Exeter, Renmin University of China, Fudan University, Shanghai JiaoTong University, University of Hong Kong, WHU.
Information Acquisition and the Pre-Announcement Drift (with Hengjie Ai and Ravi Bansal) [Slides]
Abstract: We present a dynamic Grossman-Stiglitz model with endogenous information acquisition to explain the pre-FOMC announcement drift. Because FOMC announcements reveal substantial information about the economy, investors' incentives to acquire information are particularly strong days ahead of the announcements. Information acquisition partially resolves the uncertainty for uninformed traders. Under generalized risk sensitive preferences (Ai and Bansal, 2018), resolution of uncertainty is associated with realizations of risk premium, generating a pre-FOMC announcement drift. Because our theory does not rely on leakage of information, it can simultaneously explain the high average return and the low realized volatility during the pre-FOMC announcement period.
Presented at: Chicago Booth Asset Pricing Conference, UNC Junior Conference, NBER Summer Institute Capital Markets and the Economy, Adam Smith Workshop 2022, 6th Annual Young Scholars Finance Consortium, 7th Annual University of Connecticut Finance Conference, Western Finance Association 2021, European Finance Association 2021, Northern Finance Association 2021, Society for Economic Dynamics 2021*, China International Conference in Finance 2021, China International Conference in Macroeconomics 2021, Midwest Finance Association 2021*, Atlantic Fed*, UCL*.
Abstract: We present a dynamic Grossman-Stiglitz model with endogenous information acquisition to explain the pre-FOMC announcement drift. Because FOMC announcements reveal substantial information about the economy, investors' incentives to acquire information are particularly strong days ahead of the announcements. Information acquisition partially resolves the uncertainty for uninformed traders. Under generalized risk sensitive preferences (Ai and Bansal, 2018), resolution of uncertainty is associated with realizations of risk premium, generating a pre-FOMC announcement drift. Because our theory does not rely on leakage of information, it can simultaneously explain the high average return and the low realized volatility during the pre-FOMC announcement period.
Presented at: Chicago Booth Asset Pricing Conference, UNC Junior Conference, NBER Summer Institute Capital Markets and the Economy, Adam Smith Workshop 2022, 6th Annual Young Scholars Finance Consortium, 7th Annual University of Connecticut Finance Conference, Western Finance Association 2021, European Finance Association 2021, Northern Finance Association 2021, Society for Economic Dynamics 2021*, China International Conference in Finance 2021, China International Conference in Macroeconomics 2021, Midwest Finance Association 2021*, Atlantic Fed*, UCL*.
Uncertainty and Incentives: Theory and Evidence on Mutual Funds (with Erica Jiang, Laura Starks and Sophia Sun)
Abstract: We demonstrate that economic uncertainty influences investment decisions in the mutual fund industry through incentives. Empirically, we show that during periods of high uncertainty, the flow-performance sensitivity decreases, and fund managers engage in less active portfolio management. The evidence aligns with our optimal contracting model with moral hazard, where increased uncertainty leads investors to reduce fund flows based on performance, effectively reducing the risk borne by risk-averse managers. Moreover, increased uncertainty also diminishes the informativeness of performance, resulting in less effort in active management by fund managers. Our results imply that heightened uncertainty reduces welfare and investment efficiency not only through the traditional real options channel but also because it results in less effective incentive provisions in asset management.
Abstract: We demonstrate that economic uncertainty influences investment decisions in the mutual fund industry through incentives. Empirically, we show that during periods of high uncertainty, the flow-performance sensitivity decreases, and fund managers engage in less active portfolio management. The evidence aligns with our optimal contracting model with moral hazard, where increased uncertainty leads investors to reduce fund flows based on performance, effectively reducing the risk borne by risk-averse managers. Moreover, increased uncertainty also diminishes the informativeness of performance, resulting in less effort in active management by fund managers. Our results imply that heightened uncertainty reduces welfare and investment efficiency not only through the traditional real options channel but also because it results in less effective incentive provisions in asset management.
(*presented by co-authors)