Working papers:
How Political Winds Shape Growth Horizons: Firm-Level Political Risk and Equity Duration
(Solo-authored, job market paper)
This study examines the pivotal role of firm-level political risk in affecting firm-level equity duration, revealing a significant and negative impact. By building a theoretical model, I propose that firm-level equity duration is intrinsically tied to the price-dividend ratio and is primarily influenced by political risk through the cash flow channel. To ensure robustness, I implement placebo test, instrumental variable analysis, and propensity score matching. The findings indicate that the relationship between firm-level political risk and equity duration remains unaffected by aggregate economic policy uncertainty level. Notably, periods of Democratic leadership appear to mitigate the adverse effects of political risk on equity duration. Additionally, the study documents that firm lobbying activities and institutional investor ownership are effective in counteracting the negative impacts of firm-level political risk on equity duration.
Presentations: Annual Meeting of the FMA, Dallas, October 2024 (scheduled).
Identified as a semifinalist for one of six best paper awards to be given at the 2024 FMA Annual Meeting.
The Equity Term Structures in Equilibrium Models (Solo-authored)
Listed on SSRN's Top Ten download list for: ERN: Risk Premiums (Topic) in Feburary 2023.
I address the importance of non-Gaussian features of the conditional consumption growth process in explaining the equity term structures and extend the long-run risk (LRR) model by incorporating non-Gaussian time-varying distributed shocks into the consumption and dividend growth processes. This trackable LRR-based asset pricing model not only explains a wide range of realistic non-Gaussian features observed in consumption growth and asset price moments (i.e., excess return, variance risk premium), but also explains time variation of equity term structure. Specifically, the model endogenously generates upward-sloping forward equity yield, pro-cyclical forward equity yield spread, counter-cyclical one-period equity return and pro-cyclical expected yield change. My paper sheds light on explaining equity term structure by incorporating non-Gaussian shocks.
Presentations: Annual Meeting of the FMA, Chicago, October 2023;
FMA European Conference, Aalborg, June 2023.
Work-in-progress:
Generalized Lower Bounds on the Conditional Expected Excess Return on Individual Stocks: A New Approach (with my advisor Fousseni Chabi-Yo)
Stock Market Volatility and Term Structure of Equity Returns
I examine how agents' perceptions of risk influence the prediction of equity claims with varying maturities that are proxied as duration portfolios. Utilizing the Graham and Harvey CFO 1-year and 10-year S\&P 500 return expectation survey data, I demonstrate that agents' expectations of both short-term and long-term volatility are sticky and heavily influenced by past variance. The analysis indicates that prices of equity claims with different maturities respond negatively to changes in volatility; thus, increases in current expected variance negatively predict equity returns across all maturities, while past variance positively predicts them. The slow-moving nature of extrapolative expectations suggests that agents believe variance to be highly persistent. Consequently, errors in conditional variance expectations have significant implications for long-horizon equity claims.
China’s Stock Market Return Prediction
Focusing on China’s stock market, I empirically conduct return predictions with “traditional” linear models and advanced approaches with machine learning techniques, including Lasso, Ridge, Elastic Net, Random Forest, and Neural Networks. I find that the book-to-market ratio, size, short-term reversal, dividend-price ratio, sales growth, long-term yield are the six most prominent factors among all the 33 investigated features. Among them, two macroeconomic variables (dividend-price ratio and long-term yield) are the most informative predictors for large-cap stocks; firm-specific fundamental variables, size, short-term reversal, book-to-market ratio, and sales growth, however, are the most informative predictors for small-cap stocks.