Working Papers
This paper extends a few sections of my first dissertation essay - Confident Risk Premia: Economics and Econometrics of Machine Learning Uncertainties. (Revise and Resubmit, Review of Financial Studies)
Won the best paper award at the 2023 Hong Kong Conference for Fintech, AI, and Machine Learning in Business
Job market seminars at the business schools of
Yale University, Boston College, Boston University, Copenhagen, Georgia State University (canceled), HEC Paris, HKUST, Indian School of Business, National University of Singapore, Tulane University, Universities of Florida, Georgia, and Houston
Invited seminars
Rice University, HPE Data Science Institute, Baruch College (Ph.D. Seminar, 2022), Virginia Tech (2022)
Invited Industry seminars
HPE Data Science Institute (2022), Cubist Systematic Strategies (2023)
Conference presentations at
King's College, London (2021, invited paper), NFA (2021), FMA (2021), Econometric Society Summer Meetings (2022), SoFiE (2022), Auckland Finance Conference (2022), E(astern)FA (2023), 2023 Hong Kong Conference for Fintech, AI, and Big Data for Business (2023)
ABSTRACT:
This paper derives ex-ante confidence intervals of stock risk premium forecasts that are based on a wide range of linear and Machine Learning models. Exploiting the cross-sectional variation in the precision of risk premium forecasts, I provide improved investment strategies. The confident-high-low strategies that take long-short positions exclusively on stocks with precise risk premium forecasts outperform traditional high-low strategies in delivering superior out-of-sample returns and Sharpe ratios across all models. The outperformance increases (decreases) with the model complexity (bias). The confident-high-low strategies are economically interpretable as trading strategies of ambiguity-averse investors who account for confidence intervals around risk premium forecasts.
Won Cubist Systematic Strategies Award for Outstanding Research at the WFA 2020.
Presented at:
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Western Finance Association (WFA) Annual Meetings, 2020
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European Finance Association Annual Meetings, Lisbon, Portugal, 2019
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The University of Chicago, Machine Learning and New Empirical Asset Pricing, 2018
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Northern Finance Association Annual Meetings, Charlevoix, Canada, 2018
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SoFiE Annual Conference, Lugano, Switzerland, 2018,
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European Econometric Society Annual Meetings, Cologne, Germany, 2018
ABSTRACT:
This paper develops a Bayesian methodology to compare asset pricing models containing non-traded factors and principal components. Existing comparison procedures are inadequate when models include such factors due to estimation uncertainties in mimicking portfolios and return covariances. Furthermore, regressions of test assets on such factors are interdependent, rendering comparisons with recently proposed priors sensitive to subsets of the test assets. Thus, I derive novel, non-informative priors that deliver invariant inferences. Simulations suggest that my methodology outperforms existing methods in identifying true non-traded models. I find that macroeconomic factor models dominate several, recent benchmark models with traded factors and principal components.
The paper includes the following notes on priors for comparing asset pricing models.
This article provides an extensive discussion on what priors to use, when, and why, to compare asset pricing models in general. I layout rules to derive priors under three different cases. The first case relates to Barillas and Shanken(2020), and Chib, Zeng, and Zhao(2020), which involves comparing multiple models that exclusively comprise traded factors. In the second, I discuss rules to test an individual model containing non-traded factors. The third considers comparing multiple models involving non-traded factors.
(joint with Tarun Chordia)
(Revise and Resubmit, Journal of Financial Economics)
Presentations at:
SAFE Annual Market Microstructure Conference (2023), Big data in Finance: ASSA Annual Meeting (2022), SoFiE (2022), University of Houston (2021), and Emory University (2020).
Co-author presented this work at the Mid-Atlantic Research Conference, Market Microstructure Online Seminars - Asia Pacific, Financial Markets and Corporate Governance Conference, and Deakin University.
ABSTRACT:
We develop a big-data methodology to estimate fundamental prices and true liquidity measures, explicitly considering the rounding specification due to the minimum tick size. Evaluation of the tick size pilot (TSP), which increased the tick size for some randomly chosen stocks, requires estimating the impact of rounding. True liquidity measures capture the TSP-driven decreased inventory costs of market-makers, whereas traditional measures without the rounding adjustment cannot. We find that the TSP increases market-maker profits, but does not improve liquidity and price efficiency. This result contrasts with existing empirical studies but is consistent with recent theoretical studies that account for rounding.
4. Comparisons of dynamic trading strategies
(joint with Nikolay Gospodinov and Cesare Robotti)
This paper builds on a few sections of my first dissertation essay - Confident Risk Premia: Economics and Econometrics of Machine Learning Uncertainties