Research

Working Papers

Transaction Costs and the Stochastic Discount Factor

(with Daniele Bianchi and Teng Jiao)

We show that transaction costs fundamentally reshape the stochastic discount factor (SDF) by determining which characteristics matter in equilibrium. Using deep neural networks, we incorporate transaction costs directly into robust SDF estimation rather than treating them as post-optimization adjustments or arbitrary investment constraints. Transaction cost-aware SDFs yield substantially higher net Sharpe ratios and superior cross-sectional pricing through endogenous portfolio reallocation: increased diversification, reduced turnover, and lower exposure to costly high-turnover characteristics. These effects persist across sample configurations, market regimes, neural network specifications, and alternative definitions of transaction costs, demonstrating that trading frictions are structural determinants of equilibrium asset prices.

Conditional Latent Factor Models Via Econometrics-Based Neural Networks

(Job Market Paper)

Presented at: ESSEC Business School, Duke University, University of Bristol, UC Berkeley (Haas), SoFiE Webinar for Graduate Students, EWMES 2022, CFE-CMStatistics 2021, SFI Research Days 2021, Shanghai University of Finance and Economics, Shandong University, Università della Svizzera italiana (USI Lugano)

I develop a hybrid methodology that incorporates an econometric identification strategy into artificial neural networks when studying conditional latent factor models. The time-varying betas are assumed to be unknown functions of numerous firm characteristics, and the statistical factors are population cross-sectional OLS estimators for given beta values. Hence, identifying betas and factors boils down to identifying only the function of betas, which is equivalent to solving a constrained optimization problem. For estimation, I construct neural networks customized to solve the constrained optimization problem, which gives a feasible non-parametric estimator for the function of betas. Empirically, I conduct my analysis on a large unbalanced panel of monthly data on US individual stocks with around 30, 000 firms, 516 months, and 94 characteristics. I find that 1) the hybrid method outperforms the benchmark econometric method and the neural networks method in terms of explaining out-of-sample return variation, 2) betas are highly non-linear in firm characteristics, 3) two conditional factors explain over 95% variation of the factor space, and 4) hybrid methods with literature-based characteristics (e.g., book-to-market ratio) outperform ones with COMPUSTAT raw features (e.g., book value and market value), emphasizing the value of academic knowledge from an angle of Man vs. Machine.

Extracting Statistical Factors When Betas Are Time-Varying

(with Patrick Gagliardini)

Presented at EFA 2020, MFA 2020, CFE-CMStatistics 2019, EEA & ESAM 2019, SoFiE 2019, University of Geneva, Università della Svizzera italiana (USI Lugano)

This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is nonparametric regarding the time-variation of loadings as functions of lagged common shocks and individual characteristics. The number of active factors can also be time-varying as an effect of the changing macroeconomic environment. The method uses Instrumental Variables (IV) which have full-rank covariation with the factor betas in the cross-section, and allows for a high-dimensional vector generating the conditioning information. We use Double Machine Learning to conduct inference on average conditional canonical correlations between latent and observed factors, and similar parameters of interest. We show that the conditional factor space extracted from the panel of monthly returns of individual stocks in the CRSP dataset overlaps only partly with the span of traditional sets of empirical factors.