Research

Working Papers

Double Machine Learning Inference for Conditional Latent Factors (new version! 23 Jan, 26)

(with Patrick Gagliardini) Formerly titled “Extracting Statistical Factors When Betas Are Time-Varying.”

Presented at AMES-CSW 2026, FinEML 2025, ICEEE 2021, EFA 2020, MFA 2020, CFE-CMStatistics 2019, EEA & ESAM 2019, SoFiE 2019, University of Geneva, Università della Svizzera italiana (USI Lugano)

Yearly median number of conditional factors

Remarks: This figure plots the yearly median of the estimated number of conditional latent factors for thresholds 𝛼 = 0.10, 0.20, 0.30 (corresponding to explained variance levels of 90%, 80%, and 70%, respectively). All estimates are computed out-of-sample using only the test period. Shaded regions indicate bear-market years following the classification in Lunde and Timmermann (2004).

Transaction Costs and the Stochastic Discount Factor (new version! 23 Feb, 26)

(with Daniele Bianchi and Teng Jiao)

Transaction cost–aware SDFs deliver higher net wealth than frictionless models.

Conditional Latent Factor Models Via Econometrics-Based Neural Networks

(Job Market Paper) Updated version coming soon!

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)

Objective Funcion

Model Structure

Work In Progress

Estimate Sentiment Dynamics Via Model-Based Neural Networks

(with Hao Yang) Updated version coming soon!

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