arXiv subjects

Jiacheng Zou

Publications and source records attributed to Jiacheng Zou.

3 recordsLinked to original sources

arXiv2026

The Nonstationarity-Complexity Tradeoff in Return Prediction

Does more data improve return prediction? In non-stationary financial markets, longer training windows improve prediction of complex models but incorporate outdated economic regimes, whereas simpler models require less data and are less vulnerable to changes in economic conditions. We formally characterize this nonstationarity-complexity tradeoff, showing that model complexity and training window length must be jointly optimized. We propose an adaptive selection procedure with formal performance guarantees. Over three decades of U.S. equity markets, our method improves out-of-sample $R^2$ on industry portfolios by 14% relative to fixed-window and regime-switching benchmarks, with large gains during recessions.

stat.ML↗

arXiv2026

Private Credit Markets Theory, Evidence, and Emerging Frontiers

Private credit assets under management grew from \$158 billion in 2010 to nearly \$2 trillion globally by mid-2024, fundamentally reshaping corporate credit markets. This paper provides a systematic survey of the academic literature on private credit, organizing theory and evidence around four questions: why the market has grown so rapidly, how direct lender technology differs from bank lending, what risk-adjusted returns investors earn, and whether the sector poses systemic risks. We develop an integrated theoretical framework linking delegated monitoring, soft-information processing, and incomplete contracting to the institutional specifics of modern direct lending. The empirical evidence documents a distinctive lending technology serving opaque, private-equity-sponsored borrowers at a meaningful and persistent spread premium over the broadly syndicated loan market, while performance evidence suggests that risk-adjusted returns for the average fund are largely consumed by fees.

q-fin.GN↗

arXiv2023

Inference for Large Panel Data with Many Covariates

This paper proposes a novel testing procedure for selecting a sparse set of covariates that explains a large dimensional panel. Our selection method provides correct false detection control while having higher power than existing approaches. We develop the inferential theory for large panels with many covariates by combining post-selection inference with a novel multiple testing adjustment. Our data-driven hypotheses are conditional on the sparse covariate selection. We control for family-wise error rates for covariate discovery for large cross-sections. As an easy-to-use and practically relevant procedure, we propose Panel-PoSI, which combines the data-driven adjustment for panel multiple testing with valid post-selection p-values of a generalized LASSO, that allows us to incorporate priors. In an empirical study, we select a small number of asset pricing factors that explain a large cross-section of investment strategies. Our method dominates the benchmarks out-of-sample due to its better size and power.

econ.EM↗