# A penalized two-pass regression to predict stock returns with time-varying risk premia

arXiv: https://arxiv.org/abs/2208.00972 · HTML: https://mlsanity.com/paper/2208.00972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 57 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [A diagnostic criterion for approximate factor structure](https://mlsanity.com/paper/1612.04990/) (2016)

## Abstract (arXiv)

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no-arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no-arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.