Stock momentum, long-term reversal, and other past return characteristics that predict future returns also predict future realized betas, suggesting these characteristics capture time-varying risk compensation. We formalize this argument with a conditional factor pricing model. Using instrumented principal components analysis, we estimate latent factors with time-varying factor loadings that depend on observable firm characteristics. We show that factor loadings vary significantly over time, even at short horizons over which the momentum phenomenon operates (one year), and this variation captures reliable conditional risk premia missed by other factor models commonly used in the literature. Our estimates of conditional risk exposure can explain a sizable fraction of momentum and long-term reversal returns and can be used to generate even stronger return predictions.

预测未来收益的股票动量，长期反转和其他过去收益特征也可以预测未来已实现的beta，这表明这些特征捕获了随时间变化的风险补偿。我们使用条件因素定价模型将这一论点形式化。通过使用仪器化的主成分分析，我们可以估计潜在因素以及随时间变化的因素负荷，这些因素取决于可观察到的公司特征。我们显示，即使在动量现象运行的短时间内（一年），因素负荷也会随时间发生显着变化，并且这种变化捕获了可靠的条件性风险溢价，而该风险溢价被文献中通常使用的其他因素模型所遗漏。