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We survey recent methodological contributions in asset pricing using factor models and machine learning. We organize these results based on their primary objectives: estimating expected returns, factors, risk exposures, risk premia, and the stochastic discount factor, as well as model comparison...
Persistent link: https://www.econbiz.de/10013322001
Using hundreds of significant anomalies as testing portfolios, this paper compares the performance of major empirical asset pricing models. The q-factor model and a closely related five-factor model are the two best performing models among a long array of models. The q-factor model outperforms...
Persistent link: https://www.econbiz.de/10011279578
Factors in prominent asset pricing models are positively autocorrelated. We derive a transformation that turns an autocorrelated factor to a ``time-series efficient'' factor. Time-series efficient factors earn significantly higher Sharpe ratios than the original factors and contain all the...
Persistent link: https://www.econbiz.de/10012244867
We estimate conditional multifactor models over a large cross-section of stock returns matching 25 CAPM anomalies. Using conditioning information associated with different instruments improves the performance of the Hou, Xue, and Zhang (2015, HXZ) and Fama and French (2015, 2016, FF) models. The...
Persistent link: https://www.econbiz.de/10012937406
We use deep neural networks to estimate time-varying equity risk premia. The key innovations are the nonlinear and non-parametric generalisation of Fama-Macbeth regressions through partial derivatives of an arbitrary estimator function with respect to its input and the introduction of Jacobian...
Persistent link: https://www.econbiz.de/10014344242
This paper develops a methodology for inference on asset pricing models linear in latent risk factors, valid when the number of assets diverges but the time series dimension is fixed, possibly very small. We cast the factor model within the Arbitrage Pricing Theory of Ross (1976) and show that...
Persistent link: https://www.econbiz.de/10012869201
According to no-arbitrage, risk-adjusted returns should be unpredictable. Using several prominent factor models and a large cross-section of anomalies, we find that past pricing errors predict future risk-adjusted anomaly returns. We show that past pricing errors can be interpreted as deviations...
Persistent link: https://www.econbiz.de/10014348676
We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory...
Persistent link: https://www.econbiz.de/10015066381
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Persistent link: https://www.econbiz.de/10012878991