Showing 1 - 10 of 91,468
Persistent link: https://www.econbiz.de/10011938630
Persistent link: https://www.econbiz.de/10012244733
In this paper, we combine the theory of stochastic process and techniques of machine learning with the regression analysis, first proposed by Longstaff and Schwartz 2001 and apply the new methodologies on financial derivatives pricing. Rigorous convergence proofs are provided for some of the...
Persistent link: https://www.econbiz.de/10012890648
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...
Persistent link: https://www.econbiz.de/10012899608
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...
Persistent link: https://www.econbiz.de/10012906301
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...
Persistent link: https://www.econbiz.de/10012481045
This paper evaluates the performance of machine learning methods in forecasting stock returns. Compared to a linear benchmark model, interactions and non-linear effects help improve predictive performance. But machine learning models must be adequately trained and tuned to overcome the high...
Persistent link: https://www.econbiz.de/10012829491
We examine the cross-section of international equity risk premia with machine learning methods. We identify, classify, and calculate 88 market characteristics and use them to forecast country returns with various machine learning techniques. While all algorithms produce substantial economic...
Persistent link: https://www.econbiz.de/10013306087
We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...
Persistent link: https://www.econbiz.de/10012426271
Persistent link: https://www.econbiz.de/10012793590