A News-based Machine Learning Model for Adaptive Asset Pricing
Statistical Finance
2021-06-15 v1 Machine Learning
Methodology
Machine Learning
Abstract
The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news. Using a combination of various machine learning algorithms, we first derive a company embedding vector for each basis asset from the financial news. Then we obtain a collection of the basis assets based on their company embedding. After that for each stock, we select the basis assets to explain and predict the stock return with high-dimensional statistical methods. The new model is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.
Keywords
Cite
@article{arxiv.2106.07103,
title = {A News-based Machine Learning Model for Adaptive Asset Pricing},
author = {Liao Zhu and Haoxuan Wu and Martin T. Wells},
journal= {arXiv preprint arXiv:2106.07103},
year = {2021}
}