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arXiv · 2106.07103

A News-based Machine Learning Model for Adaptive Asset Pricing

Liao Zhu·Haoxuan Wu·Martin T. Wells
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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.

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BibTeXRIS

Liao Zhu, Haoxuan Wu, Martin T. Wells. 2021-06-13. A News-based Machine Learning Model for Adaptive Asset Pricing. https://arxiv.org/abs/2106.07103

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