A News-based Machine Learning Model for Adaptive Asset Pricing

Liao Zhu, Haoxuan Wu and Martin T. Wells

Papers from arXiv.org

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.

Date: 2021-06
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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Citations: View citations in EconPapers (1)

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