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A News-based Machine Learning Model for Adaptive Asset Pricing
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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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Integrating Natural Language Processing Techniques of Text Mining Into Financial System: Applications and Limitations
A survey of 2018-2023 NLP-in-finance papers reports that asset pricing is the most studied component and that classification, LSTM, and BERT-style models dominate, with persistent data and interpretability limitations.
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