# A News-based Machine Learning Model for Adaptive Asset Pricing

@article{Zhu2021ANM, title={A News-based Machine Learning Model for Adaptive Asset Pricing}, author={Liao Zhu and Haoxuan Wu and Martin T. Wells}, journal={ArXiv}, year={2021}, volume={abs/2106.07103} }

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…

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