  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.
