A News-based Machine Learning Model for Adaptive Asset Pricing

Autor:	Zhu, Liao, Wu, Haoxuan, Wells, Martin T.
Rok vydání:	2021
Předmět:	Quantitative Finance - Statistical Finance Computer Science - Machine Learning Statistics - Methodology Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.07103 Zobrazit plný text záznamu View this record from Arxiv