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

arXiv: https://arxiv.org/abs/2106.07103 · HTML: https://mlsanity.com/paper/2106.07103/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 13 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation](https://mlsanity.com/paper/1607.05368/) (2016)
- [Form 10-Q Itemization](https://mlsanity.com/paper/2104.11783/) (2021)
- [High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model](https://mlsanity.com/paper/1804.08472/) (2018)
- [Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model](https://mlsanity.com/paper/2011.04171/) (2020)

## Built on by (most cited first, 7 of 7)

- [Clustering Structure of Microstructure Measures](https://mlsanity.com/paper/2107.02283/) (2021)
- [The Adaptive Multi-Factor Model and the Financial Market](https://mlsanity.com/paper/2107.14410/) (2021)
- [An Efficient Group-based Search Engine Marketing System for E-Commerce](https://mlsanity.com/paper/2106.12700/) (2021)
- [Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic](https://mlsanity.com/paper/2209.10792/) (2022)
- [Finding Lookalike Customers for E-Commerce Marketing](https://mlsanity.com/paper/2301.03147/) (2023)
- [Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series](https://mlsanity.com/paper/2110.00082/) (2021)
- [Integrating Natural Language Processing Techniques of Text Mining Into Financial System: Applications and Limitations](https://mlsanity.com/paper/2412.20438/) (2024)

## Abstract (arXiv)

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.