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# Modified Financial Latent Dirichlet Allocation: A Potential Feature Extraction Technique in Text Mining for Financial Time Series Prediction
- URL: https://fsc.stevens.edu/modified-financial-latent-dirichlet-allocation-a-potential-feature-extraction-technique-in-text-mining-for-financial-time-series-prediction/
- Published: 2023-01-25T17:25:28.000Z
- Updated: 2026-05-16T22:33:45.000Z
- Author: Dr. Dragos Bozdog
- Tags: 2021, #Import 2024-05-30 10:26, #Import 2025-11-08 22:14

**Author:** Chun Chen  
**Degree:** M.S. in Financial Engineering  
**Year:** 2021  
**Advisory Committee:** Dr. Dragos Bozdog, Dr. Ionut Florescu

**Abstract:** News articles can be a powerful source for many financial time series predictions. However, extracting useful information from a large amount of unstructured data and representing the features in a rather small dimensionality remain a big challenge for researchers. This paper introduces a modified topic model based on latent Dirichlet allocation (LDA) to extract features from news articles which combines sentiment analysis with text mining, denoted as Modified Financial LDA (M-FinLDA). With some assumptions, we provide the detailed mathematics behind the posterior distributions used in Gibbs Sampling and the log-likelihood used to fix the hyperparameters of the M-FinLDA model. We purpose a basic framework for applying the M-FinLDA in text mining and the features from M-FinLDA can be served as additional input features for any machine learning algorithm to improve the predictions. The experimental results show that the features from the M-FinLDA empirically give better results than the comparative features including topic distributions from the LDA and the FinLDA.