Financial Latent Dirichlet Allocation (FinLDA): Feature Extraction in Text and Data Mining for Financial Time Series Prediction

Abstract

News has been an important source for many financial time series predictions based on fundamental analysis. However, digesting a massive amount of news and data published on the Internet to predict a market can be burdensome. This paper introduces a topic model based on latent Dirichlet allocation (LDA) to discover features from a combination of text, especially news articles and financial time series, denoted as Financial LDA (FinLDA). The features from FinLDA are served as additional input features for any machine learning algorithm to improve the prediction of the financial time series. We provide posterior distributions used in Gibbs sampling for two variants of the FinLDA and propose a framework for applying the FinLDA in a text and data mining for financial time series prediction. The experimental results show that the features from the FinLDA empirically add value to the prediction and give better results than the comparative features including topic distributions from the common LDA.

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