Financial Topic Models
Initial results on S&P500 stock market data show that topic models are able to obtain meaningful stock categories from unsupervised data and show promise in revealing network-like statistics about the stock market.
Abstract
We apply topic models to financial data to obtain a more accurate view of economic networks than that supplied by traditional economic statistics. The learned topic models can serve as a substitute for or a complement to more complicated network analysis. Initial results on S&P500 stock market data show that topic models are able to obtain meaningful stock categories from unsupervised data and show promise in revealing network-like statistics about the stock market. We also discuss the characteristics of an ideal topic model for financial data.