Modeling Stock Prices with Text Contents in 10-Q Reports

Stock market prediction was once considered to be infeasible. Recent studies on using text contents of information reporting platforms has opened up new ways of analyzing the stock market with machine learning. we propose using the Securities and Exchange Committee (SEC) mandated 10-Q form as a possible source of data for stock predictions. Using the 10-Q reports of S&P 500 companies, we create our corpus by extracting bag-of-words (BOW) of any additions made to the 10-Q documents. Then, we create feed-forward multilayer neural network on stock price ratios of different target prediction periods and achieve positive prediction rates. We demonstrate that text contents of 10-Q form may have information value to stock price prediction models.