Deep learning based text regression

Most financial analysis methods and portfolio management techniques are based on risk classification and risk prediction. Stock return volatility is a solid indicator of the financial risk of a company. Therefore, forecasting stock return volatility successfully creates an invaluable advantage in financial analysis and portfolio management. While most of the studies are focusing on historical data and financial statements when predicting financial volatility of a company, some studies introduce new fields of information by analyzing soft information which is embedded in textual sources. Forecasting financial volatility of a publicly-traded company from its annual reports has been previously defined as a text regression problem. Recent studies use a manually labeled lexicon to filter the annual reports by keeping sentiment words only. In order to remove the lexicon dependency without decreasing the performance, we replace bag-of-words model word features by word embedding vectors. Using word vectors increases the number of parameters. Considering the increase in number of parameters and excessive lengths of annual reports, a convolutional neural network model is proposed and transfer learning is applied. Experimental results show that the convolutional neural network model provides more accurate volatility predictions than lexicon based models.