Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
This work addresses market risk forecasting for financial analysts and investors, offering an incremental improvement by integrating text and data resources.
The authors tackled volatility prediction in financial markets by analyzing sentiment from annual company disclosures using word embedding-enhanced IR models and fusing them with market data, achieving significant outperformance over state-of-the-art methods.
Volatility prediction--an essential concept in financial markets--has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast volatility. We specifically explore the use of recent Information Retrieval (IR) term weighting models that are effectively extended by related terms using word embeddings. In parallel to textual information, factual market data have been widely used as the mainstream approach to forecast market risk. We therefore study different fusion methods to combine text and market data resources. Our word embedding-based approach significantly outperforms state-of-the-art methods. In addition, we investigate the characteristics of the reports of the companies in different financial sectors.