Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

This study develops FinText, a financial word embedding compiled from 15 years of business news archives. The results show that FinText produces substantially more accurate results than general word embeddings based on the gold-standard financial benchmark we introduced. In contrast to well-known econometric models, and over the sample period from 27 July 2007 to 27 January 2022 for 23 NASDAQ stocks, using stock-related news, our simple natural language processing model supported by different word embeddings improves realised volatility forecasts on high volatility days. This improvement in realised volatility forecasting performance switches to normal volatility days when general hot news is used. By utilising SHAP, an Explainable AI method, we also identify and classify key phrases in stock-related and general hot news that moved volatility.

Keywords: Realised Volatility Forecasting; Machine Learning; Natural Language Processing; Word Embedding; Explainable AI; Big Data

JEL Classification: C22; C45; C51; C53; C55; C58

Suggested Citation: Suggested Citation

Rahimikia, Eghbal and Zohren, Stefan and Poon, Ser-Huang, Realised Volatility Forecasting: Machine Learning via Financial Word Embedding (July 28, 2021). Available at SSRN: https://ssrn.com/abstract=3895272 or http://dx.doi.org/10.2139/ssrn.3895272