  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.
