Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

41 Pages Posted: 29 Jul 2021 Last revised: 2 Mar 2023

See all articles by Eghbal Rahimikia

Eghbal Rahimikia

The University of Manchester - Alliance Manchester Business School

Stefan Zohren

University of Oxford - Oxford-Man Institute of Quantitative Finance

Ser-Huang Poon

Alliance Manchester Business School, University of Manchester; Alan Turing Institute

Date Written: July 28, 2021

Abstract

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

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

Eghbal Rahimikia (Contact Author)

The University of Manchester - Alliance Manchester Business School ( email )

Booth Street West
Manchester, M15 6PB
United Kingdom

HOME PAGE: http://www.rahimikia.com

Stefan Zohren

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Ser-Huang Poon

Alliance Manchester Business School, University of Manchester ( email )

Alliance Manchester Business School
Booth Street West
Manchester, Manchester M15 6PB
United Kingdom
+44 161 275 4031 (Phone)
+44 161 275 4023 (Fax)

HOME PAGE: http://www.manchester.ac.uk/research/Ser-huang.poon/

Alan Turing Institute ( email )

British Library, 96 Euston Road
96 Euston Road
London, NW12DB
United Kingdom

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
1,275
Abstract Views
5,871
Rank
29,658
PlumX Metrics