Financial Event Prediction using Machine Learning

228 Pages Posted: 15 Nov 2019 Last revised: 25 Mar 2020

See all articles by Derek Snow

Derek Snow

The Alan Turing Institute

Date Written: November 6, 2019

Abstract

This thesis focuses on the use of machine learning in financial event prediction. In the past, finance academics had to be content with mostly linear models that could only ingest a small number of variables of a particular type. Now we can use non-linear models with a larger number of variables and more versatile data types. In this thesis, I show how machine learning can lead to significant improvements in financial event prediction, more specifically, in earnings surprise, bankruptcy and facility closure predictions, all of which have significant financial implications for businesses and stakeholders alike.

Keywords: Event Prediction, Financial Event, Machine Learning, Classification, Earnings, Bankruptcy, Facility Closure

Suggested Citation

Snow, Derek, Financial Event Prediction using Machine Learning (November 6, 2019). Available at SSRN: https://ssrn.com/abstract=3481555 or http://dx.doi.org/10.2139/ssrn.3481555

Derek Snow (Contact Author)

The Alan Turing Institute ( email )

British Library, 96 Euston Rd
London, NW1 2DB
United Kingdom

HOME PAGE: http://www.turing.ac.uk/

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