Machine learning and information-theoretic approaches for financial applications

Firm-specific information contributes to resource allocation in capital markets. The prediction and inference of future firm-specific information are often critical factors that can lead to a prosperous economy. Machine Learning and Information Theory offer new, alternative methods to aid in the prediction and inference of future firm-specific information. Thus, we explore approaches from machine learning and information theory to improve existing research areas in accounting and finance. In Chapter 2, we utilized random forest trees to predict the annual direction of profitability for firms with a minimal amount of information. We demonstrate that we can out-perform benchmark models typically used in financial accounting research. Major public firms announce their annual earnings during the first quarter of the year. The embedding information in these announcements effects the announcing firm and is transferred to or incorporated by other firms. Existing literature in finance and accounting focuses on measuring information transfers as effects stemming from firm-specific information releases. In Chapter 3, we discuss a solution to estimate information transfer via transfer entropy between random processes. We show that our solution to estimate information transfer scales better with data set size and is up to 1,072 times faster than all existing, open source solutions for large datasets. In Chapter 4, we present an alternative approach to estimate information transfer between firms centered around earnings announcements. We show that information transfer between firms is stronger for firms on days with unexpected earnings news. We also demonstrate that communities of firms significantly differ depending on the presence of earnings releases.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Informatics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Ikegwu, Kelechi Moise

Contributors dc:contributor

• Brunner, Robert
• Sougiannis, Theodore
• McMullin, Jeff
• Yun, Joseph

• Machine Learning
• Information Theory
• Random Walk
• Network Science
• Finance
• Accounting
• Financial Accounting

Statement dc:rights

• Copyright 2021 Kelechi Ikegwu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/112947

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/112947