TITLE : Machine learning and information-theoretic approaches for financial applications
ABSTRACT : 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.
Use this link to cite: http://hdl.handle.net/2142/112947