Machine Learning and Financial Investing

Machine Learning and Financial Investing

Jie Du (UMBC, USA) and Roy Rada (UMBC, USA)
Copyright: © 2012 |Pages: 11
DOI: 10.4018/978-1-60960-818-7.ch610
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

This chapter presents the case for knowledge-based machine learning in financial investing. Machine learning here, while it will exploit knowledge, will also rely heavily on the evolutionary computation paradigm of learning, namely reproduction with change and selection of the fit. The chapter will begin with a model for financial investing and then review what has been reported in the literature as regards knowledge-based and machine-learning-based methods for financial investing. Finally, a design of a financial investing system is described which incorporates the key features identified through the literature review. The emerging trend of incorporating knowledge-based methods into evolutionary methods for financial investing suggests opportunities for future researchers.
Chapter Preview
Top

Background

Development in the State of the Art of Machine Learning

Machine learning can be defined as a program that based on experience E with respect to some class of tasks T and a performance measure P improves its performance at task T, as measured by P, with experience E (Mitchell, 1997). Machine learning systems are not directly programmed to solve a problem, instead they develop based on examples of how they should behave and from trial-and-error experience trying to solve the problem.

The field of machine learning addresses the question of “how can we build computer systems which can automatically improve with experience, and what are the fundamental laws that govern all learning processes?” (Mitchell, 2006).

Different techniques are used in different subfields of machine learning, such as neural networks(Chauvin & Rumelhart, 1995), instance-based learning (Aha, Kibler & Albert, 1991), decision tree learning (Quinlan, 1993), computational learning theory (Kearns & Vazirani, 1994), genetic algorithms(Mitchell, 1996), statistical learning methods (Bishop, 1996), and reinforcement learning(Kaelbling, Littman & Moore, 1996). In recent years, machine learning has shed light on many other disciplines, such as economics, finance, and management. Many machine learning tools, including neural networks and genetic algorithms, are used in intelligent financial investing research.

Complete Chapter List

Search this Book:
Reset