Machine Learning and Financial Investing

Machine Learning and Financial Investing

Jie Du (UMBC, USA) and Roy Rada (UMBC, USA)
Copyright: © 2010 |Pages: 12
DOI: 10.4018/978-1-60566-766-9.ch017
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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.
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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.

Key Terms in this Chapter

Genetic Algorithms: Genetic algorithms are search procedures based on the mechanics of natural selection and genetics and are in the class of evolutionary computation techniques.

Genetic Programming: Genetic programming is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program’s ability to perform a given computational task.

Evolutionary Computation: In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that uses iterative progress in a population selected in a guided random search using parallel processing to achieve the desired end.

Assets: An asset is defined as a probable future economic benefit obtained or controlled by a particular entity as a result of a past transaction or event in business and accounting.

Machine Learning: As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to “learn”

Portfolio Management: Portfolio management involves deciding what assets to include in the portfolio by comparing the expected return from portfolios of different asset bundles, given the goals of the portfolio owner and changing economic conditions

Portfolio: In finance, a portfolio is an appropriate mix of or collection of investments held by an institution or a private individual

Neural Logic Network: A neural logic network is a finite directed graph, consisting of a set of input nodes and an output node

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