Applying Artificial Intelligence to Financial Investing

Applying Artificial Intelligence to Financial Investing

Hayden Wimmer (Georgia Southern University, USA) and Roy Rada (University of Maryland – Baltimore County, USA)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/978-1-5225-2255-3.ch001
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Abstract

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this article summarizes key contributions of applying AI to financial investing as appears in the academic literature.
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Background

What Is Artificial Intelligence?

In the early days of computing, a typical task for a computer program was a numerical computation, such as computing the trajectory of a bullet. In modern days, a typical task for a computer program may involve supporting many people in important decisions backed by a massive database across a global network. As the tasks that computers typically perform have become more complex and more closely intertwined with the daily decisions of people, the behavior of the computer programs increasingly assumes characteristics that people associate with intelligence. When exactly a program earns the label of ‘artificial intelligence’ is unclear. The classic test for whether a program is intelligent is that a person would not be able to distinguish a response from an intelligent program from the response of a person. This famous Turing Test is dependent on factors not easily standardized, such as what person is making the assessment under what conditions.

A range of computer programming techniques that are currently, popularly considered artificial intelligence techniques includes (Rada, 2008):

  • Knowledge-based techniques, such as in expert systems.

  • Machine learning techniques, such as genetic algorithms and neural networks.

  • Sensory or motor techniques, such as natural language processing and image processing.

These methods may apply to investing. For instance, expert systems have been used to predict whether a company will go bankrupt. Neural networks have been used to generate buy and sell decisions on stock exchange indices. Natural language processing programs have been used to analyze corporate news releases and to suggest a buy or sell signal for the corporate stock.

While artificial intelligence (AI) could apply to many areas of investing, much of what happens in computer-supported investing comes from non-AI areas. For instance, computational techniques not considered primarily AI techniques include numerical analyses, operations research, and probabilistic analyses. These non-AI techniques are routinely used in investing.

Key Terms in this Chapter

Genetic Algorithm: An algorithm that mimics the genetic concepts of natural selection, combination, selection, and inheritance.

Expert System: A program that uses knowledge and inferences to solve problems in a way that experts might.

Hybrid System: A system which employs a combination of techniques and methods.

Artificial Intelligence: The ability of a computer to perform activities which are normally considered to require human intelligence.

Swarm Intelligence: Intelligence based on many individuals and decentralized control and self-organization.

Support Vector Machines (SVM): Supervised learning model to analyze data and predict which possible classes form the output.

Asset Valuation: The process of determining the worth of something.

Decision Tree: A tree like structure for modeling decisions and classifying data.

Case-Based Reasoning: The process of solving new problems based on successful past solutions to similar problems.

Machine Learning: A method of automatically learning patterns from data in order to make future predictions.

Neural Networks: Programs that simulate a network of communicating nerve cells to achieve a machine learning objective.

Risk Management: The process of managing the uncertainty in investment decision-making.

Evolutionary Computing: Branch of artificial intelligence which mimics biological evolution and often applied to optimization problems.

Investing: The act of committing money to an endeavor with the expectation of obtaining profit.

Fuzzy Systems: Systems which deal in approximations as opposed to exact representations (i.e. true/ false).

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