Artificial Intelligence and Investing

Artificial Intelligence and Investing

Roy Rada (University of Maryland, USA)
DOI: 10.4018/978-1-60566-026-4.ch041
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Abstract

The techniques of artificial intelligence include knowledgebased, machine learning, and natural language processing techniques. The discipline of investing requires data identification, asset valuation, and risk management. Artificial intelligence techniques apply to many aspects of financial investing, and published work has shown an emphasis on the application of knowledge-based techniques for credit risk assessment and machine learning techniques for stock valuation. However, in the future, knowledge-based, machine learning, and natural language processing techniques will be integrated into systems that simultaneously address data identification, asset valuation, and risk management.
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What Is Artificial Intelligence?

Computers play a role in many aspects of investing. For example, program trading is computer-driven, automatically executed trading of large volumes of shares, and has become increasingly prominent on stock exchanges. Artificial intelligence is a technique of computing that is perpetually on the cutting edge of what can be done with computers. Artificial intelligence could apply to program trading, but also other aspects of investing.

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.

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Investing And Data

The process of investing has three stages of:

  • Data identification,

  • Asset valuation, and

  • Risk management.

AI has been most often applied to asset valuation, but is also applicable to data identification and risk management.

Two, high-level types of data used in financial investing are technical data and fundamental data. The price of an asset across time is technical data, and lends itself to various computations, such as the moving average or the standard deviation (volatility). Fundamental data should support cause-and-effect relationships between an asset and its price. For instance, the quality of management of a company should influence the profitability of a company and thus, the price of its stock.

Key Terms in this Chapter

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

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

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

Asset Valuation: The process of determining the worth of something

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

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

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