Artificial Intelligence and Investing

Artificial Intelligence and Investing

Roy Rada (University of Maryland Baltimore County, USA) and Hayden Wimmer (Bloomsburg University, USA)
DOI: 10.4018/978-1-4666-5888-2.ch009
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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.

Investing and Data

The process of investing has 3-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.

The universe of fundamental data is infinite. Many streams of data that might be relevant, such as corporate earnings or corporate debt, might also be related to one another. Various non-AI tools, such as linear regression analysis and principal components analysis, might be used in identifying what sets of data are more likely to be useful than what other sets. Such non-AI, computational techniques can be combined with AI techniques in experimenting with various combinations of data and choosing what data to use in asset valuation.

Key Terms in this Chapter

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.

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

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

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

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

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

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

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

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

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

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

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

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