Introduction of Artificial Intelligence in Stock Market

10 Jul, 2021

8 min read

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In the last few decades, the buzzword within the trading and investing sector has been artificial intelligence, or ‘AI.’

What is Artificial Intelligence?

But what does AI mean? In a nutshell, the term artificial intelligence refers to a branch of computer science whose concern is to create ‘smart’ machines or machines capable of mimicking and/or ‘learning’ to perform tasks that would typically require human intelligence. Being an interdisciplinary science with multiple approaches, AI seems to be creating a paradigm shift in every tech industry sector. Unsurprisingly, it has made its way into the world of trading as well. 

Artificial Intelligence and Finance

Experts claim that AI and finance seem to be made for each other in many ways. Techniques that make it easier to identify a pattern that the human eye might not have otherwise detected are one of the fundamental functions of AI. Since finance seems to be quantitative at its core, it is difficult not to correlate it with such functions (data analysis, prediction, error coding) that AI can easily fulfill. 

Although the tools have been around since the 1980s, it wasn’t until the turn of the 21st century that financial firms first started investing in AI. Today, almost all firms are now aiming to tap into financial applications that come embedded with both deep learning and machine learning, almost as a kind of necessity. When it comes to stock trading, artificial intelligence certainly isn’t new, but the wide-ranging access to AI’s capabilities has been relatively limited to large firms. Let’s explore the introduction and growth of AI stock trading. 

Introduction of Artificial intelligence in stock markets

According to one industry player, “Artificial intelligence is to trading what fire was to cavemen.” In other words, AI stock trading has been a game-changer for modern investors. But how did this come about? When was AI first introduced in stock markets, and what was the reception like? AI’s inception in the stock market started on a theoretical level back in the 1960s with Robert Shlaifer. 

In 1959, Shalifer wrote a seminal book called “Probability and Statistics for Business Decision.” The fallout from its publication saw an increase in the popularity regarding research in the domain of statistics in the business world. On a practical level, the 1980s saw the growth of artificial neural networks and fuzzy systems, both of which were to be incorporated to give financial tools better predictive power. 

Perhaps the first, or at least one of the first few artificial intelligence-driven programs that supposedly predicted the stock market was the ‘Protrader expert system.’ The Protrader expert system was designed by California State University’s School of Business student K.C Chen and University of Illinois’ Ting-peng Lian. With their expertise, Chen and Lian successfully predicted the famous 1986 87-point drop in the Dow Jones Industrial Average (although some historical records claim their successful results could have been the consequence of an overfitting error). 

The Protrader Expert system’s major functions included monitoring premiums in the market, determining the optimum investment strategy, executing transactions when they were most advantageous, and modifying the system's knowledge base through a machine learning mechanism. Sound familiar? Most of today’s brokerage firms’ predictive AIs are modeled after this nascent system. 

Another tool was developed by Arthur D. Little Inc. and Chase Lincoln First Bank around the same time that ran on AI. This system was able to carry out debt, retirement, education, life insurance, and investment planning, in addition to budget recommendations, income tax planning, and wealth achievement for other financial goals. If a client wished to receive their financial report from this machine, it would cost them $300 back in the 1980s.

What does AI Stock trading look like presently?

Since the 1980s, AI’s role in finance has evolved from being experimental to essential. Although humans continue to play a huge role in the trading equation, the role of AI has grown to a significant level. As per a recent study conducted by the U.K. research firm Coalition, electronic trading that is AI-driven accounts for nearly 45% of the revenues earned solely in cash equities trading. Even areas of investing that seem to be reluctant to incorporate more automation into their functioning, such as hedge funds, operate with AI-powered analysis tools to build portfolios and receive their investment suggestions. 

To add to this, the branch of AI known as ‘machine learning (a broad field on its own) appears to be evolving at an even quicker pace, with financial institutions becoming one of its earliest adopters. Once Wall Street statisticians learned that they could apply machine learning to multiple aspects of finance, such as investment trading applications, they effectively harnessed the power of crunching millions of data points in real-time. This process ultimately captured information that current statistical models simply couldn’t.

One of the key ways machine learning has impacted modern-day trading is its ability to identify complex trading patterns on a huge scale in real-time across multiple markets. When this kind of machine learning is combined with the high-speed, big data processing power of AI, present-day trading software can provide its clients with a clear ongoing assessment of risk, market predictions, and investment options. Machine learning isn’t just used to crunch numbers. 

One Chicago-based company employs AI by using speech recognition and natural language processing technology to reduce the time it takes to sift through financial data, conversions, and notes. Using the company’s platform, financial professionals employ AI to access market insights, notes, and trending companies in real-time. 

In a nutshell

Artificial intelligence is an essential ingredient for trading today. It is actively replacing human statisticians with its huge data processing abilities. When big-data processing is combined with AI’s ability to segregate this data ‘intelligently,’ investors get a machine-driven replacement for a human, financial advisor. AI has actively altered the stock markets of the 21st century. 

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