Artificial Intelligence in the Stock Market

Success in the stock market has always relied on understanding trends. Traditionally, this understanding came from studying the news and consumer behavior. All of this information is publicly available, in theory giving all investors equal footing. The efficient market hypothesis reflects this concept. It states that share prices reflect all available information, making it impossible to outperform the market consistently.¹ This theory has been borne out by research that shows no actively managed funds have exceeded benchmark performances over the long term.²

However, artificial intelligence (AI) may disrupt this trend or, alternatively, make markets even more efficient. AI can analyze tremendous amounts of data far faster than humans and provide insights into how various factors may affect market movement. Investors who understand how to wield these powerful tools may have an advantage over those who don’t. Understanding AI’s evolution can help investors and regulators understand the historical context and potential ramifications of AI in the stock market.

This article will explore AI and stock trading by outlining the history of finance and technology, including the benefits and risks.

Pre-AI Foundations (1960s–1980s)

Quantitative trading laid the foundation for today’s AI stock trading. Long before machine learning and neural networks, traders experimented with using mainframe computers and mathematical models to analyze stocks. This led to careers for quants, financial professionals with strong mathematical and statistical backgrounds who developed models to optimize trading using mainframe computers.

Beginning in the 1970s and gaining momentum in the 1980s, investors began using program trading, which employed rules-based systems to buy or sell stocks in response to specific triggering events. Hedge funds used this technology to identify and exploit trading opportunities much faster than was possible with manual trading.³

Machine-Learning Takes Hold (1990s–2000s)

In the 1990s, advances in technology enabled the development of more sophisticated AI, particularly through the use of statistical learning algorithms and neural networks. These enabled the development of support vector machines, which could identify bullish or bearish market conditions by analyzing data. Neural networks, though limited at the time, hinted at the technology advances that lay ahead.⁴

Around this time, high-frequency trading models used machine learning to exploit small inefficiencies across markets by executing thousands of trades per second. To make these models even more efficient, firms physically moved their servers closer to exchange data centers to gain a millisecond advantage.⁴

Deep Learning and Natural-Language Processing Boom (2010s)

In the 2010s, fueled by advances in computer processing power and cloud infrastructure, computer scientists made dramatic advances in deep learning and natural language processing. This allowed them to build models that could handle increasingly complex forecasting, including price prediction based on volatile market factors. Natural language processing enabled models to extract unstructured data from sources such as social media to analyze consumer sentiment. With this information, investors could adjust their positions within seconds of breaking news or viral social media posts.⁵ AI-driven robo-advisors on investment platforms, such as Betterment and Wealthfront, brought the advantages of AI to everyone, democratizing portfolio management. These models automatically balance portfolios even after the market moves.⁶

AI Integration Across the Trade Lifecycle (2020s)

The 2020s has seen the widespread application of AI in every aspect of trading. In the front office, AI models use real-time data streams to evaluate market signals and execute trading algorithms that adapt to market conditions. Behind the scenes, businesses use AI algorithms to detect fraudulent transactions and ensure they’re complying with applicable financial regulations.⁷

Businesses also use AI models for risk analysis to stay ahead of changing market conditions. These models rely on rapid data processing, which cloud technology facilitates. Edge computing, the practice of locating the data processing equipment closer to the data-generating equipment, reduces lag time and makes these models even faster.⁷

Benefits of AI in Markets

AI is making markets more efficient and benefits stock trading in several ways. By responding to market forces in real-time, AI models reduce the time investors need to spend researching. AI makes the market more efficient because it can adjust to breaking news faster. AI tools automate risk management through activities such as anomaly detection, which flags transactions that could indicate fraud. They can also improve risk mitigation by allowing investors to weigh the likelihood of negative outcomes through predictive models. Automated investing eliminates the risks associated with making emotional decisions. Robo-advisors can make adjustments based on personalized factors such as risk tolerance and investing goals.⁷

Risks and Controversies

Although AI models can benefit financial markets, they also come with risks. One significant risk is that AI algorithms can cause a flash crash, which is a dramatic price drop due to a repetitive feedback loop. This occurred in 2010 when the Dow Jones Industrial Average fell over 1,000 points in a few minutes.⁸

AI tools can also suffer from model overfitting, which is where the model performs well on historical data but fails when exposed to new data. This can occur when the model interprets irrelevant data as meaningful.⁸

Another major concern about AI models in all use cases is that many are “black boxes.” No one understands exactly how they’re making decisions. They could be making recommendations based on biased, outdated or bad data. Computer scientists are working on building more explainable models, but they’re generally not as effective.

AI systems also present ethical issues with data privacy and bias. Individual investors may not realize how much of their data is being exposed to these systems. AI tools have had significant problems with bias in the past, so they may bring those issues to the stock market, which could impact market fairness for everyone.⁸

The Road Ahead

There are several emerging trends in AI stock trading that you may see in the near future. AI models rely on high-quality, usable data to work effectively. However, obtaining such data can be expensive and time-consuming. Generative AI has the potential to create data that mimics real-world data for training models and testing scenarios.⁹

Advances in quantum computing have the potential to upend optimization routines. Quantum algorithms can theoretically process data in parallel, which means they can handle problems in seconds that may take traditional computers hours, days or longer to process.¹⁰

The potential for innovation in trading with AI is generating a growing need for interdisciplinary talent. People who understand both finance and AI will be at the forefront of these developments.

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