Deciphering the AI Hype: Can Artificial Intelligence Beat the Market?

From generating professional headshots to writing compelling content, many are buzzing about the illustrious phenomenon of artificial intelligence. But to what end? And the bigger question on the minds of many in the financial industry: Can AI beat the market?

While the AI intrigue is relatively new, Wall Street has been attempting to use computers to make decisions through algorithms for nearly 40 years.1  For decades mathematicians and broker dealers have been attempting to build trading models that analyze historical data and market trends with little to no human input through models that attempt to extrapolate from past data to identify patterns with limited human input.2 The result? Little to no correlation between machine powered trading platforms and unparalleled success.1

At a time where many people are wondering what AI can’t do, read on to find out why beating the market is still on the list…at least for now.

Markets are Efficient

Largely, markets are efficient.  “All of the knowable information is already in the price today,” says Mark Matson, Founder and CEO of Matson Money. “Only unknowable information can change the price moving forward.”  As reflected in the Efficient Market Hypothesis, current stock prices are fairly valued because they reflect all existing information at the time they are sold and therefore, stock picking or market timing in an attempt to outperform the market is highly unlikely and achievable only by luck.6 Because of this, no person – or computer, including AI investment management – can consistently or accurately predict the price of a particular stock in the future.  AI investment bots use past data to make decisions, therefore, their analyzation can become obsolete based on the swift movement of evolving markets.5

The World Can Be Unpredictable

A significant amount of market fluctuation is influenced by factors that are inherently uncertain.  AI investing bots can’t predict war, global unrest, political outcomes, economic factors, or natural disasters. It relies heavily on historical data and trends to make predictions, struggling to account for random or unforeseen events. While history often repeats itself, the market’s reaction can still vary. At the height of the COVID-19 Pandemic, when COVID-related hospitalizations were on the rise, unemployment rates were at an all-time high, and the economy was facing a recession, many investors were bracing for the worst, but the S&P took a surprising upward swing in June 2020, demonstrating just how unpredictable markets can be.3

Data Overload: Dissecting the Noise

AI can process an incredible amount of data, but does it possess the intuition to decipher critical data from the static? Data overload can create too much noise that can lead AI to potentially find false patters or miss critical signals where patterns do exist, called underfitting or overfitting.2  Machines can get bamboozled by noisy markets and can be caught off guard by fickle trends.

The Adaptive Nature of the Market

Markets are constantly moving and changing. They are highly competitive and adaptive.3  For AI to consistently beat the market would require its investing strategy to be equally as adaptive. A caveat for consideration: computers are not intelligently designed to learn and evolve on their own.1  Unlike human beings who have the capacity to evolve, imagine, and generate new thought processes independently, computers currently lack such characteristics.

Regulatory and Ethical Considerations

There are also regulatory and ethical considerations that may prevent AI from performing certain transactions. While the SEC hasn’t completely ruled out AI from investing models, SEC Chair Gary Gensler said the SEC wants to ensure investment advisors and broker dealers are not misleading the public about their use of AI or how they are using it.4 They also need to properly disclose risk involved with using AI and avoid AI washing – making false claims about how AI is being used – that may violate securities laws.4

Human Behavioral Factors

Emotional and psychological factors can play a significant role in an investor’s long-term success. AI investing apps cannot factor in the human dimension of wealth creation that have the power to dramatically influence decisions around money and as a result, the ebb and flow of markets.

Behavioral finance attempts to understand how psychological factors affect markets.  At Matson Money, we are committed to helping families understand both the mathematical and behavioral dimensions of wealth creation. To help families stay disciplined over a lifetime, we train and develop investors through an ongoing relationship with their advisor, who can help them navigate the investors dilemma when it comes to understanding their emotions, biases and instincts around their investing strategy.

Interested in learning how to stay prudent and disciplined over a lifetime? Learn more about Matson Money’s 2-day breakthrough educational event on purpose and investing, the American Dream Experience.


This content is based on the views, opinions, beliefs, or viewpoints of Matson Money, Inc.  This content is not to be considered investment advice and is not to be relied upon as the basis for entering into any transaction or advisory relationship or making any investment decision.

All of Matson Money’s advisory services are marketed almost exclusively by either Solicitors or Co-Advisors.  Both Co-Advisors and Solicitors are independent contractors, not employees or agents of Matson.

Other financial organizations may analyze investments and take a different approach to investing than that of Matson Money. All investing involves risks and costs. No investment strategy (including asset allocation and diversification strategies) can ensure peace of mind, guarantee profit, or protect against loss.  


Efficient Market Hypothesis

Eugene F. Fama, “Random Walks in Stock Market Prices,” Financial Analysts Journal, September/October 1965.


  1. Zuckerman, Gregory. The Wall Street Journal. Published April 12, 2023. Retrieved 18 January 2024 from
  2. What is Overfitting? Amazon. Retrieved 6 May 2024 from,not%20for%20the%20test%20set.
  3. Jennings, John. Why the Stock Market Doesn’t Make Any Sense. Forbes. Published June 15, 2020. Retrieved 6 May 2024 from
  4. SEC Chair Gary Gensler on AI Washing.
  5. Artificial intelligence (AI) versus machine learning (ML) versus predictive analytics: Key differences. Tableau. Retrieved 16 May 2024 from,tasks%20from%20mundane%20to%20incredible.
  6. Baldridge, Rebecca and Curry, Benajamin. What is the Efficient Market Hypothesis. Forbes. Published May 11, 2022. Retrieved 16 May 2024 from