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The Machine Behind the Market: A.I.’s Rise in Modern Finance

  • Writer: Tyler White
    Tyler White
  • Jan 22
  • 7 min read

 

According to the J.P Morgan Asset Management department, companies around the world will spend $350 billion on Artificial Intelligence (A.I.) infrastructure investing in 2025, a single year leap so large that it surpasses the entire GDP of Denmark or Malaysia. A.I. isn’t just creeping into the world of investing, it’s kicking down the door. Not only was this budget spent on the advancement of A.I. technology, but specifically the advancement of A.I. investment models that will determine how trillions of dollars will flow through global markets. A.I. investing is no longer a niche subject in Finance, but rather a matter that is one of the biggest modern market forces in society today. From lightning-fast data crunches to robots claiming they can build a profitable portfolio in seconds, the potential applications of A.I. in investing continue to expand. Yet the same algorithms that can promise success also misfire or behave in ways humans can’t understand, destroying value in seconds. A.I. in finance sits at the crossroads of possibility and risk, and it is on us to truly navigate the uncharted territory it promises.


A.I. didn’t suddenly emerge in the investing world; its creation has evolved from decades of quantitative trading, where computers have already been generating algorithms and models to help firms make decisions. What distinguishes today’s A.I. from earlier generations of algorithmic trading is not automation, but adaptability—the ability to learn, adjust, and optimize its investment decisions in real time. Today’s A.I. systems can simultaneously analyze earnings reports, news events, and social media sentiment. Financial institutions have taken notice; J.P. Morgan reports that after the release of ChatGPT, companies in the A.I. ecosystem generated roughly 75% of the S&P 500’s total returns, 80% of its earnings growth, and 90% of its capital spending growth. This change is reflected in academic research at the portfolio level: a 2022 study published in Management Science reported that A.I. investment models generated returns that, in some cases, were up to 57% higher than those of traditional quant strategies, and in several markets, A.I. was more than twice as accurate as human models at forecasting investment volatility.



To fully understand the A.I. evolution, it is helpful to look to the past. Early algorithmic trading relied on fixed statistical analysis that needed to be changed every time the market moved, which is where A.I. is different. Today’s A.I. tools adapt in real time (via news, social media, earnings reports, etc.) to optimize investing profit in a matter of seconds. Firms like J.P. Morgan and Schroders now use A.I., qualified as “machine learning systems” for portfolio optimization, sentiment analysis, and risk modeling. What began as a niche tool for quantitative analysts has now become a vital piece of the modern finance industry.



The biggest reason why A.I. has taken such a strong hold on the finance industry is its ability to process information at a speed and scale that humans simply can’t compete with. Rather than reviewing a single earnings report at a time, or briefly skimming a group of news headlines, AI systems can analyze thousands of data points at the same time. It pulls from economic indicators, market movements, corporate filings, and even social media sentiment to gather an understanding of the big picture. For example, the typical analyst at Schroders is tasked with researching and monitoring roughly 20-30 companies over the span of a few days. With A.I., this process is reduced to a matter of minutes. Over time, efficiency gains compound exponentially, giving firms a structural advantage in speed, scale, and cost. This new acceleration allows firms to react faster to market changes, test more investment ideas, and manage a greater number of portfolios with greater precision at a time.


The very speed that makes A.I. so appealing is also a source of serious risk, but speed is only the beginning of the problem. Regulators warn that as more firms rely on similar A.I. models, decisions can overlap, resulting in a ‘herding’ behavior that amplifies market swings and, in extreme cases, destabilizes the entire system. The International Monetary Fund (IMF) supports this claim, stating that the mass use of A.I. across all firms will lead to vulnerabilities in overlapping data sets and cloud providers. A.I. models are not designed with awareness of how similar models operate at other firms, meaning they may unknowingly make the same decisions at the same time. If most firms rely on similar A.I. models, markets could become more vulnerable to sudden, correlated errors, and the consequences of this similarity are already measurable. As the IMF shows in a recent comparison, A.I. driven ETFs (exchange-traded funds) have been proven to rotate their portfolio more than 11 times per year, whereas traditional funds are just 0.26 times.


The CFTC’s 2024 report adds to this concern, describing how many financial institutions cannot even explain the thought process behind most of their A.I. model’s decision-making. This leaves both investors and regulators in the dark. This leads us to the question: who cares if humans cannot understand it, as long as the machine is delivering the results? The risks extend beyond just overlapping data sets between firms. Biased training data, obscure model logic, and automated decision making are all potential flaws that lead to error. Real world market history has already shown how unpredictable these systems can be: in 2012, a software glitch in Knight Capital’s automated machine learning system executed a flood of erroneous trades in under an hour, triggering a loss of $440 million and nearly bankrupting the firm. Another pattern occurred during 2010 in the event now known as the “Flash Crash,” when automated trading systems collectively misinterpreted market data and erased nearly $1 trillion in total market value, plunging the Dow Jones nearly 1,000 points. Together, these failures display a deeper truth: that the very technology that was designed to make investing smarter, can make it more fragile through unforeseeable errors inside algorithms driving billions of dollars in daily trading. For those investing their money personally, the Consumer Federation of America strongly warns against solely trusting A.I. to make their investment decisions until they have clearer disclosure on the A.I. 's accuracy. Looking toward the close future, the most promising financial developments and profits are occurring where A.I. is utilized as more of a “co-pilot,” with hybrid human-machine investment strategies, and ethical regulations designed to reduce bias. These changes, taking the risks into account, point toward a future where A.I’s revolutionary benefits can be utilized while minimizing investors risks with turning over markets to algorithms.


As A.I. becomes more deeply integrated into financial markets and firms, the question is no longer about using the technology, but rather the way in which we must govern it. Financial regulators around the world all emphasize the same message: transparency must catch up to innovation. As more firms adopt the use of A.I., regulators are faced with a new obstacle. The challenge with A.I. is the speed and complexity at which it operates, a level that the existing market rules were never designed for. The CFTC warns that many financial institutions cannot even fully explain how the A.I. systems work themselves, making it nearly impossible for any regulation to occur. The IMF also states that because A.I. systems share the same overlapping cloud sources and data system, a failure in one singular model or vendor could cascade across the entire financial system, potentially causing another Knight Capital or “Flash Crash Event” in a matter of minutes. However, regulation is not far behind. Agencies such as the NIST (U.S agency under Department of Commerce) have developed risk-management programs for A.I., but adoption throughout the financial industry is lagging and inconsistent. The CFTC emphasizes not only the importance of U.S. financial sector cooperation, but of global cooperation as well, explaining that because markets are globally connected, A.I. regulation cannot happen independently. A.I. is advancing like never before, at a rate that restricts the current frameworks from keeping markets safe. There is a race between A.I. innovation and regulatory oversight, a race where policymakers are not yet winning.


The rise of A.I. in the finance industry is reshaping the market in ways that were previously unseen a few years ago. It has provided firms with faster analysis, more precise insights, and equips investors with tools that give them more potential than ever. But it has also introduced significant risks; hidden biases, data set overlap, and a growing overdependence on systems in which most analysts don’t fully understand. The intersection between possibility and risk make this subject one of the most prominently debated ones in the financial world today, and for good reason. A.I. has the potential to bring large profits, but also devastating losses to firms, all depending on how each firm navigates this new era of investing. The investors who succeed won’t be the ones who put all their faith into the A.I. systems, but will be those who understand A.I. 's strengths, caution its weaknesses, and use it as an aid rather than replacement. In a world where machines are reshaping the markets, the edge belongs not to those who trust A.I. blindly, but to those who understand where its power ends.



References:


Bianco Research. How Much Has AI Boosted the Market Since ChatGPT? Bianco Research LLC, 2023. 


Bouveret, Antoine, Gaston Gelos, and Evan Papageorgiou. “Artificial Intelligence and Its Impact on Financial Markets and Financial Stability.” International Monetary Fund, September 6, 2024. 


Columbia Business School Digital Future Initiative. “AI Finance Conference: Insights on Artificial Intelligence in Finance.” 2024. 


Consumer Federation of America. Opportunities and Risks of Artificial Intelligence in Investment Markets. October 2024. 

https://consumerfed.org/wp-content/uploads/2024/10/Opportunities-and-Risks-of-Artifici al-Intelligence-in-Investment-Markets-Formatted-Final.pdf. 


Doornik, Jurgen A., and David F. Hendry. “Statistical Model Selection with Machine Learning.” Management Science 68, no. 3 (2022): 1511–1531. 


Financial Crisis Inquiry Commission (FCIC). Preliminary Findings on the May 6, 2010 Flash Crash. Washington, DC: U.S. Government Printing Office, 2011. 


Grand View Research. Algorithmic Trading Market Size, Share & Trends Analysis Report. Grand View Research, 2024. 

International Monetary Fund (IMF). “Artificial Intelligence Can Make Markets More Efficient—and More Volatile.” IMF Blog, October 15, 2024.


International Monetary Fund (IMF). AI-Driven ETF Portfolio Turnover Comparison. Dataset published 2024. 

(Used for Figure: AI vs. Traditional ETF Turnover) 


J.P. Morgan Asset Management. TMT: Four Trends We’re Watching in the Artificial Intelligence Investing Space. 2024. 

https://www.jpmorgan.com/insights/markets-and-economy/top-market-takeaways/tmt-4-t rends-were-watching-in-the-artificial-intelligence-investing-space. 


J.P. Morgan Asset Management. AI Ecosystem Contribution to S&P 500 Returns (Chart). Bloomberg Data, 2024. 

(Used for Figure: AI Ecosystem’s Contribution to S&P 500 Returns.) 


Knight Capital Group. “SEC Charges Knight Capital for Violations Related to August 2012 Trading Incident.” U.S. Securities and Exchange Commission, 2013. 


National Institute of Standards and Technology (NIST). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce, 2023. 


Schroders Investment Management. “How We’re Using AI to Enhance Our Investment Edge in Public Markets.” Schroders Insights, 2024. 


U.S. Commodity Futures Trading Commission (CFTC). Report of the Technology Advisory Committee: Artificial Intelligence in Financial Markets. May 2, 2024. https://www.cftc.gov/media/10626/TAC_AIReport050224/download. 


Vlastelica, Ryan. “AI Is Taking Over the Market.” The Wall Street Journal, 2024. 

Winkler, Rolfe. “AI on Wall Street: The Investors Using Algorithms to Outsmart the Market.” The Wall Street Journal, 2024.

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