An efficiency market hypothesis (EMH) proposes that financial markets are efficient and it is impossible to consistently outperform the market through active investment strategies. But how do AI and EMH coexist? The theory is based on the idea that prices of financial assets, such as stocks and bonds, reflect all publicly available information and that new information is quickly and fully incorporated into prices. 

However, the emergence of artificial intelligence (AI) has challenged the EMH and has led to even more skepticism of its validity.  This article looks at the interplay between AI and EMH to understand whether EMH is durable given the advent of new generations of artificial intelligence.

Dr. Becker and her team have spoken at length about these advances of mathematical modeling in finance, and the ability to find inefficiencies.  Here are the top 5 reasons why AI continues to prove that the EMH is wrong:

1. AI for financial data ingestion creates a data edge – One of the assumptions of the EMH is that all publicly available information is reflected in asset prices. However, AI algorithms can analyze enormous amounts of data, including information that is not always publicly accessible or easy to access for human investors. This ability to ingest and analyze vast amounts of data returns advantage to those that can ingest and make sense of information at speed and clarity. This allows AI to potentially identify trends and patterns that may not be apparent to human investors, giving it an advantage in predicting asset prices. Beyond man vs. machine, we also can anticipate machine vs machine competition where certain machines are advantaged in ingesting and evaluating different financial data sources.
2. AI can decision-making is a source of competitive advantage. The EMH assumes that all investors have equal access to information and can act on it at the same speed. However, AI algorithms can both recognize patterns and execute trades to capture them much faster than humans, allowing them to take advantage of market inefficiencies before they are corrected. To contrast that with discretionary trading, beyond the simple ingestion and dissection of financial data, the time it takes to make decisions and weigh countervailing and supportive data is a point of competition.  Using advanced artificial intelligence, it is proven possible to preprogram decision making parameters such that information is distilled and decided upon with higher conviction and speed – providing a decision advantage.
3. AI- technologies can exploit market inefficiencies creating a timing edge – EMH assumes prices reflect all information available and that active investment strategies cannot consistently beat the market. AI algorithms can, however, identify and exploit market errors and mis-pricings, allowing them to consistently outperform the market. These inefficiencies can be substantial like misunderstanding of data, or technical like information delays.  The ability to access markets and execute trades in efficient ways provides a timing and pricing advantage. Practically speaking, this may be faster order execution, or more advanced ways to execute large orders across various markets.
4. Adapting to changing market conditions using AI creates a responsive edge- The EMH assumes that financial markets are stable and past behaviors is a good indicator of future behaviors. A key advantage of AI over human investors, who may struggle to adapt to changing market conditions, is its ability to adapt to changing market conditions and learn from past data. This underscores the importance of ever-improving data sets, evaluation and decisions modeling, and execution. It’s these advances that create further opportunities for AI. A mathematical approach can validate or negate the continuance of any market condition, and help create the next variant of a trading strategy.  Both algorithms and discretionary investors often face fading issues. But trained intelligence can accelerate the creation of new, more responsive trading strategies.
5.  AI can reproduce the performance edge of select, proven investors, creating a performance edge– The EMH assumes that it is impossible to consistently outperform the market through active investment strategies. However, AI algorithms can replicate the success of top investors by analyzing their strategies and adapting them for use in their own investment decisions. This might mean a thorough analysis of winning decisions and loss avoidance, building investment models and then refining those. This approach of systematically replicating and leveraging winning investments creates an advantage for AI-based investments to potentially outperform the market.

In conclusion, while financial artificial intelligence is still in its infancy, there are numerous grounds for it to invalidate the notion that markets are ‘efficient’. Further, there are definitive evidence to show that AI can use its ever-improving computational powers to consistently outperform the market. While the EMH may still hold true in some esoteric markets, it is clear that the emergence of AI has called into question its universal validity.