Using AI to Analyze Financial Markets

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Using AI to Analyze Financial Markets

Using AI to Analyze Financial MarketsArtificial Intelligence, AI, and machine learning have already made major contributions to financial market analysis. As we have explored the opportunities, and they are incredible, we also need to recognize the challenges. Financial analysis is not at all like facial recognition, where there have been no real changes in 10,000 years.

Financial analysis is not like playing games, either. Take chess. The rules and objectives are totally clear and they do not change. Now let’s think about financial markets. The rules change and they may be interpreted differently. Market participants have different objectives, different risk tolerances. Rule interpretation and enforcement is not consistent.

And some participants, just a few, may bend the rules. In short, with financial market analysis, there are a lot of complications and complex feedback effects. It gets harder. Even random variables can develop short-term patterns, and a machine developed explicitly for pattern recognition must learn to distinguish which patterns may have some useful persistence, and which ones to throw away.

Markets are dynamic. Market participants act and react to every new piece of information, adding complexity to the feedback effects. Data from each time unit are not of equal value, and older data may be of much less value than newer data. Markets may be episodic, with one set of characteristics dominating for months or even years, and then giving a way to a new set of environmental factors.

When applying machine learning to financial market analysis, we’ve learned that a tremendous amount of time and effort must be invested in extensive data cleaning, checking and verification. Deep domain expertise is required for both data cleansing, as well as choosing the right AI or machine learning tools just to attack a given problem and provide useful solutions that can stand the test of time.

Speaking of time, the analysis can never be static; always dynamic. Meaning ideas such as Bayesian Inference becoming extremely important to consider. And then there is the application of theory, not always from finance. Appreciating market structure can take one into theoretical physics, behavioral finance and disequilibrium economics, just to name a few.

All of this complexity underscores the difficulty and challenges, yet we do not wish to dampen the enthusiasm and the promise. We have already made amazing strides with our new AI and machine learning tools, coupled with big data applications. What we’ve learned is that we need diverse teams with domain expertise, statistical skills and theoretical thinking.

Collaborative teams are likely to show us the way to effectively employ machine learning in financial analysis.


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