Can Artificial Intelligence Predict a Butterfly Effect?

From fraud detection to risk management to asset trading, a growing number of the world’s financial decisions are being handled by artificial intelligence. Machines will not entirely replace investor intuition, high-octane trading and client relationships at the core of the business, but now the typical financier is just as likely to be an expert in data mining, not just financial markets. A branch of artificial intelligence known as machine learning, which involves processing huge quantities of data to identify patterns and make predictions that humans might otherwise miss, is particularly ubiquitous in the financial markets. Powered by neural networks that resemble the connections of a human brain, machine learning becomes more effective as it’s fed more data. 

Can AI predict a buttefly effect?

Despite the hype, AI is more aspiration than reality. As of 2017, less than 40 percent of all companies even had an AI strategy in place, according to a survey conducted by MIT’s Sloan Management Review. The financial industry has been quicker than most industries to embrace the power of artificial intelligence, because highly digitized, automated transactions and trades tend to generate lots of data. The financial technology startup Overbond, which can predict the timing and pricing of new bond issuances, has gained approximately half of all institutional investors in Canada as clients. 

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With enough data and computing power, machine learning algorithms could make predictions based on any conceivable inputs, no matter how seemingly disparate or loosely connected their relationship. It would be enormously valuable if we could understand how small events in one part of the world may have larger consequences in another part—a phenomenon known as the butterfly effect. Machine learning will soon excel at this kind of analysis. Meanwhile, check out the trading views community for great ideas:

Just to take one example, Yewno, a startup launched in 2014, created a machine learning algorithm that could discover hidden relationships in the fields of finance, education, science, and governance. The company claims that it can help its customers build a portfolio of stocks and ETFs based on a more comprehensive understanding of market anomalies and vital indicators. In January, the company launched a new investment research platform known as Yewno|Edge, which can uncover the “hidden relationships between companies and complex concepts” through the application of computational linguistics and semantic analysis (the study of language and context), allowing users to “evaluate a company’s exposure to virtually any factor” such as trade wars and data privacy in order to build their portfolios accordingly. Yewno also claims that it can incorporate insights gathered from the analysis of individual events, including news headlines, lawsuits, earnings calls, insider trading cases, and IPOs. 

So far the technology is only used at the margins of the financial system, but as artificial intelligence grows more powerful, it will begin to predict the effects of anomalous events with even greater accuracy. Seemingly small events in one part of the market could be analysed quickly before they have an effect on the rest of the system. That will hopefully make the financial industry more resilient and efficient for the people investing in it. 

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