Eureqa Hits Wall Street; Automatically Identifies Key Predictive Relationships

Posted by Jason Kutarnia

12/1/16 10:46 AM

As a team of data scientists, analysts and software developers, we didn’t expect to be praised as financial gurus. But in an industry of ever-present uncertainly and huge financial gains and losses at stake, Eureqa, the dynamic modeling engine, displays a unique competitive advantage in the technology stack: the ability to quickly derive extremely accurate and simple-to-understand models that predict what will happen in the future, and why.

Typically, Wall Street employs elite squadrons of quants and analysts to build models to make forecasts about where individual stocks and other financial instruments are headed. Some firms, such as the consistently elite hedge funds, make delightful profits by “beating the market,” i.e., outperforming an industry-standard index like the S&P 500. Other financial institutions make their money simply off of the fees they charge for commissions. The laggards have significant room for improvement, where instead of leveraging only industry news and well-known metrics like return on equity, price/earnings ratio and idiosyncratic volatility, they could use stockpiles of data to search for signals and early indicators that an investment is primed to tumble or soar. Hunches and over-simplified metrics should be a thing of the past, and the proof should be in the pudding (the data). Some things, like natural disasters and leadership changes, are not always part of the data, but for everything else…there’s Mastercard. Err, Eureqa.

And for those overachievers – the hedge funds, the private wealth management firms, the day traders – who think they have mastered their own domain, we’re here to tell you, there’s a lot of room for improvement. Financial models are time-consuming to build, often to the tune of weeks or months to refine…and meanwhile, the markets, whether moving up or down, are making people money while you’re on the sidelines crafting your models. In addition to the time sink, manual human-made models with tools like R and SAS are not as accurate as they come, nor are they easy to interpret. The result is that firms are leaving millions on the table, and not understanding why the markets or assets behave as they do. It’s one thing to predict that real estate will beat the market in 2017, based on an algorithm that contains 2,000 variables and mind-numbingly complex transformations of those variables. But what if I could accurately predict that real estate in the Northeast U.S. will appreciate 10-12%, while I should leave the Midwest untouched, and the “drivers” of this growth will be 4 truly impactful variables: demographic growth of Millennials moving into the cities, wage increases, job growth, and a slowing of new construction permits. I could not only make more money, but I could justify all of my investments beforehand with a comprehensive understanding of “how things work.”

In order to validate Eureqa’s approach to a major investment firm, I built a simple trading strategy using the stocks in the S&P 500. The goal was to forecast whether a stock’s excess monthly return – the difference between the stock’s return and the overall S&P 500 return – would be positive or negative. In our strategy, we bought a stock if Eureqa predicted its excess return would be positive, and we shorted any stocks Eureqa thought would be negative.

Immediately, the client saw the enormous value of Eureqa. Leveraging publicly available data sets through 2014, in a matter of a few hours Eureqa created classification models unique to each industry (retail, finance, technology, etc.), and we plugged individual companies into the models to predict whether the stock would achieve excess return for 2015. We then hypothetically created a simple, equal portfolio of the predicted “overachievers”. Remarkably, Eureqa’s anticipated winning portfolio achieved a compound excess return of 14.1% for the following year, compared with the S&P 500’s disappointing -.7%. Not only was our portfolio’s performance exceptional, but so was our fundamental understanding of the causes of its success. We could convey to our hypothetical clients, bosses and others that not only did our strategy work this year, but it’s likely to work again next year, because some of the key drivers of excess returns for stock X are variables Q, R, S, T, U and V, and this is how it’ll move in the context of the current economy. In a matter of hours, with Eureqa at my side, a graduate student in tissue motion modeling transformed into a powerful financial analyst with a theoretical market-beating investment portfolio. Now, imagine what this application could do with even more data, and in the hands of a true industry expert…

Topics: Eureqa, Machine Intelligence, Financial Analysis

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