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A-Team Insight Blogs

How ‘Deep Learning’ Could Make A Deep Impact On Trading

As a machine learning technique, “deep learning” has evolved enough to be useful for trading operations, according to Elliot Noma, managing director at Garret Asset Management. Noma will be moderating a panel on the uses and limitations of the technique, at the Intelligent Trading Summit in New York on June 8.

Deep learning expands on neural nets, which simulate the levels of communication within the human brain — the same neural communications that lead to the decisions comprising consciousness, Noma explains. Neural nets previously only had one or two layers, while deep learning-capable neural nets can have as many as 100 layers, which make these networks better suited for working on large sets of data, he adds.

“The error rates for neural nets on classifying images had been around 30 percent,” says Noma. “Over the past few years, using the technology for deep learning, the error rates have come down to the same as human beings — no more than 5 percent.”

With each layer in a deep-learning network containing hundreds of simulated neurons, and 100 or more layers possible, such a network can “assess large amounts of data, and be trained on multiple different types of data sets,” says Noma. “Different results from different models can be connected together.”

New data sets keep arriving, including Twitter feeds, sentiment analysis, political and government statements, satellite data and other social media information. Deep learning can analyse all of these data sets, and compare the resulting analyses. Deep learning can also add analyses into multiple models that a firm is using.

“The key terms are boosting, bagging and stacking, which allow you take different large data sets, combine them in different ways, combine the analyses in different ways and adjust the analyses so if a previous analysis has mistakes, the neural nets catch and correct those mistakes,” says Noma.

For trading operations, deep learning networks can back-test new data sets and examine how they fit among all the available data. “You used to have to hire an analyst or assign an analyst to learn about the data, understand how to clean the data, and understand how the data fits with other data sets,” he says.

However, trading operations managers must put some guidance and care into implementation of deep learning technology, Noma explains. “With any powerful technique, you must have some idea of what it can do and what its limitations are,” he says. “You need access to someone who has that experience, whether that’s homegrown or external, to understand what the appropriate applications are for variations.”

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