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To Understand Machine Learning In Finance, Start With Structural Fundamentals

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Mark Melin
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Artificial Intelligence (AI) and its industrious cousin, Machine Learning (ML), are becoming ubiquitous in daily life – loaded to the gills in smart phones, seamlessly interwoven into social media and internet advertising as well as found in natural language processing. Despite being so prevalent, the use cases, both positive and negative, highlight an industry in its infancy. Finance is one next frontier, as intelligent computer applications will determine dominance of certain sectors, “making old tools obsolete,” noted Bank of America Equity and Quant Strategist Savita Subramanian. But where does an investment manager get started in this burgeoning field? Start by understanding the structure, with a new April 23rd research piece from BofA, “Signal over noise,” laying down the fundamentals.

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Mark Melin is an alternative investment practitioner whose specialty is recognizing the impact of beta market environment on a technical trading strategy. A portfolio and industry consultant, wrote or edited three books including High Performance Managed Futures (Wiley 2010) and The Chicago Board of Trade’s Handbook of Futures and Options (McGraw-Hill 2008) and taught a course at Northwestern University's executive education program.