Machine learning models in finance

Introduction and background There has been interest in developing computer algorithms that improve automatically through experience (“machine learning”) for many decades. The term “machine learning” (ML) was popularized in 1959 by Arthur Lee Samuel[1], a pioneer in computer gaming and artificial intelligence who first developed a program able to improve its performance[2] playing checkers, which defeated a human player in 1962 running on an IBM 7094[3], one of the first commercially available computers. Arthur L. Samuel using an IBM 7094.   Research on ML algorithms, and the increasing available computer power, has allowed addressing more complex problems, breaking milestones of performance on tasks that were once deemed either too complex or simply out of reach for non-human “intelligent” systems. In the past two decades, IBM’s DeepBlue defeated in 1997 the then world chess champion Garri Kasparov, using a program which performed over 200 million calculations per second, a brute force approach instead of a machine learning algorithm[4]. Nevertheless, outperforming human intelligence at a complex task was an important milestone for a computer program, and in the following years other ML programs mastered other complex tasks that previously seemed out of reach for non-human intelligent systems. Watson, a natural language question-answering… continue reading

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