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History of Machine Learning in Finance and Economics

Summary:
Derek Snow in this piece: Finance and Economics have been slow to adopt modern machine learning techniques. Nevertheless, the researchers and practitioners in these respective domains have been essential in laying the bedrock of what we now refer to as machine learning. The use of mathematics in the service of social and economic analysis dates back to the 17th century. Then, mainly in German universities, a style of instruction emerged which dealt explicitly with the detailed presentation of data as it relates to public administration. Gottfried Achenwall lectured in this fashion in the mid-18th century, coining the term statistics. By the 19th century, we saw a flurry of economic statistics, some of which gave rise to statistical learning methods. Then at the start of the 20th

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Derek Snow in this piece:

Finance and Economics have been slow to adopt modern machine learning techniques. Nevertheless, the researchers and practitioners in these respective domains have been essential in laying the bedrock of what we now refer to as machine learning. The use of mathematics in the service of social and economic analysis dates back to the 17th century. Then, mainly in German universities, a style of instruction emerged which dealt explicitly with the detailed presentation of data as it relates to public administration. Gottfried Achenwall lectured in this fashion in the mid-18th century, coining the term statistics. By the 19th century, we saw a flurry of economic statistics, some of which gave rise to statistical learning methods. Then at the start of the 20th century, French mathematician Louis Bachelier published his treatise Theory of Speculation that is considered the first scholarly work on mathematical finance.

In 1966 Joseph Gal in the Financial Analyst Journal wrote that ‘’It will soon be possible for portfolio managers and financial analysts to use a high-speed computer with the ease of the desk calculator’’[1]. Today, machine learning code has been streamlined; in less than 10-lines of code, you can create a close to state-of-the-art machine learning option pricing model with free online computing power. This is reminiscent of the 1970s, where not long after the creation of the Chicago Board Options Exchange, Black-Scholes option values could be easily calculated on a handheld calculator. We are not there yet, but it is in within reach. This article seeks to understand the use and the development of what we now refer to as machine learning throughout the history of finance and economics.

Amol Agrawal
I am currently pursuing my PhD in economics. I have work-ex of nearly 10 years with most of those years spent figuring economic research in Mumbai’s financial sector.

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