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In essence, Eisenstadt created a quantitative ranking system where he used a six-month trailing performance of different stocks to find out that the top stocks were performing better than the bottom-ranking stocks.Įventually, over time, this type of “quantitative” investing approach evolved to become more and more sophisticated. In 1965, Sam Eisenstadt used a form of quantitative investing approach to try to assess stock performance. Quantitative investing started in the 1900s when Louis Bachelier published his doctoral thesis on “Theory of Speculation” modeling options.Įventually, Edward Thorp, considered the “father of quantitative investing”, was a mathematical professor at the New Mexico State University and University of California, Irvine where he predicted and simulated the blackjack card game in the casino. Today, more and more investors are looking into “quantitative investing” to enhance their returns. In the past, this type of investing approach was almost exclusively in the domain of sophisticated hedge funds. Machine-learning technologies, artificial intelligence, and the use of sophisticated programs and algorithms allow sophisticated traders and investors to analyze and process vast amounts of quantitative data analysis to make better investment decisions. With the advancement of technology and the availability of powerful computers able to process enormous quantities of data at lower costs, more and more investors have started using quantitative investing techniques and strategies to complement their primary investing approach. The main objective of “quantitative investing” is to make investment decisions or develop investment strategies by analyzing the historical quantitative data available.
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Some refer to quantitative investing as mathematical investing or systematic investing. Quantitative investing is a type of investment strategy where you use advanced mathematical modeling, data analysis, and computer systems to determine the best time to make an investment transaction.