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The AI Delusion$
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Gary Smith

Print publication date: 2018

Print ISBN-13: 9780198824305

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780198824305.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 28 February 2021

Beat the Market II

Beat the Market II

Chapter:
Chapter 11 Beat the Market II
Source:
The AI Delusion
Author(s):

Gary Smith

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198824305.003.0013

Nowadays, technical analysts are called quants. Being overly impressed by computers, we are overly impressed by quants using computers instead of pencils and graph paper. Quants do not think about whether the patterns they discover make sense. Their mantra is, “Just show me the data.” Indeed, many quants have PhDs in physics or mathematics and only the most rudimentary knowledge of economics or finance. That does not deter them. If anything, their ignorance encourages them to search for patterns in the most unlikely places. The logical conclusion of moving from technical analysts using pencils to quants using computers is to eliminate humans entirely. Just turn the technical analysis over to computers. A 2011 article in the wonderful technology magazine Wired was filled with awe and admiration for computerized stock trading systems. These black-box systems are called algorithmic traders (algos) because the computers decide to buy and sell using computer algorithms in place of human judgment. Humans write the algorithms that guide the computers but, after that, the computers are on their own. Some humans are dumbstruck. After Pepperdine University invested 10 percent of its portfolio in quant funds in 2016, the director of investments argued that, “Finding a company with good prospects makes sense, since we look for under valued things in our daily lives, but quant strategies have nothing to do with our lives.” He thinks that not having the wisdom and common sense acquired by being alive is an argument for computers. He is not alone. Black-box investment algorithms now account for nearly a third of all U.S. stock trades. Some of these systems track stock prices; others look at economic and noneconomic data and dissect news stories. They all look for patterns. A momentum algorithm might notice that when a particular stock trades at a higher price for five straight days, the price is usually higher on the sixth day. A mean-reversion algorithm might notice that when a stock trades at a higher price for eight straight days, the price is usually lower on the ninth day. A pairs-trading algorithm might notice that two stock prices usually move up and down together, suggesting an opportunity when one price moves up and the other doesn’t.

Keywords:   algorithmic traders (algos), black boxes, convergence trades, flash crash, gold/silver ratio, high-frequency trading, in-sample data, out-of-sample data, quants, set-aside solution

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