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Quantitative Value, + Web Site: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray, Tobias E. Carlisle

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CHAPTER 10

Bangladeshi Butter Production Predicts the S&P 500 Close

“I always find it extraordinary that so many studies are made of price and volume behavior, the stuff of chartists. Can you imagine buying an entire business simply because the price of the business had been marked up substantially last week and the week before? Of course, the reason a lot of studies are made of these price and volume variables is that now, in the age of computers, there are almost endless data available about them. It isn't necessarily because such studies have any utility; it's simply that the data are there and academicians have [worked] hard to learn the mathematical skills needed to manipulate them. Once these skills are acquired, it seems sinful not to use them, even if the usage has no utility or negative utility. As a friend said, to a man with a hammer, everything looks like a nail.”

—Warren Buffett, “The Superinvestors of Graham-and-Doddsville”1

In 1995, David J. Leinweber set out to find the metric that best predicted movements in the U.S. stock market.2 He started with data about the annual closing price of the Standard & Poor's (S&P) 500 index for the 10 years from 1983 to 1993. He then consulted an archive of international data series published by the United Nations, which covered information like changes in interest rates, economic growth, and unemployment for all 140 United Nations (UN) member countries. Using a statistical technique called “regression analysis,” Leinweber sought ...

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