This is called spurious correlation. It's well known in financial / economic time-series analysis. The lesson is that you never measure the correlation between

This is called spurious correlation. It's well known in financial / economic time-series analysis. The lesson is that you never measure the correlation between the PRICE LEVELS of products, instead you measure the correlation between the daily/weekly/etc CHANGE IN PRICE LEVELS. A famous example of this: The tale of David Leinweber, which is related in the excellent new book "Quantitative Value," illustrates this point about "stupid data miner tricks." Leinweber sifted through a United Nations CD covering the economic data of 140 countries. He found that butter production in Bangladesh explained 75 percent of the variation of the S&P 500 Index. Not satisfied, he found that if he added a broader category of global dairy products, the correlation would rise to 95 percent. Then he added a third variable, the population of sheep, and found that he had now explained 99 percent of the variation in the S&P 500 for the period 1983-'99. (http://www.cbsnews.com/news/what-butter-production-means-for-your-portfolio/ http://www.cbsnews.com/news/what-butter-production-means-for...)