Back in 2004, when Wall Street was in full subprime-craze mode, a reclusive stock picker named Michael Burry read the fine print—and made a fortune. In an excerpt from his new book, Michael Lewis explains why Burry saw what no one else did.
People with Asperger’s couldn’t control what they were interested in. It was a stroke of luck that his special interest was financial markets and not, say, collecting lawn-mower catalogues. When he thought of it that way, he realized that complex modern financial markets were as good as designed to reward a person with Asperger’s who took an interest in them. “Only someone who has Asperger’s would read a subprime-mortgage-bond prospectus,” he said.
The types of jobs that pay middle-class wages have shifted since 1980. Fewer of these positions are in male-dominated production occupations, while a greater share are in workplaces more open to women.
“Again, before starting this post, I'd like to inform readers that the book Quantitative Trading With R, written by Harry Georgakopoulos, with contributions from myself, is now available for order o...”
Via M. Edward (Ed) Borasky
Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culprit. We then argue that the real question is whether such data contain more predictability than price returns themselves: our backtest yields a performance of about 17bps per week which only weakly depends on the kind of data on which predictors are based, i.e. either past price returns or Google Trends data, or both.
Last week I travelled to Seattle and had the great honor of meeting Ed Seykota and watching him give a presentation to the Puget Sound chapter of the Market Technicians Association. Ed is without doubt one of the most renowned trend followers in market history, and came to prominence for many through arguably the most … Continue reading »
Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises. Here, we investigate whether data generated through Internet usage contain traces of attempts to gather information before trading decisions were taken. We present evidence in line with the intriguing suggestion that data on changes in how often financially related Wikipedia pages were viewed may have contained early signs of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of decision making.
by Daniel Hanson Last time, we looked at the four-parameter Generalized Lambda Distribution, as a method of incorporating skew and kurtosis into an estimated distribution of market returns, and capturing the typical fat tails that the normal distribution cannot. Having said that, however, the Normal distribution can be useful in constructing Monte Carlo simulations, and it is still commonly found in applications such as calculating the Value at Risk (VaR) of a portfolio, pricing options, and estimating the liabilities in variable annuity contracts. We will start here with a simple example using R, focusing on a single security. Although perhaps...
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