Quantocracy’s Daily Wrap for 04/04/2018

This post is a bit of an experiment. My good friend Steven Wood and I started discussing some collaborations a few months ago. We hope that it ups the quality of our research and also brings some new insights into each of our philosophies. Below is his recent post, for which I helped provide some research on the performance of spin-offs, which was popularized by Joel Greenblatt in the mid

When traders set in-sample and out-of-sample periods for their backtests, its common just to pick some dates that split the data into a pre-defined percentage or range. The trouble with this approach is that it often doesnt take into account the type of market environments that exist in those periods. For example, your in-sample period may be very bearish, and if your out-of-sample period is

We have talked about outliers several times in this blog. Examples include how to detect them or how to transform the data to remove them. Here we have another technique to detect outliers in our big data set: the isolation forest algorithm. The idea behind the isolation forest method The name of this technique is based on its main idea. The algorithm isolates each point in the data and splits

We wanted to take a moment to highlight two must read posts from Newfound Research. Newfound is a thought leader in the TAA space and we highly recommend following them now. The Diversification Dangers of DIY Tactical and Diversifying the What, How, and When of Trend Following Newfound outlines three ways in which TAA investors often fail to diversify: WHAT they trade: failing to diversify

We develop a strong diagnostic for bubbles and crashes in bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfes law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal