Quantocracy’s Daily Wrap for 08/19/2016

When testing trading strategies a common approach is to divide the initial data set into in sample data: the part of the data designed to calibrate the model and out of sample data: the part of the data used to validate the calibration and ensure that the performance created in sample will be reflected in the real world. As a rule of thumb around 70% of the initial data can be used for calibration

We are going to play around with a mixture distribution made up of a large proportion of ~N(0, 1) and a small proportion of ~N(0, 1+a). The wider distribution is "polluting" the standard normal distribution. We are going to see that mean absolute deviation is a more efficient estimator of the distribution's dispersion than standard deviation. We are also going to see some unexpected

Eric Crittenden was recently on Meb Fabers podcast and he tells a compelling story about the perils of survivor bias in backtesting. Erics story begins when he is an undergraduate working on a project for a quantitative finance course. The professor asked that the students develop a systematic investment program and get their hands dirty with backtesting. Eric decided to backtest a portfolio

Is your high dividend investment strategy based on buying companies with a high dividend yield and high dividend cover? Saving_chalkIf so you can do a lot better. In this article I summarise an interesting research paper that found the normal way most investors look at dividend income investing is all wrong. I also show you how to find ideas that fit with what the researchers found that really