Quantocracy’s Daily Wrap for 06/07/2017

In 2012 we published a whitepaper entitled Adaptive Asset Allocation: A Primer in which we built upon the simple, robust momentum framework proposed by Mebane Faber in his 2009 study Relative Strength Strategies for Investing. Our approach utilized a portfolio optimization overlay to this framework which served to stabilize and strengthen the dynamic mix of high-momentum assets,

I assume that you're reading this because you are one of many people who were affected by the changes to Yahoo Finance data in May (2017). Not only did the URL change, but the actual data changed as well! The most noticeable difference is that the adjusted close column is now only split-adjusted, whereas it used to be split- and dividend-adjusted. Another oddity is that only the close prices

Negative month for the State of Trend Following report, putting the YTD well in the red. Please check below for more details. Detailed Results The figures for the month are: May return: -3.14% YTD return: -7.44% Below is the chart displaying individual system results throughout May: StateTF May And in tabular format: System May Return YTD Return BBO-20 -8.18% -14.54% Donchian-20 -5.35% -16.26%

This paper compares and contrasts factor investing and sector investing, and then seeks a compromise by optimally exploiting the advantages of both styles. Our results show that sector investing is effective for reducing risk through diversification while factor investing is better for capturing risk premia and so pushing up returns. This suggests that there is room for potentially fruitful

This is the third in a new advanced series of posts written by Imanol Prez, a PhD researcher in Mathematics at Oxford University and an expert guest contributor to QuantStart. In this post Imanol applies the Theory of Rough Paths to the task of handwritten digit classificationa common task for testing the effectiveness of machine learning models. – Mike. As we discussed in the last article,