Quantocracy’s Daily Wrap for 08/19/2015

My second post on this blog was a look at mean reversion, Is mean reversion dead? Given I am using a new data provider(Premium Data), it has been almost two years since that post and there have been other articles on this recently, I figured it was time to check again. The research will focus on Russell 1000 stocks since 1995. The test is back to 1995 covers 3 bull markets and 2 bear ma

Given a constant speed, time and distance are fully correlated. Provide me with the one, and Ill give you the other. When two variables have nothing to do with each other, we say that they are not correlated. You wish that would be the end of it. But it is not so. As it is, things are perilously more complicated. By far the most familiar correlation concept is the Pearson

Yesterday I had a coffee with a person I have known for 20 years. He has worked as a quantitative analyst, a trader and a finance professor for at least as that long. He is one of the most knowledgeable people I know. When he says something it is worth listening. What he said was (roughly), " I've come to believe that there is something to technical analysis".

Investing in the current environment is difficult. Most, if not all, asset classes have high nominal prices, suggesting low nominal expected returns. Not exactly exciting. And for many investors who are retired and/or have near-term liquidity needs, investing in equity exposureswhile necessary to generate higher expected returnsalso prevents many investors from sleeping at night!

We assume that the drift in the returns of asset prices consists of an idiosyncratic component and a common component given by a co-integration factor. We analyze the optimal investment strategy for an agent who maximizes expected utility of wealth by dynamically trading in these assets. The optimal solution is constructed explicitly in closed-form and is shown to be affine in the co-in