Quantocracy’s Daily Wrap for 07/22/2015

Today we have a guest post from David Weilmuenster who I worked with while at Connors Research. A widely applied technique for scoring assets in rotational systems is to rank those assets by their price momentum, or return, over a given historical window and to rotate into the assets with higher momentum. This approach seeks to capitalize on the well-demonstrated tendency f

Yesterday we wrote a post showing a potential way to time the market using valuation-based signals. In the past we have also examined how to use momentum-based signals (moving average rules and time-series momentum) to time the market. A natural question is what happens when we combine the valuation-based signals with the momentum-based signals? Here at Alpha A

In the last article of the Time Series Analysis series we discussed the importance of serial correlation and why it is extremely useful in the context of quantitative trading. In this article we will make full use of serial correlation by discussing our first time series models, including some elementary linear stochastic models. In particular we are going to discuss White

The European Central Bank just released a research report that might be of some interest to readers of this blog. It turns out that Social Data can be useful in predicting the stock market (go figure!): Quantifying the effects of online bullishness on international financial markets [ECB] In our work, we develop a simple, direct and unambiguous indicator o

Mandelbrot has significantly contributed in many ways to the area of finance. He was one of the first who criticized the oversimplifications centered around the early stochastic process models of Bachelier utilizing normal distribution. In his view, markets were fractal and much wilder than classical theory suggests. Additionally, he was a profound critic of the efficient markets hypoth

Night Trading: Lower Risk but Higher Returns?