Quantocracy’s Daily Wrap for 08/10/2018

Use of machine learning in the quantitative investment field is, by all indications, skyrocketing. The proliferation of easily accessible data – both traditional and alternative – along with some very approachable frameworks for machine learning models – is encouraging many to explore the arena. However, these financial ML explorers are learning that there are many ways in which using ML to

All that began to change around 2000 with the advent of high frequency data and the concept of Realized Volatility developed by Andersen and others (see Andersen, T.G., T. Bollerslev, F.X. Diebold and P. Labys (2000), The Distribution of Exchange Rate Volatility, Revised version of NBER Working Paper No. 6961). The researchers showed that, in principle, one could arrive at an estimate of

James Paulsen, chief investment strategist at Leuthold Group, sees trouble brewing in the growing disconnect between US equity sectors. He told CNBC earlier this week that correlations among US equities is unusually low and flashing a warning signal. Thats an especially dangerous sign when the stock markets valuation is so high. Lets dig deeper into the topic by crunching correlations on

We can decompose the returns process Rt as follows: While the left hand side of the equation is essentially unforecastable, both of the right-hand-side components of returns display persistent dynamics and hence are forecastable. Both the signs of returns and magnitude of returns are conditional mean dependent and hence forecastable, but their product is conditional mean independent and hence

We are looking for 3-4 traders (or trading teams) to showcase as Strategy Managers on our Algorithmic Trading Platform. Ideally these would be systematic quant traders, since that is the focus of our fund (although they dont have to be). So far the platform offers a total of 10 strategies in equities, options, futures and f/x. Five of these are run by external Strategy Managers and five are run