Quantocracy’s Daily Wrap for 01/03/2017

What if you had a tool that could help you decide when to apply mean reversion strategies and when to apply momentum to a particular time series? Thats the promise of the Hurst exponent, which helps characterise a time series as mean reverting, trending, or a random walk. For a brief introduction to Hurst, including some Python code for its calculation, check out our previous post. Even if you

This is a summary of the recent performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Read more about our backtests or let AllocateSmartly help you follow these strategies in

Happy new year! I have noticed that my quantitative posts get the most readership and discussion. So this year, Ill be posting a lot more research and will start the year off by exploring momentums robustness. There are two good ways to test the robustness of a rules-based trading strategy: The test of time – how does the strategy behave in different market regimes? Parameter sensitivity

Overfitting the data is a serious problem when constructing financial models. One way to guard against this is to have lots of data. This helps you determine if your results are robust by seeing how they hold up over different time periods. But this assumes the underlying market dynamics remain stable over time. That is not always the case. Gogi Gerwal gives a good example of how you may be misled

In a recent posting, I compared the prices of US active mutual fund to estimates of future pre-fee excess returns. In summary, I found that the annual expenses of most active funds met or exceeded a generous estimate of their potential before-fee excess returns. That is, many funds look like theyre priced to fail. What I didnt include in that posting, though, was detail on how I derived