We are often asked how to aggregate RavenPack data at a country level for macro strategies – like trading indices. My latest research tackles this problem and the results are excellent on the S&P 500.

Specifically, in my testing dating back to September 2007, I was able to produce news-based strategies that had Information Ratios (IRs) of 1.45 versus a buy-and-hold S&P 500 strategy IR of 0.23 and a pure return-driven model IR of 0.73. I was also able to reduce maximum drawdowns in news-based models by over 70% compared to a buy & hold strategy.

To construct my news-based models, I produced six different indicators per country or economy. These indicators are at topic level – Corporate, Business, Economic, Societal, Political and Environmental. I then used these indicators – on their own and in conjunction with traditional price and momentum indicators – to time my entry and exit from trades in long-only and long/short strategies on the S&P 500.

The overview of my results can be seen in the figure below. It’s worth noting that aggregate corporate news is a key driver of the performance on these strategies.

In future research I intend to use the methodology described in my paper for similar studies on other markets and asset classes. I believe these indicators, perhaps weighted differently, could prove valuable in many systematic macro strategies.

Amongst other things, I’ll be presenting these results at the Global Derivatives USA conference in Chicago next month and on a webinar in December.