This year 2016, we have looked to be as transparent as we could on the performance of our strategies based on Machine-Learning. Although no real money has been invested into these strategies yet, during the course of the year performance has been monitored by Thomson-Reuters, as a trusted third-party.
We are close to year-end, and we have the pleasure of announcing three significant pieces of news:
- We are very proud to have signed an exclusive agreement with the highly respected firm Boussard & Gavaudan, by which these strategies will become investable under their umbrella, starting early next year.
- The strategies have benefited from this month in the US, both on the long-short and on the long sides.
- We will very soon be growing our team, increasing our research capacity to new domains in this greenfield territory of Artificial Intelligence applied to money management.
Overall, the month of November has been robust. When we look under the bonnet at the stocks that have been picked both on the long and short legs, we see few mistakes. As usual, there is no sector bet. Our goal is to identify the winners and the losers within each broad industrial sector.
Let us restate what we deliver, on a log-return basis (log-returns significantly undershoot returns, but transaction costs are not incorporated):
- We focus on two long-only non-leveraged strategies: the “hedged” one which gradually turns away from the machine-learning algorithm to go into cash when the SP500 becomes too volatile and the “no hedge” one which switches to the SP500 when the SP500 volatility rises.
|Price based – ex dividends||SP500||Long – cash hedged||Long – no hedge|
- We run a Long-Short non directional, non leveraged strategy, consistent with the UCITs constraints.
|Long-Short Price based|
Our approach is always to remain humble and down to earth, so let us not expect all months to look like November 2016!
We are available to discuss with you our work : firstname.lastname@example.org