Alpha Momentum Strategy - Portfolio Report, Performance, Commentary – January 2019, Second Half

As per The Alpha Momentum Strategy:

Both the intermediate and long term trends of US stocks are lower. As such, the portfolio will remain 100% in cash/equivalents (no stock market exposure).

Portfolio Performance since Website Inception

Commentary: It was a tumultuous time to be invested in US stocks in the 4th quarter of 2018 – domestic equities saw some real volatile price action! With moves in indices as aggressive as they were, most medium term traders who followed their daily PnL likely experienced several instances of feeling like a hero relative to the indices one day, and a zero the next (assuming a non-trivial degree of active share, of course).

Volatility in stock prices can cause stress on investors and traders, and it is in times of stress when investors and traders often make their worst decisions. It is partially for this reason that systematic trading and investing confers an edge for those who are willing to pursue it.

By relying on systems that have been well researched and tested, that have demonstrated edge, and those that are robust and evidence based, a trader or investor is able bypass the effects of this stress on their trading process. So long as the trader doesn’t override his system or make a careless mistake, the elevated stress that the trader feels makes absolutely no difference whatsoever on the investments in his accounts. By circumventing this “emotional trap”, systematic traders gain a meaningful edge over the narrative driven mainstream trading community.

Side note: See the article at the link below. This is a good piece by Corey Hoffstein on robusticity with regard to a systematic dual momentum approach (as outlined by Gary Antonacci). The Alpha Momentum Strategy, outlined on this website, “borrows” several concepts from Antonacci’s work (among others’). Hoffstein points to the massive dispersion in performance that can occur between different parameterizations of the same system (e.g. use a 200 day moving average for one iteration, and a 150 day moving average for another iteration). This is precisely why we rebalance more than once per month and use several different parameterizations in our models - to (attempt to) avoid getting unlucky and ending up with the worst iterations in real time trading. We have written about this before on our blog (see that article HERE), and Hoffstein does a great job of covering this topic from another angle. See Hoffstein’s piece HERE.

Thanks for following along!