In all my system development, I still have not been able to determine what universal underlying conditions significantly improve a system’s chances of outperforming buy-and-hold. Also, I have found very little discussion, so maybe R with some help from ttrTests can help answer my question of when I should just go buy-and-hold (a very pleasant situation for a money manager). Starting in this business in 1998, I have often said that I dream of a day when I can just buy and hold similar to Japan stocks 1980-1990, US stocks 1990-2000, and US bonds 1982-now.
Those who follow my blog or know me already understand my obsession with drawdown, but that obsession focuses more on client/manager psychology (Investing for the Long Run) rather than drawdown’s effects on tactical systems. I do not understand the industry’s focus on standard deviation. I have never had a client call me or even worse fire me because my standard deviation has increased. I know the argument is that higher standard deviations lead to higher drawdown, but as I show later in the post, this does not seem to be the case.
Clients call me or fire me because they have lost money, so if I can minimize the frequency, amplitude, and duration of drawdowns, then I can help/guide the client and reduce the worry, which is one main reason why they are paying me. Also, though I think that focusing on minimizing drawdown can meaningfully increase the chances of achieving their long term return objectives (Drawdown Control Can Also Determine Ending Wealth and Confidence, Ending Equity, and What I Can Do as the Money Manager), which is even more likely the reason why clients pay me.
How nice would it be if drawdown also determines an objective system’s success? To start the testing I thought I would use the fine work of David St. John on ttrTests (ttrTests 4th and Final Test) to get 100,000 bootstrapped samples from monthly S&P 500 data to examine drawdown, standard deviation, skewness, and compound returns on buy-and-hold versus a Mebane Faber 10-month moving average system. Since I am so biased, I will let you determine the significance of drawdown on the results.
Here is where I get some confidence in my belief higher standard deviation does not necessarily cause worse drawdowns. However, it is interesting that higher standard deviation has as high a correlation as drawdown with system out(under)performance (bottom right).
J. K. Galbraith provided all the answer one needs: "genius is a rising market".ReplyDelete
You might want to try a block-bootstrap approach for preserving some of the long-range dependence when extending the size of your sample of drawdownsReplyDelete