“Luck is the residue of design”
There is a lot more luck involved in investing than people think. I’m not saying there isn’t skill involved in investing or that there aren’t ways to outperform the market over time. Even if you have a process that can be shown to outperform the market over long time periods, there can be a great deal of variation in returns from year to year. A well designed investment model can certainly help manage some of that luck, but it is difficult to eliminate it entirely.
Several years ago I looked at some momentum models in a unique way. Most research on equity momentum involves buying a large basket of stocks (the top decile or top quintile, for example) and rebalancing on some preset schedule (like monthly). There are a lot of active strategies that aren’t run that way so we wanted to find out how much variation there could be in owning a sub set of the high momentum stocks and rebalancing more frequently. Do you need to own all of the big winners in order for a momentum strategy to work?
In order to attempt to answer that question I created a process that picked stocks at random out of a high momentum basket and held them until they were weak. (You can read about it in more detail in the original whitepaper I published by clicking here.) In the test shown in this post, I am using a universe of the top 1000 market capitalization stocks traded on US exchanges. That eliminates the problem of holding very illiquid stocks; every stock in that universe should have sufficient liquidity to trade without major slippage costs. Each week the stocks were ranked by their trailing 12 month performance, which is a very standard way to measure momentum. Anything that ranked in the top decile based on the trailing 12 month performance rank was considered to be “eligible” for the portfolio. Anything that ranked below the top quartile of the ranks was eliminated immediately from the portfolio. The portfolio was set up to hold 50 stocks.
Most tests would just pick the top ranked stock when something needed to be bought. The difference in my test was we picked something at random from the “eligible” list. There were about 100 eligible stocks each week – the top 10% of the 1000 stock universe (excluding buyouts, etc…). Then I ran the process 100 times to create 100 different equity curves. It would be the same thing as giving the eligible list to 100 different people each week and telling them they can pick anything they want off the list as long as they don’t already own it. You are going to wind up with 100 totally different portfolios over time with the only thing in common being the process of buying high momentum stocks and selling them when they get weak.
The results of the 100 trials are summarized in the table and graph below (click the image to enlarge). The table shows the return of the S&P 500 as well as the average return of the 100 trials each year. There is also a section that shows where the quartile breaks occur each year. The graph shows the returns year by year with the red bar being the average return, the box showing where the mid quartiles are, and the whiskers extending to the min and max returns. The green dot is the S&P 500.
The biggest thing that should jump out at you is that even by picking stocks at random, all 100 trials outperform the S&P 500 Total Return Index. That is pretty amazing. The actual stocks you put into the portfolio don’t matter as much as you would think. The process is what is important. Constantly cutting the losers and buying winners is what drives the performance. The process helps to manage the luck of stock picking over time!
You can also see that from year to year the returns can vary quite a bit. So what is the difference? Literally, luck. Some years the process is lucky, some years it isn’t, but when the process is solid it works out over time. It is also a good reminder of why it is so important to focus on the process rather than the results over a short time period. Just because a process underperforms for a year it doesn’t mean it is “broken.” This, unfortunately, is how most investors think. There is so much research on poor investor behavior I’m not even going to attempt to address it here!
A solid investment process winds up managing the luck that exists in implementing the system over short time periods. Momentum is a robust enough factor to handle picking stocks and random from a highly ranked sub set of securities and then selling them when they are weak. What happens from year to year is a lot about luck, but over time the design of the process overcomes the luck.
The returns used within this article are the result of a back-test using indexes that are not available for direct investment. Returns do include dividends, but do not include transaction costs. Back-tested performance is hypothetical (it does not reflect trading in actual accounts) and is provided for informational purposes to illustrate the effects of the discussed strategy during a specific period. Back-tested performance results have certain limitations. Such results do not represent the impact of material economic and market factors might have on an investment advisor’s decision making process if the advisor were actually managing client money. Back-testing performance also differs from actual performance because it is achieved through retroactive application of a model investment methodology designed with the benefit of hindsight. Dorsey, Wright & Associates believes the data used in the testing to be from credible, reliable sources, however; Dorsey, Wright & Associates, LLC (collectively with its affiliates and parent company, “DWA”) makes no representation or warranties of any kind as to the accuracy of such data. Past performance is not indicative of future results. Potential for profits is accompanied by possibility of loss.