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Dear Editor,

This is a great example of how the most basic technical analysis can have a profound affect on your investment strategy’s results.

Maybe it has been mentioned already (certainly it has in the trading system literature), but I thought I should point out some concerns to readers who are about to implement such a strategy:

2. During periods of sideways movement in the market, moving average systems give conflicting signals that turn out to be false.  One way to reduce this impact is have longer term moving averages.  Unfortunately, this also increases the lag identified in paragraph one above.
3. Because of the inherent lags in a trend-following system, users will tend to discount it and lose faith in its capabilities.  In sideways markets you get chopped up; in long trends you make your money.  Knowing how a system works is one thing; following it takes some real bravery and determination.
4. There is a significant bias in Shiller’s S&P 500 data, known as survivorship bias.  For data that old, it is impossible to identify the correct value of the index, since the index is comprised of the companies that survived, and doesn’t include the companies that failed.  Survivorship bias gives a nicer result than what would have been experienced.  S&P only has the survivorship-free data going back to 1950, but the last time I looked they don’t report a survivorship bias free index.  Also, if you are using a dividend total return index, but in your actual investing don’t have the same yield level in your equity investments as in the index, then a substantial portion of the return will have been lost.
5. There is also a problem with using average closing prices rather than the last month’s closing price.  If you are trading on a signal reported on the last day of the month, then you cannot possibly have purchased the average close of the month just passed.  Your testing results have to be based on the next available trade date after you confirmed the signal.  Also, if you used the system but instead used the next month’s average close as the price at which you invested or sold, how do you execute such a trade?  I would expect most readers do not want or are not able to implement such a strategy.  Therefore, test a system that you would actually have a high degree of likelihood of implementing in real life, across all your client accounts. 
6. I suspect that Shiller had a dearth of data for the S&P 500 and took what data he could, as an average, since many data was suspect and this is a method of removing outliers and bad data points. Bad data would have been rampant in the earliest years, so he rightly used a method that continued into the present to be consistent in his data cleaning.  Also, Shiller is an economist, not an investment manager and he constructed an index for his purposes.
7. When testing any system, I advise against optimizing parameters.  The reason is that parameters change over time.  Also, any parameter that has wide swings in results is just not a good parameter to use in a system.  If you think that changing from a 4-period moving average to a 5-period moving average will actually double your profits in the future as maybe your optimization has told you, then you are in for a surprise.
8. What is not shown is how a simple trend following system would have done if it were trained in the 1990-to-present era, when tested “out of sample” through the 1970s.  Ted?
9. One of Ted’s great contributions has not been commented on: his use of the risk parameter of maximum drawdown.  They say that if you are winning in Vegas, take your profits as soon as possible since the longer you play, the more likely you are to lose it back to the house.  Well, the corollary to the investment market is the longer you live, the higher the likelihood that you will experience its worst loss possible.  That is the concept of maximum drawdown.  Buy-and-hold will always experience the maximum drawdown, and if you want to avoid that scenario with your clients, you have to be ready to pull the trigger and go to cash or all-bonds.  Ted gives a simple signal that we can all implement to avoid those drawdowns.

Ted does a great job (Ockam would be proud) of pointing out that buy-and-hold or its modern equivalent “asset allocation with semi-annual rebalancing” just won’t do the trick of avoiding major losses.  However, a good long-term trend indicator that is easy to follow as well as implement for your clients will help you take steps to avoid catastrophic loss.

Best regards,

Patrick A. Sullivan

Ted Wong replies:

Patrick,

Thank you for your feedback and kind words. I am flattered and honored.  I don't have much to add to your comments since they are as thoughtful as they are rich in content. I have a few words regarding your points (7) to (9).

(1) First, I did use EMA to reduce the lags of SMA.

(7) I will have something to add to the topic of optimization in Part 3 (which I am writing in the next two weeks). But you are right - using in-sample data to optimize the system and out-of-sample data to validate the system would only work if the markets are static.

(8) Indeed, if one uses data from the 1930s and the 1940s to train the MAC system, it may fall apart in the 1950s and 1960s. I will have more to say about this in Part 3.

(9) In Part 2, I show that market exposure itself is a risk because it subjects investors to the volatility of the markets.

Thanks again for your inspiring comments. Ockam's principle echoes the tenet in modern physics of relying more on the elegance of simplicity as proof of any theory, especially when you cannot conduct experiments to validate it (like the String Theory).

Ted

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