Astrikos is free this week so I cut and pasted something from there here that should interest many. Astrikos hardly ever has a free week, so it's definitely worth checking out. To get in the rest of the week the username is free and the password is pass. Cheers. By Rainsford Yang Wednesday, April 23rd 2003 9:00pm ET There's been a lot of press recently concerning the fact that the S&P500 has closed above its 200-day moving average, which sounds a lot more impressive than it really is. We've done extensive research on moving averages, and as far as the major stock market averages are concerned, a close above the 200-day moving average is absolutely meaningless. In fact, simple moving averages in general, except for the very short-term ones, should be disregarded in my opinion. Our studies clearly show that the best performing moving average (the 1-day average) isn't an average at all - it's simply today's close. If it's higher than yesterday's, the trend is up. If it's lower, the trend is down. This most basic of 'strategies' blew away all longer-term moving average strategies by a mile, as you'll see by the results of the studies below. And you can bet these results won't make it to the mainstream media anytime soon. No one would believe it. We're going to review three typical strategies for employing moving averages - the 'penetration', the 'crossover' and the 'slope' - beginning with a brief explanation of each strategy, and followed by a short list of the moving averages that produced the best returns in terms of buying and selling the S&P500. In every case, simple moving averages were used, and performance figures are based on the daily S&P500 close beginning in 1970 and finishing at the present. Moving Average Penetration This strategy goes long the market when the S&P500 closes above its X-period moving average (upside penetration), and goes short the market when the S&P closes below its X-period moving average (downside penetration). Best performing moving averages: 2-day moving average: +54,859% 3-day moving average: +46,038% 4-day moving average: +10,371% Other "popular" moving averages: 10-day moving average: +974% 200-day moving average: +601% 50-day moving average: +370% Clearly, the shorter the moving average, the more effective the 'penetration' strategy. Using a 2-day average would have returned nearly 55,000% since 1970, meaning $10,000 would have turned into a cool $5.5 million. Approximately 40% of the trades were winners, and the average winner was 1 1/2 times the average loser. Of course, with nearly 4,000 trades in 33 years, you'd be establishing a new position roughly every other day, making you very popular with your broker. The oft-mentioned 200-day moving average penetration, that gets more press than any other moving average setup, underperformed buy & hold significantly. Why does it still warrant such attention? Moving Average Crossover This strategy goes long the market when an X-period moving average crosses above a Y-period moving average, and goes short the market when an X-period moving average crosses below a Y-period moving average. Best performing combinations: 60/190 moving averages: +798% 2/200 moving averages: +792% 2/180 moving averages: +690% Other "popular" combinations: 50/200 moving averages: +648% 60/180 moving averages: +538% 40/200 moving averages: +502% The best performing crossover signals all occurred using a very long-term average (180 days or more) and a short-term average (anywhere from 2-60 days). I've also included some of the more popular crossovers for comparison, including the 50/200 and 60/180. However, an important point to keep in mind when it comes to all of the crossover strategies is that every single one underperformed buy & hold, most by a significant amount. Therefore, we can safely conclude that the concept of using two moving averages that cross one another to trigger buy and sell signals does not work. Moving Average Slope This strategy goes long the market when an X-period moving average is greater than the previous day's moving average (slope is positive), and goes short the market when an X-period moving average is less than the previous day's moving average (slope is negative). Best performing moving averages: 1-day moving average: +54,859% 2-day moving average: +20,912% 111-day moving average: +1,250% Other "popular" moving averages: 200-day moving average: +244% 20-day moving average: +143% 50-day moving average: +47% All of the popular averages (20-day, 50-day, 200-day) underperformed buy & hold significantly, which suggests that looking at the slope of these averages is not beneficial in the slightest. Again, it's the very short-term averages that performed best, beginning with the 1-day (which isn't really an average at all), followed by the 2-day and then the 111-day. That last one is interesting, since there were a number of moving averages in the 110-120 day range that outperformed buy & hold. If you were thinking of including a longer-term moving average in your work, one in this range would be more beneficial than the 200-day average. You might also notice that the 1-day moving average performance is the same as the 2-day 'penetration' performance, and that's because they are the same. Think about the concept of the penetration strategy. If today's close is higher than yesterday's close, it'll also be higher than the 2-day average of today's close and yesterday's close. Similarly, if today is lower than yesterday, it'll also be lower than the 2-day average of today and yesterday. Hence, the 1-day slope and the 2-day penetration strategy trigger the exact same signals. Both simply take into account solely what happened today, and use that as the basis for what may occur tomorrow. Clearly, if you're utilizing moving averages in your trading methodology, it's most important to see what's occurring right now, as opposed to what's occurred on average over the past X amount of days. This also tends to suggest that exponential and/or weighted moving averages, which give more weight to current data and less weight to past data, would probably be more effective overall than simple moving averages. I'll examine this in greater detail in a future column.