There's a certain sort of trader or market commentator who gets excited when they see the current market price or index level rising above or below a particular moving average, be it the 30-day, 60-day or 180-day moving average. And it's easy to see why, because when you look back at the historical graph, then looking back on when those crossing points happened was normally a good time to buy or sell. In general, if the spot price crosses a longer moving average from above, that's a signal to sell, and when it crosses from below, it's a good time to sell.
And actually, it turns out that if you did this, you would outperform a buy and hold strategy for all three moving averages. The best one is the 30 day moving average. I've built a little spreadsheet that models this trading strategy with the Russian RTS index, and you can download it from here. You can cut and paste your own time series, and try your own pairs.
Actually, as it turns out, the best trading strategy of all would have been to use the 26 day moving average and the spot price. This holds if you look at the RTS since inception. I got all excited about this, until I looked at the last twelve months, which shows me that I should have been using the 1 day moving average instead of the spot price, and the 84 day moving average. The 1 and 26 pair would have made me money, but much less. The 1 and 30, and the 1 and 60, and the 1 and 180 would not have done so well.
(You can investigate this using the data tables function of Excel. I haven't included it in the spreadsheet linked above, because it's very processor intensive, and makes the spreadsheet very unwieldy. If you want to play with it, I've included it in the spreadsheet here, but it may crash your computer, as it's a bit buggy, and needs quite a powerful computer. It might make sense for you to build your own data table in Excel, which is quite simple, and is a useful thing to learn.)
So what works for one period doesn't necessarily work for all periods. Also, when I applied this to the S&P, it turned out that almost none of the strategies outperformed a buy and hold strategy, and those that were, were fairly randomly distributed. (By the way, I haven't found any investigations of this in the literature or online, and would love to see references if anyone knows of anyone who has investigated this topic. I can't believe I'm the only person who has thought of it. And also I know that there are easier ways to do this than Excel, but my programming isn't up to it yet.)
What does this have to do with emerging markets? Well, it's more about investing in general. Beware of any rules of thumb, because they don't necessarily hold up when you test them against the data. It may be that these trading rules are only stable over a short period of time, and need to be continually updated. I suspect that a lot of high frequency trading programs use variants on these strategies, calculating optimal patterns, and then trading with them. The bottom line is that technical analysis is complex stuff, and should only be used if you have a lot of computational firepower to deploy - using crude tools will almost certainly lead to disappointing results.
And actually, it turns out that if you did this, you would outperform a buy and hold strategy for all three moving averages. The best one is the 30 day moving average. I've built a little spreadsheet that models this trading strategy with the Russian RTS index, and you can download it from here. You can cut and paste your own time series, and try your own pairs.
Actually, as it turns out, the best trading strategy of all would have been to use the 26 day moving average and the spot price. This holds if you look at the RTS since inception. I got all excited about this, until I looked at the last twelve months, which shows me that I should have been using the 1 day moving average instead of the spot price, and the 84 day moving average. The 1 and 26 pair would have made me money, but much less. The 1 and 30, and the 1 and 60, and the 1 and 180 would not have done so well.
(You can investigate this using the data tables function of Excel. I haven't included it in the spreadsheet linked above, because it's very processor intensive, and makes the spreadsheet very unwieldy. If you want to play with it, I've included it in the spreadsheet here, but it may crash your computer, as it's a bit buggy, and needs quite a powerful computer. It might make sense for you to build your own data table in Excel, which is quite simple, and is a useful thing to learn.)
So what works for one period doesn't necessarily work for all periods. Also, when I applied this to the S&P, it turned out that almost none of the strategies outperformed a buy and hold strategy, and those that were, were fairly randomly distributed. (By the way, I haven't found any investigations of this in the literature or online, and would love to see references if anyone knows of anyone who has investigated this topic. I can't believe I'm the only person who has thought of it. And also I know that there are easier ways to do this than Excel, but my programming isn't up to it yet.)
What does this have to do with emerging markets? Well, it's more about investing in general. Beware of any rules of thumb, because they don't necessarily hold up when you test them against the data. It may be that these trading rules are only stable over a short period of time, and need to be continually updated. I suspect that a lot of high frequency trading programs use variants on these strategies, calculating optimal patterns, and then trading with them. The bottom line is that technical analysis is complex stuff, and should only be used if you have a lot of computational firepower to deploy - using crude tools will almost certainly lead to disappointing results.




