It is inherently difficult to decide on a trading strategy or indicator. You have to decide on how often you want to trade, what you want to trade and what kind of indicators you will pursue. I deliberately avoided technical indicators when I started and only restarted looking a indicators recently for a mini-project. The model or system that I would use had to involve some amount of quantitative analysis. The rationale is simple. The financial markets are highly competitive with many people trying to make a living off speculation. Any single or combination of technical indicator(s) that can be easily picked up by a layman is unlikely to be consistently profitable over time, as there is no free lunch in this world.
Consider the example of moving average rules, i.e. where you go long if a moving average over the last n days crosses above another moving average over the last n+m days, and vice versa. If asset returns have momentum, i.e. follow trends, moving averages should ideally work. However, following market trends can be compared to a “hot potato” game where the last player catching the soon-to-end trend loses money.
Academics have in the past done considerable research on technical trading rules. Brock once tested the moving average rule on daily data from 1897 to 1986, on the Dow Jones Industrial Average. He found that using any two of the 1, 2 and 5-day short averages and the 50, 150 and 200-day long averages produced statistically significant profit over time. More recent research by Olsen found that profitability from moving average trading strategies have largely disappeared in the foreign exchange markets by the 1990s.
However, innovative uses of moving averages have spawned strategies that were profitable. Okunev and White used the difference between the short moving average and long moving average as a measure of momentum and showed that a strategy of going long currencies with the most momentum and shorting currencies with the least momentum produced significant profits. Moving average rules are also used with other technical indicators in more complicated strategies. For example, Neely used a technique known as “genetic programming” to successfully search for profitable trading rules, inclusive of the moving average crossover, over time.
Hence, it becomes apparent that the same technical indicator has to be implemented in an increasingly sophisticated manner in order to make money.
References
Christopher Neely, Paul Weller, and Rob Dittmar (1997). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Journal of Financial and Quantitative Analysis Vol 32, Issue 4, pg 405-426
Dennis Olson (2004). Have trading rule profits in the currency markets declined over time? Journal of Banking and Finance Vol. 28, pg 85-105
John Okunev and Derek White (2003). Do momentum-based strategies still work in foreign currency markets? Journal of Financial and Quantitative Analysis Vol. 38, Issue 2, pg 425-447
William Brock, Josef Lakonishok and Blake LeBaron (1992). Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance Vol. XLVII, Issue 5, pg 1731 – 1764
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The objective of Finance4Traders is to help traders get started by bringing them unbiased research and ideas. Since late 2005, I have been developing trading strategies on a personal basis. Not all of these models are suitable for me, but other investors or traders might find them useful. After all, people have different investment/trading goals and habits. Thus, Finance4Traders becomes a convenient platform to disseminate my work...(Read more about Finance4Traders)
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