Evidence-Based Technical Analysis
November 22nd, 2006The fallacy of technical analysis is interesting. How was I able to make money with these tools in the past? Luck, combined with a market that was trending mostly up; that’s how. In actuality, throwing chicken entrails against the wall would have roughly the same predictive capability as my multi period stochastics, moving averages and all the rest of it. I didn’t have to read a 528 page book to figure that out. I simply started to lose money.
If anyone out there is working on black box trading systems, Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals, by David R Aronson, looks like it could be invaluable. So, grease up the rotor on your propellerhead cap and find someone to slide the food and caffeine under the door:
Evidence-Based Technical Analysis is a breakthrough book in that it rigorously applies the scientific method and recently developed statistical tests to determine the true effectiveness of trading strategies, rules or systems discovered by data mining. Traditional technical analysis – as currently practiced – is more like a faith-based folk art than a science, the author asserts. These subjective interpretive methods cannot be back-tested or evaluated, yet many believe that they are effective. The author explains that because of various cognitive biases and illusions, such as hindsight bias, illusory correlations, etc., people often adopt beliefs that are unsupported by evidence or even contradicted by evidence. For example, the famous head and shoulders pattern – a cornerstone of traditional TA when tested objectively – has been shown to have no predictive power. Yet many TA texts and most TA experts believe in the pattern’s efficacy. To move technical analysis forward, the author proposes a new type of technical analysis, which he calls: evidence-based technical analysis or EBTA. Unlike traditional technical analysis, EBTA is restricted to objective methods whose historical profitability can be quantified and then rigorously scrutinized. The author provides a new statistical methodology specifically designed for evaluating the performance of rules that are discovered by data mining, a process in which many rules are back-tested and the best performing rule(s) is selected. Experimental results presented in the book show that data mining is an effective approach for discovering useful rules. However, the historical performance of the best rule (s) is upwardly biased – a combined effect of randomness and data mining. Thus new statistical tests are needed to make reasonable inferences about the future profitability of rules discovered by data mining. Most importantly, in a data mining case study the author evaluates more than 6,400 signaling rules applied to the S&P500 Index using these new tests. For technical analysts and traders, the book is a wake-up call to abandon subjective, interpretive methods and embrace an approach that is scientifically and statistically valid. For other traders, the rigorous testing of trading signals/rules may make their data mining efforts more productive and stimulate the development of new systems, signaling rules.
Related: The Magic Mystery Dot
OOH! Sexy new WordPress digs. About time!
Sexy in a weird way. Kevin’s got three balls. I prefer two balls, like at Waihopai. Hell, I’ll admit it … I’d rather have no balls.
HA. Most Kiwis wouldn’t even get that one.