David Aronson Evidence Based Technical Analysis

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What is Evidence Based Technical Analysis

Evidence based technical analysis (EBTA) is dedicated to the proposition that technical analysis should be approached in a scientific manner. This implies several things. First, it is restricted to objective methods that can be simulated on historical data. Second, the historical performance statistics produced by such back-testing are then evaluated in a statistically rigorous fashion. In other words, profitable past performance is not taken at face value but rather evaluated in light of the possibility that back-test profits can occur by sheer luck. The problem of lucky performance is especially pronounced when many methods are back-tested and a best method is selected. This activity is called data mining. Though data mining is a promising approach for finding predictive patterns in data produced by largely random complex processes such as financial markets, its findings are upwardly biased. This is the data mining bias. Thus, the profitability of methods discovered by data mining must be evaluated with specialized statistical tests designed to cope with the data mining bias. EBTA employs such methods.

EBTA rejects all subjective, interpretive methods of Technical Analysis as worse than wrong, because they are untestable. Thus classical chart patterns, Fibonacci based analysis, Elliott Waves and a host of other ill defined methods are rejected by EBTA. Yet there are numerous practitioners who believe strongly that these methods are not only real but effective. How can this be? Here, EBTA relies on the findings of cognitive psychology to explain how erroneous beliefs arise and thrive despite the lack of valid evidence or even in the face of contrary evidence. Cognitive psychologists have identified various illusions and biases, such as the confirmation bias, illusory correlations, hindsight bias, etc. that explain these erroneous beliefs.

Thus EBTA relies on computerized methods for identifying patterns, and combining evidence into useful trading signals. Due to recent advances in computing and data mining algorithms it becomes possible for the modern technical analyst to amplify their research efforts and find the real gold. In other words, EBTA advocates a synergistic partnership between technical analysts and data mining computers to expand the valid base of knowledge called technical analysis. The union of humans and intelligent machines makes sense because the two entities have different but complimentary information processing abilities. Whereas human intelligence has a limited ability to engage in complex configural reasoning, which is required to identify valid predictive variables and combine them into a mathematical function, it can pose questions and proposed candidate variables. Whereas computer intelligence is ill equipped to pose questions and propose variables it has enormous capacities to identify relevant predictors and derive optimal combining functions.

However, this new approach to technical analysis will require that human technicians abandon some tasks they now do and learn a new set of analytical skills. While they will no longer try to subjectively evaluate complex information patterns, they will need to learn about the kinds of data transformations that produce variables that are most digestible to data mining computers. They will also need to learn which data mining approaches are most viable and which types of problems are most amenable to data mining.

David Aronson is the author of “Evidence Based Technical Analysis” (John Wiley & Son’s 2006).

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