Evidence-Based Technical Analysis: Part 1 - Science and Inference

This episode comprised of excerpts from David R. Aronson's book, "Evidence-Based Technical Analysis," which focuses on applying the scientific method and statistical inference to trading signals . The author, an adjunct finance professor and former proprietary trader, argues that much of subjective technical analysis ( TA ) is unreliable, akin to pseudoscience, because its claims are often vague, unfalsifiable, and susceptible to cognitive biases like overconfidence and the confirmation bias . In contrast, Aronson champions Evidence-Based Technical Analysis (EBTA) , which requires objective, well-defined rules that can be rigorously back-tested and statistically evaluated to determine their actual predictive power, differentiating real insights from chance or data-mining bias . The book systematically outlines the methodological and philosophical foundations for this objective approach, explaining concepts like deductive logic , falsifiability , and how to identify and avoid illusory correlations in market data