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