Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals
John Wiley & Sons, 11.07.2011 - 544 Seiten
Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals. Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining.
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DataMining Bias The Fools Gold of Objective
Theories of Nonrandom Price Motion
Case Study of Rule Data Mining for the SP 500
Case Study Results and the Future of
Proof That Detrending Is Equivalent
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behavior behavioral finance belief best rule biased bootstrap Chapter cognitive complex compute conclusion confidence interval confirmation bias correlation daily return data mining data-mining bias defined detrended deviation divergence earned error estimate evaluated evidence example expected return fact false falsified financial markets forecast future head-and-shoulders pattern hindsight bias hypothesis test illustrated in Figure indicator induction infinite number investors knowledge large number Law of Large logical luck mean return Monte Carlo permutation moving average null hypothesis number of observations number of rules objective observed performance occur out-of-sample outcomes output values p-value percent performance statistic population parameter positive predictive power Premise price changes prior probability problem profitable random variable rate of return rational risk rule’s expected rules back tested rules tested sample mean sample statistic sampling distribution scientific signals statistical inference statistical significance stocks subjective technical analysis testable theory threshold trading trend true valid words