David Aronson is a pioneer in machine learning and nonlinear trading system development and signal boosting/filtering.
He is author of “Evidence Based Technical Analysis” and his most recent book "Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments" is an in-depth look at developing predictive-model-based trading systems.
He was also an adjunct professor of finance, regularly teaching MBA and financial engineering students a graduate-level course in technical analysis, data mining and predictive analytics.
In this episode David shares research into the effectiveness of indicators to identify Bull and Bear markets; he’s tested a large number of indicators and combinations with some interesting results! We also discuss issues with data mining, conditions where traditional methods of measuring data mining levels can be problematic and then finish up with the future state of Technical Analysis.