Machine Learning and the Stock Market

Abstract

Practitioners allocate substantial resources to technical analysis whereas academic theories of market efficiency rule out technical trading profitability. We study this long-standing puzzle by designing a machine learning algorithm to search for profitable technical trading rules while controlling for data-snooping. Our results show that an investor can find profitable technical trading rules using past prices, and that this out-of-sample profitability decreases through time, showing that markets have become more efficient over time.

Keywords: Technical trading, Machine learning, Big data analysis

JEL Classification: B26, G12, G14, C58, N20