Charting by machines

Scott Murray, Yusen Xia and Houping Xiao

Journal of Financial Economics, 2024, vol. 153, issue C

Abstract: We test the efficient market hypothesis by using machine learning to forecast stock returns from historical performance. These forecasts strongly predict the cross-section of future stock returns. The predictive power holds in most subperiods and is strong among the largest 500 stocks. The forecasting function has important nonlinearities and interactions, is remarkably stable through time, and captures effects distinct from momentum, reversal, and extant technical signals. These findings question the efficient market hypothesis and indicate that technical analysis and charting have merit. We also demonstrate that machine learning models that perform well in optimization continue to perform well out-of-sample.

Keywords: Efficient market hypothesis; Machine learning; Deep learning; Charting; Technical analysis; Cross-section of stock returns (search for similar items in EconPapers)
JEL-codes: G11 G12 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304405X2400014X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:153:y:2024:i:c:s0304405x2400014x

DOI: 10.1016/j.jfineco.2024.103791

Access Statistics for this article

Journal of Financial Economics is currently edited by G. William Schwert

More articles in Journal of Financial Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().