Technical Analysis in the Stock Market: A Review

35 Pages Posted: 24 May 2021

See all articles by Yufeng Han

Yufeng Han

University of North Carolina (UNC) at Charlotte - Finance

Yang Liu

Tsinghua University - School of Economics & Management

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Yingzi Zhu

Tsinghua University - School of Economics & Management

Date Written: May 21, 2021

Abstract

Technical analysis is the study for forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios, but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, but also discuss briefly recent studies that apply machine learning approaches, such as Lasso, neural network and genetic programming, to forecast returns both in the time-series and on the cross-section.

Keywords: Technical Analysis, Machine Learning, Genetic Programming, Cross-sectional Returns, Predictability

JEL Classification: G12, G14, G15

Suggested Citation

Han, Yufeng and Liu, Yang and Zhou, Guofu and Zhu, Yingzi, Technical Analysis in the Stock Market: A Review (May 21, 2021). Available at SSRN: https://ssrn.com/abstract=3850494 or http://dx.doi.org/10.2139/ssrn.3850494

Yufeng Han

University of North Carolina (UNC) at Charlotte - Finance ( email )

9201 University City Boulevard
Charlotte, NC 28223
United States

Yang Liu

Tsinghua University - School of Economics & Management ( email )

Beijing
China

Guofu Zhou (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

Yingzi Zhu

Tsinghua University - School of Economics & Management ( email )

Beijing, 100084
China
+86-10-62786041 (Phone)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
347
Abstract Views
797
rank
109,331
PlumX Metrics