Date of Award

Summer 8-2019

Degree Type

Dissertation

Degree Name

Ph.D.

Degree Program

Financial Economics

Major Professor

Neal Maroney

Second Advisor

Ronnie Davis

Third Advisor

Kabir Hassan

Fourth Advisor

Yuki Naka

Fifth Advisor

Duygu Zirek

Abstract

Academic research has shown throughout the years the ability of technical indicators to convey predictive value, informational content, and practical use. The popularity of such studies goes in and out over the years and today is being recognized widely by behavioral economists. Automated technical analysis is said to detect geometric and nonlinear shapes in prices which ordinary time series methods would be unable to detect. Previous papers use smoothing estimators to detect such patterns. Our paper uses local polynomial regressions, digital image processing, and state of the art machine learning tools to detect the patterns. Our results show that they are nonrandom, convey informational value, and have some predictive ability. We validate our results with prior works using stocks from the Dow Jones Industrial Average for a sample period from 1925-2019 using daily price observations.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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