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.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-3850 |
| Date | 05 August 2019 |
| Creators | Lutey, Matthew |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of New Orleans Theses and Dissertations |
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