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An Algorithm for Testing the Efficient Market Hypothesis
The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Stren...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3812129/ https://www.ncbi.nlm.nih.gov/pubmed/24205148 http://dx.doi.org/10.1371/journal.pone.0078177 |
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author | Boboc, Ioana-Andreea Dinică, Mihai-Cristian |
author_facet | Boboc, Ioana-Andreea Dinică, Mihai-Cristian |
author_sort | Boboc, Ioana-Andreea |
collection | PubMed |
description | The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH). |
format | Online Article Text |
id | pubmed-3812129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38121292013-11-07 An Algorithm for Testing the Efficient Market Hypothesis Boboc, Ioana-Andreea Dinică, Mihai-Cristian PLoS One Research Article The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH). Public Library of Science 2013-10-29 /pmc/articles/PMC3812129/ /pubmed/24205148 http://dx.doi.org/10.1371/journal.pone.0078177 Text en © 2013 Boboc, Dinică http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Boboc, Ioana-Andreea Dinică, Mihai-Cristian An Algorithm for Testing the Efficient Market Hypothesis |
title | An Algorithm for Testing the Efficient Market Hypothesis |
title_full | An Algorithm for Testing the Efficient Market Hypothesis |
title_fullStr | An Algorithm for Testing the Efficient Market Hypothesis |
title_full_unstemmed | An Algorithm for Testing the Efficient Market Hypothesis |
title_short | An Algorithm for Testing the Efficient Market Hypothesis |
title_sort | algorithm for testing the efficient market hypothesis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3812129/ https://www.ncbi.nlm.nih.gov/pubmed/24205148 http://dx.doi.org/10.1371/journal.pone.0078177 |
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