Technical Trading Rules Empirical Evidence from Future Data

Business & Finance, Finance & Investing, Banks & Banking
Cover of the book Technical Trading Rules Empirical Evidence from Future Data by Philipp Jan Siegert, GRIN Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Philipp Jan Siegert ISBN: 9783638432443
Publisher: GRIN Publishing Publication: October 27, 2005
Imprint: GRIN Publishing Language: English
Author: Philipp Jan Siegert
ISBN: 9783638432443
Publisher: GRIN Publishing
Publication: October 27, 2005
Imprint: GRIN Publishing
Language: English

Master's Thesis from the year 2005 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: A, Sophia Antipolis Campus (France); SKEMA Business School (Global Finance Chair), 27 entries in the bibliography, language: English, abstract: Most banks and the recently upcoming hedge fund industry rely to a different extent on technical trading rules and technical analysis. The fact that these technical trading rules yield superior returns in practice raises several questions that will be examined in the thesis. First, one of the most crucial questions is in which assets technical trading rules perform extraordinarily well. This analysis is based on a risk-return approach with an assessment of the negative standard deviation of each asset as a risk indicator. Second, the statistical significance of technical trading is examined by using a simulation method known as bootstrap. Third, null models are simulated to answer the question to what extent autoregressive models and GARCH models are able to capture the dependencies in the time series. Finally, a rule optimizer is used to assess if any rule parameters yield superior returns over a wide range of assets. We find that under a risk-return perspective trading rules look very attractive as most rules are able to significantly reduce the negative standard deviation compared to a buy-and-hold strategy. However, not all rules are able to outperform a simple buy-and-hold strategy in terms of absolute return. Statistical significance is generally weak and only some rules can be qualified as highly statistically significant. We do not find much evidence that autoregressive and GARCH null models perform well in capturing the dependencies that lead to superior returns of technical trading rules. With respect to trading rule parameters we find that shorter rules generally perform better when trading costs are not considered and that currencies benefited from a larger standard deviation trading band.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Master's Thesis from the year 2005 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: A, Sophia Antipolis Campus (France); SKEMA Business School (Global Finance Chair), 27 entries in the bibliography, language: English, abstract: Most banks and the recently upcoming hedge fund industry rely to a different extent on technical trading rules and technical analysis. The fact that these technical trading rules yield superior returns in practice raises several questions that will be examined in the thesis. First, one of the most crucial questions is in which assets technical trading rules perform extraordinarily well. This analysis is based on a risk-return approach with an assessment of the negative standard deviation of each asset as a risk indicator. Second, the statistical significance of technical trading is examined by using a simulation method known as bootstrap. Third, null models are simulated to answer the question to what extent autoregressive models and GARCH models are able to capture the dependencies in the time series. Finally, a rule optimizer is used to assess if any rule parameters yield superior returns over a wide range of assets. We find that under a risk-return perspective trading rules look very attractive as most rules are able to significantly reduce the negative standard deviation compared to a buy-and-hold strategy. However, not all rules are able to outperform a simple buy-and-hold strategy in terms of absolute return. Statistical significance is generally weak and only some rules can be qualified as highly statistically significant. We do not find much evidence that autoregressive and GARCH null models perform well in capturing the dependencies that lead to superior returns of technical trading rules. With respect to trading rule parameters we find that shorter rules generally perform better when trading costs are not considered and that currencies benefited from a larger standard deviation trading band.

More books from GRIN Publishing

Cover of the book Biological determinism and the development of tragic characters in 'Jude the Obscure' by Philipp Jan Siegert
Cover of the book Neuromarketing in Sports by Philipp Jan Siegert
Cover of the book An international marketing strategy for Black Sheep Brewery in Australia by Philipp Jan Siegert
Cover of the book The role of different corporate culters in case of a merger by Philipp Jan Siegert
Cover of the book Historic and Ethnic Development of Ethiopia by Philipp Jan Siegert
Cover of the book Thomas Jefferson and Slavery - Was He Really an Opponent of the Institution? by Philipp Jan Siegert
Cover of the book Valid Fundamental Arguments by Philipp Jan Siegert
Cover of the book An Analysis of eBay's Culture by Philipp Jan Siegert
Cover of the book Americanization - The US strikes back? by Philipp Jan Siegert
Cover of the book China in the 1970s - From Cultural Revolution to Emerging World Economy by Philipp Jan Siegert
Cover of the book Blurring the Boundaries in Bobby Ann Mason's 'In Country' (1985) by Philipp Jan Siegert
Cover of the book Materials Handling And Packaging Field Research by Philipp Jan Siegert
Cover of the book Business Analysis of web.de AG by Philipp Jan Siegert
Cover of the book Peter Ackroyd and Metafiction. A Brief Introduction by Philipp Jan Siegert
Cover of the book Knowledge Management by Philipp Jan Siegert
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy