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.
Inhaltsverzeichnis (Table of Contents)
- PREFACE
- EXECUTIVE SUMMARY
- I. INTRODUCTION
- The emergence of technical trading
- The academic community's attitude towards technical trading
- The efficient market hypothesis
- II. PROBLEM STATEMENT
- Returns and the significance of technical trading rules
- Can econometric models explain the patterns of technical trading?
- Is there an optimal simple trading rule?
- III. LITERATURE REVIEW
- The profitability of technical trading rules – Early results
- Confirmatory Research about Technical Trading Rules
- Brock, Lakonishok and LeBaron (1992)
- Levich and Thomas (1991)
- Ratner and Leal (1999)
- Evidence for Declining Returns of Trading Rules in Recent Sub Periods
- Sullivan, Timmermann and White (1999)
- LeBaron (2002)
- The issue of trading costs
- IV. METHODOLOGY
- Simple Technical Trading Rules
- Variable length moving average (VMA) rules
- Fixed length moving average (FMA) rules
- Trading Range Break (TRB) rules
- The statistical significance of technical trading rules
- Random Walk Null Model
- AR(1) Null Model
- AR(1)-GARCH(1,1) Null Model
- Simple Technical Trading Rules
- V. EMPIRICAL RESULTS
- Why future contracts?
- Summary statistics
- Risk-Return Results
- Returns of VMA strategies
- Returns of FMA strategies
- Returns of TRB strategies
- Negative standard deviation assessment
- Time Series Dependencies
- Approach
- Results
- Are there optimal trading rule parameters?
- VI. CONCLUSION
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This thesis investigates the effectiveness of technical trading rules in generating superior returns in financial markets. The research analyzes the performance of various technical trading rules across different assets, focusing on risk-return relationships, statistical significance, and the ability of econometric models to capture time series dependencies.
- The profitability of technical trading rules in different assets.
- The statistical significance of technical trading rules.
- The role of econometric models in capturing the patterns of technical trading.
- The identification of optimal trading rule parameters.
- The impact of trading costs on the profitability of technical trading rules.
Zusammenfassung der Kapitel (Chapter Summaries)
The introduction provides an overview of the emergence and historical development of technical trading, exploring the perspectives of both practitioners and academics. It also touches upon the efficient market hypothesis and its implications for technical analysis.
The problem statement chapter outlines the specific research questions addressed in the thesis, focusing on the returns generated by technical trading rules, the explanatory power of econometric models, and the existence of an optimal trading rule.
The literature review section presents a comprehensive analysis of previous studies on the profitability of technical trading rules, examining both early and more recent findings. It discusses the work of prominent researchers such as Brock, Lakonishok and LeBaron (1992), Levich and Thomas (1991), Ratner and Leal (1999), Sullivan, Timmermann and White (1999), and LeBaron (2002). This chapter also explores the impact of trading costs on the performance of technical trading rules.
The methodology chapter details the technical trading rules employed in the empirical analysis, including variable length moving average (VMA), fixed length moving average (FMA), and trading range break (TRB) rules. It further outlines the statistical methods used to assess the significance of these rules, specifically the random walk, AR(1), and AR(1)-GARCH(1,1) null models.
Schlüsselwörter (Keywords)
Technical trading rules, risk-return analysis, statistical significance, econometric models, autoregressive models, GARCH models, trading costs, future contracts, empirical evidence, financial markets, efficient market hypothesis, risk management.
- Citar trabajo
- Philipp Jan Siegert (Autor), 2005, Technical Trading Rules Empirical Evidence from Future Data, Múnich, GRIN Verlag, https://www.grin.com/document/45926
