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.
Table of Contents
I. INTRODUCTION
The emergence of technical trading
The academic community’s attitude towards technical trading
The efficient market hypothesis
Data snooping and pre-testing bias
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
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
Research Objectives and Focus
The thesis aims to empirically evaluate the performance and statistical significance of simple technical trading rules across a diverse set of future contracts. By employing a risk-return framework and robust bootstrap simulation methods, it seeks to determine whether these rules can reliably outperform a buy-and-hold strategy and whether standard econometric null models can explain any observed technical trading performance.
- Empirical assessment of VMA, FMA, and TRB trading strategies.
- Evaluation of rule performance using a risk-return approach focusing on negative standard deviation.
- Statistical significance testing via bootstrap simulations against Random Walk and GARCH null models.
- Investigation into the profitability of trading rules in the context of transaction costs and future contracts.
- Analysis of optimal parameter configurations for diverse asset classes including commodities, interest rates, and currencies.
Excerpt from the Book
The emergence of technical trading
The past years at international stock markets have been characterized by increased activity of so called hedge funds and a significant increase of bank’s trading floor activity. Compared to past decades, banks are increasingly hiring quantitative analysts, often educated on a PhD level in sciences such as mathematics, physics, engineering or economics. What determines the need for these “Quants” and why have they only been hired in recent years in such a large number by banks and hedge funds?
One rationale for the increase in quantitative staff is the emergence of complex derivative products such as exotic options or synthetic swaps in today’s financial markets. Pricing these products involves complex mathematical equations and forecasting models that can only be handled by specialised staff. A second reason can be found in the emergence of technical trading systems that aim to outperform other trading strategies by solely relying on quantitative investment criteria. In essence these systems do not assess any market information other than past quantitative characteristics such as prices or volatility.
Although technical trading grew significantly in past years and has become a big source of income for banks and hedge funds, the concept is far away from being something new. In fact, technical trading is often considered to be the first form of trading at stock markets, applied long before financial disclosure information enabled market participants to trade on fundamentals rather than on past prices.
Summary of Chapters
I. Introduction: This chapter introduces the rise of quantitative finance and technical trading in modern markets, discussing the academic skepticism regarding market efficiency and data snooping.
II. Problem Statement: This section frames the research question concerning the statistical significance of technical trading rules and the potential for econometric models to explain their success.
III. Literature Review: This chapter reviews historical and contemporary empirical research on technical trading, highlighting the debate between profitability and transaction costs.
IV. Methodology: This chapter details the specific trading rules used (VMA, FMA, TRB) and the bootstrap simulation process implemented to test statistical significance against various null models.
V. Empirical Results: This chapter presents the data analysis, showing returns and risk profiles for various futures, as well as the results of the dependency tests using AR(1) and GARCH models.
VI. Conclusion: This final chapter synthesizes the research findings, confirming that while technical rules can reduce risk, their absolute returns are often not statistically significant or replicable through simple econometric null models.
Keywords
Technical Trading Rules, Future Contracts, Bootstrap Simulation, Efficient Market Hypothesis, Data Snooping, Quantitative Finance, Risk-Return Approach, Moving Average, Trading Range Break, GARCH Model, Econometrics, Financial Markets, Statistical Significance, Transaction Costs, Asset Management.
Frequently Asked Questions
What is the primary scope of this research?
The research focuses on the empirical performance of simple technical trading rules applied to a variety of future contracts, including commodities, interest rates, and currency pairs, over the period from 1990 to 2005.
What are the main thematic fields covered in the work?
The work covers market efficiency theories, the mechanics of technical analysis (VMA, FMA, TRB), statistical validation through simulation, and risk management through the assessment of negative standard deviations.
What is the central research question?
The study asks whether simple technical trading rules yield returns that are statistically significant and whether their success can be explained by underlying dependencies captured by autoregressive and GARCH models.
Which scientific methods does the author employ?
The author uses a risk-return framework, "brute force" parameter optimization, and a bootstrap methodology to simulate artificial time series for testing the statistical significance of trading strategies.
What topics are discussed in the main part of the thesis?
The main part covers the historical context of technical trading, the formulation of specific trading rules, an extensive literature review, and the detailed presentation of empirical test results for different futures.
Which keywords best characterize this work?
Key terms include Technical Trading Rules, Future Contracts, Bootstrap Simulation, Efficient Market Hypothesis, Data Snooping, and Risk-Return Approach.
Why did the author specifically choose future contracts for this study?
Futures were chosen due to their leverage, which allows for smaller capital usage, their implicit inclusion of dividends and interest rate differentials, and generally lower transaction costs compared to physical asset trading.
Does the author find that simple technical rules generally outperform buy-and-hold strategies?
The study finds that while trading rules are often effective at reducing risk—specifically the negative standard deviation—they do not always outperform a buy-and-hold strategy in terms of absolute mean returns.
What does the author conclude about the GARCH null model?
The author concludes that the AR(1)-GARCH(1,1) model has limited explanatory power regarding the success of technical trading, suggesting that it fails to capture all relevant dependencies within the tested time series.
- Quote paper
- Philipp Jan Siegert (Author), 2005, Technical Trading Rules Empirical Evidence from Future Data, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/45926
