Technical Trading Rules Empirical Evidence from Future Data

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

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.