This thesis will examine how the efficient market hypothesis (Fama, 1965), the random-walk hypothesis, and the Martingale model relate to the profitability of technical analysis. Additionally, relevant research on profitability up to 2023 will be presented and summarized, highlighting major findings in the context of profitability following technical trading rules. The contribution to current research is made by testing the profitability of popular technical analysis strategies and discussing the underlying reasons for any observed profitability. Furthermore, a separate analysis will be conducted, applying four technical analysis strategies to the S&P 500 time series from 2005 to 2023.
This thesis shows that none of the, in selected literature deemed profitable, trading strategies applied by Marshall (2017), Kuang et al. (2014), and Brock et al. (1992) are outperforming a S&P 500 buy-and-hold strategy between 2005 and 2023. Robustness checks such as break-even transaction cost analysis show that only two moving average variation strategies are profitable at the 20 basis points transaction cost level. Neglecting this influence of transaction costs, only one of these strategies’ returns are greater than those of a buy-and-hold strategy, though still of unsignificant nature. Nevertheless, technical analysis strategies exhibit superior performance in terms of Sharpe ratio compared to the buy-and-hold approach. This is primarily due to this thesis’ assumption of investing in risk-free treasury bills when not allocated to the S&P 500 index.
Table of Contents
1 Introduction
2 Background and Theory
2.1 Classification of Different Technical Analysis Strategies
2.2 Efficient Market Hypothesis
2.3 Martingale Model
2.4 Random Walk Models
2.5 Augmented Dickey-Fuller Test
2.6 Additional Models
3 Literature Review
4 Methodology
4.1 Data
4.2 Technical Analysis Strategies
4.2.1 Moving Average Crossover
4.2.2 Bollinger Bands
4.2.3 Relative Strength Index
4.2.4 Moving Average Convergence Divergence (MACD)
4.3 Descriptive Statistics
4.3.1 Underlying and Buy-and-Hold Strategy
4.3.2 Strategies
5 Results
6 Conclusion
Appendix A R Methodology, Return Analysis, and Trading Signals Overview
A.1 R Analysis Excerpt: MA5-150 Profit and Sharpe Ratio Calculation
A.2 Yearly Mean Daily Returns of Technical Analysis Strategies
A.3 Volume Analysis across Technical Analysis Strategies
A. 4 Entry and Exit Points following Technical Analysis Strategies
Objectives & Research Themes
This thesis investigates the profitability of various technical trading strategies when applied to the S&P 500 index between 2005 and 2023, assessing whether these rules can outperform a traditional buy-and-hold approach in a modern, more efficient market environment.
- Evaluation of four distinct technical analysis strategies, including both mean-reversion and trend-following approaches.
- Statistical assessment of S&P 500 market efficiency using ADF tests from 2005 to 2023.
- Robustness checks incorporating transaction costs to determine practical strategy viability.
- Analysis of risk exposure during major market crashes to test potential market-timing benefits.
- Comparison of Sharpe ratios and annual returns between active technical trading and passive buy-and-hold investments.
Excerpt from the Thesis
1 Introduction
Although previous research (see de Souza et al., 2018; Gerritsen, 2016; Marshall et al., 2009) on profitability of technical trading systems is divided and inconclusive, practitioners still use such rules (Marshall et al., 2009). Specifically, significant increases in trading volume up to 30% can be directly linked to technical trading heuristics (Etheber et al., 2014). Jiang et al. (2022) argue that the relevance of technical analysis trading in the 21st century may be explained by the use of machine learning which results in better price predictions and trend recognition abilities.
Nevertheless, there has been an ongoing dispute between investment professionals and practitioners about the value of technical analysis – in his book “A Random-walk down Wall Street”, Malkiel (1996, p.103) states harshly:
“Obviously, I am biased against the chartist. This is not only a personal predilection, but a professional one as well. Technical analysis is anathema to the academic world. We love to pick on it. Our bullying tactics are prompted by two considerations: (1) the method is patently false; and (2) it's easy to pick on. And while it may seem a bit unfair to pick on such a sorry target, just remember: it is your money we are trying to save.”
With that in mind, it is surprising that previous research has been ambiguous about profitability findings, since the findings should be pointing clearly against profitability according to finance theory (Fama, 1965). Moreover, the topic’s relevance is given since technical analysis could, if proof of profitability was found, provide value to investors, traders, and financial institutions.
Summary of Chapters
1 Introduction: This chapter highlights the ongoing debate surrounding technical analysis and outlines the thesis goal to evaluate the profitability of trading strategies using recent S&P 500 data.
2 Background and Theory: This section details core financial theories such as the Efficient Market Hypothesis, random-walk models, and disequilibrium dynamics that serve as the foundation for the analysis.
3 Literature Review: The chapter summarizes previous academic findings on the profitability of technical trading systems and addresses common methodological challenges like data snooping.
4 Methodology: This chapter explains the dataset selection, the four technical strategies implemented (Moving Average Crossover, Bollinger Bands, RSI, MACD), and the metrics used for performance evaluation.
5 Results: This chapter presents the quantitative findings, evaluating the profitability of the selected strategies and their risk exposure compared to a buy-and-hold strategy.
6 Conclusion: The final chapter summarizes the thesis, confirming that while technical strategies offer superior Sharpe ratios in some cases, they generally do not outperform a buy-and-hold approach after transaction costs.
Keywords
Technical Analysis, S&P 500, Efficient Market Hypothesis, Moving Average Crossover, Bollinger Bands, Relative Strength Index, MACD, Profitability Assessment, Risk-Adjusted Returns, Transaction Costs, Market Crises, Data Snooping, Sharpe Ratio, Quantitative Finance, Trading Heuristics
Frequently Asked Questions
What is the core focus of this research?
The research evaluates the profitability of major technical analysis strategies applied to the S&P 500 index over the period from 2005 to 2023.
Which technical analysis strategies are examined?
The paper examines four main strategies: Moving Average Crossover, Bollinger Bands (BB), the Relative Strength Index (RSI), and the Moving Average Convergence Divergence (MACD).
What is the primary goal of this study?
The goal is to determine if these technical strategies can consistently outperform a simple buy-and-hold strategy, accounting for transaction costs in a modern market environment.
Which scientific methodology is utilized?
The study uses quantitative analysis involving daily market data, applying statistical performance metrics like the Sharpe ratio and conducting robustness checks for transaction costs and crash risk.
What is covered in the primary body of the thesis?
The main body covers a review of theoretical market models, an analysis of prior academic literature, detailed technical strategy definitions, and an empirical evaluation of trading performance.
Which keywords best characterize this work?
Key terms include Technical Analysis, S&P 500, Market Efficiency, Sharpe Ratio, and Quantitative Strategy Testing.
How does this study address the impact of market crises?
The thesis evaluates crash risk susceptibility by analyzing how the chosen strategies behaved during 10 major S&P 500 daily market declines, specifically looking at how they avoid or accumulate exposure.
What is the conclusion regarding market efficiency?
The research concludes that the S&P 500 likely exhibits at least weak-form efficiency during the observed period, as the strategies fail to consistently yield excess returns after considering transaction costs.
- Arbeit zitieren
- David Neufang (Autor:in), 2024, Profitability Evaluation of Major Technical Analysis Trading Strategies Applied to the S&P 500 from 2005 to 2023, München, GRIN Verlag, https://www.grin.com/document/1500849
