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Technical Analysis, Spread Trading and Data Snooping Control

Last Updated on 10 February, 2024 by Rejaul Karim

The profound investigation by Ioannis Psaradellis, Jason Laws, Athanasios A. Pantelous, and Georgios Sermpinis uncovers a compelling discourse on “Technical Analysis, Spread Trading, and Data Snooping Control,” marking a pivotal contribution to the realm of forecasting.

This impactful study delves into the evaluation of the performance of frequently traded spreads, leveraging a vast array of technical trading rules (TTRs) across diverse subperiods. For the first time, the paper implements an in-depth excessive out-of-sample analysis, shedding light on the significant predictability of commodity spreads compared to equity and currency spreads.

Notably, the out-of-sample performance of portfolios showcases remarkable results, surpassing transaction cost estimates and yielding a notable Sharpe ratio in 2016. The study’s overarching examination refutes prior assertions of a uniformly downward trend in the selection of predictive TTRs, charting a new trajectory in forecasting understanding.

This rigorous exploration navigates the intricate intersections of technical trading rules, spread trading predictability, and false discovery rate, presenting crucial insights for portfolio performance and efficacious forecasting methodologies.

Abstract Of Paper

This paper utilizes a large universe of 18,410 technical trading rules (TTRs) and adopts a technique that controls for false discoveries to evaluate the performance of frequently traded spreads using daily data over 1990–2016. For the first time, the paper applies an excessive out-of-sample analysis in different subperiods across all TTRs examined. For commodity spreads, the evidence of significant predictability appears much stronger compared to equity and currency spreads. Out-of-sample performance of portfolios of significant rules typically exceeds transaction cost estimates and generates a Sharpe ratio of 3.67 in 2016. In general, we reject previous studies’ evidence of a uniformly monotonic downward trend in the selection of predictive TTRs over 1990–2016.

Original paper – Download PDF

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Author

Ioannis Psaradellis
University of St Andrews School of Economics and Finance

Jason Laws
University of Liverpool – Accounting and Finance Division

Athanasios A. Pantelous
Monash University – Department of Econometrics & Business Statistics

Georgios Sermpinis
University of Glasgow

Conclusion

Ioannis Psaradellis, Jason Laws, Athanasios A. Pantelous, and Georgios Sermpinis culminate their comprehensive study on “Technical Analysis, Spread Trading, and Data Snooping Control” with groundbreaking findings that unravel a paradigm shift in forecasting dynamics.

The meticulous evaluation of 18,410 technical trading rules (TTRs) employing a robust false discovery control technique has brought a fresh perspective to the field, particularly in delineating the predictability of commodity spreads vis-à-vis equity and currency spreads.

Significantly, the out-of-sample performance of portfolios has defied expectations, surpassing transaction cost estimates and yielding a noteworthy Sharpe ratio in 2016, thereby underscoring the potent efficacy of TTRs in portfolio performance.

The study’s compelling rejection of a uniform downward trend in the selection of predictive TTRs over 1990–2016 instigates a pivotal reevaluation of forecasting dynamics.

This conclusion heralds a new era of understanding in the intricate intersections of technical trading rules, spread trading predictability, and false discovery rate, offering a transformatively insightful outlook for efficacious forecasting strategies in financial markets.

Related Reading:

Evaluating Commodity Exposure Opportunities

Dynamic Regime Strategy for Stress Testing and Optimizing Institutional Investor Portfolios

FAQ

Q1: What is the primary focus of the study on “Technical Analysis, Spread Trading, and Data Snooping Control,” and what methodology does it employ?

A1: The primary focus of the study is to evaluate the performance of frequently traded spreads using technical trading rules (TTRs). The study employs a vast universe of 18,410 TTRs and utilizes a technique that controls for false discoveries. It conducts an in-depth excessive out-of-sample analysis across various subperiods to assess the predictability of commodity spreads compared to equity and currency spreads.

Q2: How does the study contribute to the understanding of predictability in commodity spreads, and what sets it apart from previous research?

A2: The study contributes by showcasing the significant predictability of commodity spreads compared to equity and currency spreads. It employs an extensive excessive out-of-sample analysis, providing a nuanced examination of the performance of TTRs in different subperiods. The research challenges previous assertions of a uniformly downward trend in the selection of predictive TTRs over the period 1990–2016.

Q3: What are the notable findings regarding the out-of-sample performance of portfolios based on TTRs, and how do they impact the understanding of portfolio performance?

A3: The out-of-sample performance of portfolios based on TTRs is remarkable, surpassing transaction cost estimates and yielding a notable Sharpe ratio in 2016. This finding suggests the potent efficacy of TTRs in enhancing portfolio performance. It challenges prior notions and offers a fresh perspective on the ability of TTRs to generate positive returns.

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