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Technical analysis, spread trading, and data snooping control
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DOI：10.1016/j.ijforecast.2021.10.002.png)
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Abstract
En 中文
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. Crown Copyright (c) 2021 Published by Elsevier B.V. on behalf of International Institute of Forecasters. All rights reserved.
Keywords:
Technical trading rules
Spread trading predictability
False discovery rate
Bootstrap test
Portfolio performance
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