Performance of technical trading rules: evidence from the crude oil market

Ioannis Psaradellis, Jason Laws, Athanasios Pantelous, Georgios Sermpinis

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)


    This study investigates the debatable success of technical trading rules, through the years, on the trending energy market of crude oil. In particular, the large universe of 7846 trading rules proposed by Sullivan, Timmermann, and White (1999. “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap.” The Journal of Finance 54 (5): 1647–1691. doi:10.1111/0022-1082.00163), divided into five families (filter rules, moving averages, support and resistance rules, channel breakouts, and on-balance volume averages), is applied to the daily prices of West Texas Intermediate (WTI) light, sweet crude oil futures as well as the United States Oil (USO) fund, from 2006 onwards. We employ the k-familywise error rate (k-FWER) and false discovery rate (FDR) techniques proposed by Romano, J. P., and M. Wolf. (2007. “Control of Generalized Error Rates in Multiple Testing.” The Annals of Statistics 35 (4): 1378–1408. doi:10.1214/009053606000001622) and Bajgrowicz, P., and O. Scaillet. (2012. “Technical Trading Revisited: False Discoveries, Persistence Tests, and Transaction Costs.” Journal of Financial Economics 106 (3): 473–491. doi:10.1016/j.jfineco.2012.06.001) respectively, accounting for data snooping in order to identify significantly profitable trading strategies. Our findings explain that there is no persistent nature in rules performance, contrary to the in-sample outstanding results, although tiny profits can be achieved in some periods. Overall, our results seem to be in favor of interim market inefficiencies.
    Original languageEnglish
    Pages (from-to)1793-1815
    Number of pages23
    JournalEuropean Journal of Finance
    Issue number17
    Early online date27 Nov 2018
    Publication statusPublished - 22 Nov 2019


    • Crude oil
    • Technical trading
    • Data snooping
    • Transaction costs
    • Persistence
    • Market efficiency


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