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Applying Hurst Exponent in pair trading strategies on Nasdaq 100 index

Author

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  • Bui, Quynh
  • Ślepaczuk, Robert

Abstract

This research aims to seek an alternative approach to stock selection for algorithmic investment strategy. We try to build an effective pair trading strategy based on 103 stocks listed in the NASDAQ 100 index. The dataset has a daily frequency and covers the period from 01/01/2000 to 31/12/2018 , and to 01/07/2021 as an additional out-of-time dataset. In this study, Generalized Hurst Exponent, Correlation, and Cointegration methods are employed to detect the mean-reverting pattern in the time series of a linear combination of each pair of stock. The result shows that the Hurst method cannot outperform the benchmark, which implies that the market is efficient. These results are quite sensitive to varying number of pairs traded and rebalancing period but they are less sensitive to financial leverage degree. Moreover, the Hurst method is better than the cointegration method but is not superior as compared to the correlation method.

Suggested Citation

  • Bui, Quynh & Ślepaczuk, Robert, 2022. "Applying Hurst Exponent in pair trading strategies on Nasdaq 100 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    • Handle: RePEc:eee:phsmap:v:592:y:2022:i:c:s037843712100964x
    DOI: 10.1016/j.physa.2021.126784
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    Citations

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    Cited by:

    1. Baiquan Ma & Robert Ślepaczuk, 2022. "The profitability of pairs trading strategies on Hong-Kong stock market: distance, cointegration, and correlation methods," Working Papers 2022-02, Faculty of Economic Sciences, University of Warsaw.
    2. Day, Min-Yuh & Ni, Yensen, 2023. "Be greedy when others are fearful: Evidence from a two-decade assessment of the NDX 100 and S&P 500 indexes," International Review of Financial Analysis, Elsevier, vol. 90(C).
    3. Mar Grande & Florentino Borondo & Juan Carlos Losada & Javier Borondo, 2024. "Anti-Persistent Values of the Hurst Exponent Anticipate Mean Reversion in Pairs Trading: The Cryptocurrencies Market as a Case Study," Mathematics, MDPI, vol. 12(18), pages 1-14, September.
    4. Rayadurgam, Vikram Chandramouli & Mangalagiri, Jayasree, 2023. "Does inclusion of GARCH variance in deep learning models improve financial contagion prediction?," Finance Research Letters, Elsevier, vol. 54(C).
    5. Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023. "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers 2023-20, Faculty of Economic Sciences, University of Warsaw.

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    More about this item

    Keywords

    Generalized Hurst Exponent; Algorithmic trading strategies; Portfolio choice; Mean-reversion strategy; Pair trading; Correlation & cointegration trading; Efficient market hypothesis;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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