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Time-dependent scaling patterns in high frequency financial data

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  • Nava, Noemi
  • Di Matteo, Tiziana
  • Aste, Tomaso

Abstract

We measure the influence of different time-scales on the intraday dynamics of financial markets. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures of complexity: 1) an amplitude scaling exponent and 2) an entropy-like measure. We apply these measures to intraday, 30-second sampled prices of various stock market indices. Our results reveal intraday trends where different time-horizons contribute with variable relative amplitudes over the course of the trading day. Our findings indicate that the time series we analysed have a non-stationary multifractal nature with predominantly persistent behaviour at the middle of the trading session and anti-persistent behaviour at the opening and at the closing of the session. We demonstrate that these patterns are statistically significant, robust, reproducible and characteristic of each stock market. We argue that any modelling, analytics or trading strategy must take into account these non-stationary intraday scaling patterns.

Suggested Citation

  • Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2016. "Time-dependent scaling patterns in high frequency financial data," LSE Research Online Documents on Economics 68645, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:68645
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    File URL: http://eprints.lse.ac.uk/68645/
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    References listed on IDEAS

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    1. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    Cited by:

    1. Berend Jelmer Dirk Gort & Xiao-Yang Liu & Xinghang Sun & Jiechao Gao & Shuaiyu Chen & Christina Dan Wang, 2022. "Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting," Papers 2209.05559, arXiv.org, revised Jan 2023.
    2. Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2018. "Financial time series forecasting using empirical mode decomposition and support vector regression," LSE Research Online Documents on Economics 91028, London School of Economics and Political Science, LSE Library.
    3. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2018. "Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression," Risks, MDPI, vol. 6(1), pages 1-21, February.
    4. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    5. Arias-Calluari, Karina & Najafi, Morteza. N. & Harré, Michael S. & Tang, Yaoyue & Alonso-Marroquin, Fernando, 2022. "Testing stationarity of the detrended price return in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    6. Karina Arias-Calluari & Morteza. N. Najafi & Michael S. Harr'e & Fernando Alonso-Marroquin, 2019. "Stationarity of the detrended price return in stock markets," Papers 1910.01034, arXiv.org, revised Aug 2020.

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    JEL classification:

    • F3 - International Economics - - International Finance
    • G3 - Financial Economics - - Corporate Finance and Governance

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