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Durations, volume and the prediction of financial returns in transaction time

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  • HAFNER, Christian H.

Abstract

Traditional microstructural theories of asset pricing emphasize the role of volume as a trend indicator. With the availability of large transaction data sets, one has started recently to incorporate more information of the trades, such as the time between trades, to describe the multivariate dynamics of transactions. Without knowing a priori the relation between the observed components of a trade—price, duration between trades, and volume—one may follow the principle of 'letting the data speak for themselves'. The goal of this paper is to evaluate the informational content of both volume and durations to predict transaction returns using explorative non-parametric methods. The empirical results for transaction data of IBM stock prices confirm the role of volume as a trend indicator. After a sell (buy) expected returns are decreasing (increasing) with volume and increasing (decreasing) with durations. A.forecasting exercise shows that the superiority of the non-parametric model over simple parameterizations carries over to out-of-sample prediction.
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Suggested Citation

  • HAFNER, Christian H., 2005. "Durations, volume and the prediction of financial returns in transaction time," LIDAM Reprints CORE 1784, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1784
    DOI: 10.1080/14697680500040033
    Note: In : Quantitative Finance, 5(2), 145-152, 2005.
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    Cited by:

    1. Taylor, Nicholas, 2004. "Trading intensity, volatility, and arbitrage activity," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 1137-1162, May.
    2. Zhang, Yaohua & Zou, Jian & Ravishanker, Nalini & Thavaneswaran, Aerambamoorthy, 2019. "Modeling financial durations using penalized estimating functions," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 145-158.
    3. Stanislav Anatolyev & Dmitry Shakin, 2007. "Trade intensity in the Russian stock market: dynamics, distribution and determinants," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 87-104.
    4. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    5. Jiang, Zhi-Qiang & Chen, Wei & Zhou, Wei-Xing, 2009. "Detrended fluctuation analysis of intertrade durations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(4), pages 433-440.
    6. Bajzik, Josef, 2021. "Trading volume and stock returns: A meta-analysis," International Review of Financial Analysis, Elsevier, vol. 78(C).
    7. Luc Bauwens & Nikolaus Hautsch, 2009. "Modelling Financial High Frequency Data Using Point Processes," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 41, pages 953-979, Springer.
    8. Christian M. Hafner, 2012. "Cross-correlating wavelet coefficients with applications to high-frequency financial time series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1363-1379, December.

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