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Durations, Volume and the Prediction of Financial Returns in Transaction Time

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  • Christian M. Hafner

    (Electrabel)

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 nonparametric methods. The empirical results for transaction data of IBM stock prices confirm the role of volume as a trend indicator and suggest that the bid-ask bounce is smaller in highly active than in less active trading periods. That is, after a sell (buy) expected returns are decreasing (increasing) with volume and increasing (decreasing) with durations.

Suggested Citation

  • Christian M. Hafner, 2000. "Durations, Volume and the Prediction of Financial Returns in Transaction Time," Econometric Society World Congress 2000 Contributed Papers 0599, Econometric Society.
    • Handle: RePEc:ecm:wc2000:0599
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    References listed on IDEAS

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    Cited by:

    1. 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.
    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. Luc, BAUWENS & Nikolaus, HAUTSCH, 2006. "Modelling Financial High Frequency Data Using Point Processes," Discussion Papers (ECON - Département des Sciences Economiques) 2006039, Université catholique de Louvain, Département des Sciences Economiques.
    4. Stanislav Anatolyev & Dmitry Shakin, 2006. "Trade intensity in the Russian stock market:dynamics, distribution and determinants," Working Papers w0070, Center for Economic and Financial Research (CEFIR).
    5. Bajzik, Josef, 2021. "Trading volume and stock returns: A meta-analysis," International Review of Financial Analysis, Elsevier, vol. 78(C).
    6. 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.
    7. Taylor, Nicholas, 2004. "Trading intensity, volatility, and arbitrage activity," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 1137-1162, May.
    8. 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.

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