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

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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Article provided by Taylor & Francis Journals in its journal Quantitative Finance.

Volume (Year): 5 (2005)
Issue (Month): 2 ()
Pages: 145-152

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Handle: RePEc:taf:quantf:v:5:y:2005:i:2:p:145-152
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  1. Robert F. Engle, 1996. "The Econometrics of Ultra-High Frequency Data," NBER Working Papers 5816, National Bureau of Economic Research, Inc.
  2. BAUWENS, Luc & GIOT, Pierre, . "Asymmetric ACD models: Introducing price information in ACD models," CORE Discussion Papers RP -1670, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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  4. Russell, Jeffrey & Engle, Robert F, 1998. "Econometric Analysis of Discrete-Valued Irregularly-Spaced Financial Transactions Data Using a New Autoregressive Conditional Multinomial Model," University of California at San Diego, Economics Working Paper Series qt00m2c5hk, Department of Economics, UC San Diego.
  5. Luc BAUWENS & Pierre GIOT, 2000. "The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks," Annales d'Economie et de Statistique, ENSAE, issue 60, pages 117-149.
  6. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
  7. Gouriéroux, Christian & Jasiak, Joanna & Le Fol, Gaëlle, 1999. "Intra-day market activity," Economics Papers from University Paris Dauphine 123456789/5478, Paris Dauphine University.
  8. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
  9. Easley, David & O'Hara, Maureen, 1987. "Price, trade size, and information in securities markets," Journal of Financial Economics, Elsevier, vol. 19(1), pages 69-90, September.
  10. Wolfgang HÄRDLE & A. TSYBAKOV & L. YANG, 1996. "Nonparametric Vector Autoregression," SFB 373 Discussion Papers 1996,61, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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