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High-frequency Cash Flow Dynamics

Author

Listed:
  • Davide Pettenuzzo

    () (Brandeis University)

  • Riccardo Sabbatucci

    () (Stockholm School of Economics)

  • Allan Timmermann

    () (University of California San Diego)

Abstract

We develop a new approach to modeling high-frequency dynamics in cash flows extracted from daily firm-level dividend announcements. Daily cash flow news follows a noisy process that is dominated by outliers so our approach decomposes this series into a persistent component, large but infrequent jumps, and temporary shocks with time-varying volatility. Empirically, we find that the persistent cash flow growth component is a better predictor of future dividend growth than alternative predictors from the literature. We also find strong evidence that news about the persistent cash flow component has a significantly positive effect on same-day stock market returns, while news about the temporary cash flow components has little effect on returns. Negative jumps in the cash flow process and higher cash flow volatility are associated with elevated stock market volatility and a higher probability of observing a jump in daily stock returns. These findings suggest that high-frequency news about the underlying cash flow growth process is an important driver not only of average stock market performance but also of the volatility and jump probability of stock prices.

Suggested Citation

  • Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2018. "High-frequency Cash Flow Dynamics," Working Papers 120, Brandeis University, Department of Economics and International Businesss School.
  • Handle: RePEc:brd:wpaper:120
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    File URL: http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP120.pdf
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    References listed on IDEAS

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    Cited by:

    1. Farmer, Leland & Schmidt, Lawrence & Timmermann, Allan G, 2018. "Pockets of Predictability," CEPR Discussion Papers 12885, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    High-frequency cash flow news; predictability of dividend growth; jump risk; dynamics in stock returns; Bayesian modeling;

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