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Direct multiperiod forecasting for algorithmic trading

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  • Hiroyuki Kawakatsu

Abstract

This paper examines the performance of iterated and direct forecasts for the number of shares traded in high†frequency intraday data. Constructing direct forecasts in the context of formulating volume weighted average price trading strategies requires the generation of a sequence of multistep†ahead forecasts. I discuss nonlinear transformations to ensure nonnegative forecasts and lag length selection for generating a sequence of direct forecasts. In contrast to the literature based on low†frequency macroeconomic data, I find that direct multiperiod forecasts can outperform iterated forecasts when the conditioning information set is dynamically updated in real time.

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  • Hiroyuki Kawakatsu, 2018. "Direct multiperiod forecasting for algorithmic trading," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 83-101, January.
    • Handle: RePEc:wly:jforec:v:37:y:2018:i:1:p:83-101
    DOI: 10.1002/for.2488
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    Cited by:

    1. Nino Antulov-Fantulin & Tian Guo & Fabrizio Lillo, 2021. "Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 905-940, December.

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