Forecasting Intraday Trading Volume: A Kalman Filter Approach

An accurate forecast of intraday volume is a key aspect of algorithmic trading. This manuscript proposes a state-space model to forecast intraday trading volume via the Kalman filter and derives closed-form expectation-maximization (EM) solutions for model calibration. The model is extended to handle outliers in real-time market data by applying a sparse regularization technique. Empirical studies using thirty securities on eight exchanges show that the proposed model substantially outperforms the rolling means (RM) and the state-of-the-art Component Multiplicative Error Model (CMEM) by 64% and 29%, respectively, in volume prediction and by 15% and 9%, respectively, in Volume Weighted Average Price (VWAP) trading.

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