Forecasting Intraday Trading Volume: A Kalman Filter Approach

16 Pages Posted: 26 Apr 2018

See all articles by Ran Chen

Ran Chen

Hong Kong University of Science & Technology (HKUST)

Yiyong Feng

Hong Kong University of Science & Technology (HKUST)

Daniel Palomar

Hong Kong University of Science and Technology (HKUST)

Date Written: August 31, 2016

Abstract

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.

Keywords: Algorithmic trading, EM, intraday trading volume, Kalman filter, Lasso, VWAP

JEL Classification: C51, C53, C61, G12

Suggested Citation

Chen, Ran and Feng, Yiyong and Palomar, Daniel, Forecasting Intraday Trading Volume: A Kalman Filter Approach (August 31, 2016). Available at SSRN: https://ssrn.com/abstract=3101695 or http://dx.doi.org/10.2139/ssrn.3101695

Ran Chen

Hong Kong University of Science & Technology (HKUST) ( email )

Clear Water Bay
Hong Kong

Yiyong Feng

Hong Kong University of Science & Technology (HKUST) ( email )

Clear Water Bay
Hong Kong

Daniel Palomar (Contact Author)

Hong Kong University of Science and Technology (HKUST) ( email )

Clear Water Bay
Kowloon, 00000
Hong Kong

HOME PAGE: http://www.danielppalomar.com/

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