Algorithmic trading for online portfolio selection under limited market liquidity

Youngmin Ha; Hai Zhang

doi:10.1016/j.ejor.2020.03.050

Algorithmic trading for online portfolio selection under limited market liquidity

Youngmin Ha, Hai Zhang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

Abstract

We propose an optimal intraday trading algorithm to reduce overall transaction costs by absorbing price shocks when an online portfolio selection (OPS) method rebalances its portfolio. Having considered the real-time data of limit order books (LOBs), the trading algorithm optimally splits a sizeable market order into a number of consecutive market orders to minimise the overall transaction costs, including both the liquidity costs and the proportional transaction costs. The proposed trading algorithm, compatible with any OPS methods, optimises the number of intraday trades and finds an optimal intraday trading path. Backtesting results from the historical LOB data of NASDAQ-traded stocks show that the proposed trading algorithm significantly reduces the overall transaction costs when market liquidity is limited.

Original language	English
Pages (from-to)	1033-1051
Number of pages	19
Journal	European Journal of Operational Research
Volume	286
Issue number	3
DOIs	https://doi.org/10.1016/j.ejor.2020.03.050
Publication status	Published - 1 Nov 2020
Externally published	Yes

Keywords

Algorithmic trading
Investment analysis
Limit order book
Market impact cost
Online portfolio selection

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10.1016/j.ejor.2020.03.050

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abstract = "We propose an optimal intraday trading algorithm to reduce overall transaction costs by absorbing price shocks when an online portfolio selection (OPS) method rebalances its portfolio. Having considered the real-time data of limit order books (LOBs), the trading algorithm optimally splits a sizeable market order into a number of consecutive market orders to minimise the overall transaction costs, including both the liquidity costs and the proportional transaction costs. The proposed trading algorithm, compatible with any OPS methods, optimises the number of intraday trades and finds an optimal intraday trading path. Backtesting results from the historical LOB data of NASDAQ-traded stocks show that the proposed trading algorithm significantly reduces the overall transaction costs when market liquidity is limited.",

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KW - Investment analysis

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KW - Market impact cost

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Algorithmic trading for online portfolio selection under limited market liquidity

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Keywords

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