Algorithmic trading for online portfolio selection under limited market liquidity
Ha, Youngmin and Zhang, Hai (2020) Algorithmic trading for online portfolio selection under limited market liquidity. European Journal of Operational Research, 286 (3). pp. 1033-1051. ISSN 0377-2217 (https://doi.org/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 (LOB), the trading algorithm optimally splits a sizeable market order into a number of consecutive market orders to minimize the overall transaction costs, including both the liquidity costs and the proportional transaction costs. The proposed trading algorithm, compatible with any OPS methods, optimizes 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.
ORCID iDs
Ha, Youngmin and Zhang, Hai ORCID: https://orcid.org/0000-0001-9319-346X;-
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Item type: Article ID code: 72230 Dates: DateEvent1 November 2020Published28 April 2020Published Online18 March 2020AcceptedSubjects: Social Sciences > Finance Department: Strathclyde Business School > Accounting and Finance Depositing user: Pure Administrator Date deposited: 01 May 2020 09:52 Last modified: 23 Sep 2024 00:49 URI: https://strathprints.strath.ac.uk/id/eprint/72230