Algorithmic trading for online portfolio selection under limited market liquidity

Youngmin Ha and Hai Zhang

European Journal of Operational Research, 2020, vol. 286, issue 3, 1033-1051

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.

Keywords: Investment analysis; Algorithmic trading; Online portfolio selection; Market impact cost; Limit order book (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:286:y:2020:i:3:p:1033-1051

DOI: 10.1016/j.ejor.2020.03.050

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