Algorithmic trading for online portfolio selection under limited market liquidity

Youngmin Ha, Hai Zhang

Research output: Contribution to journalArticle

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.
Original languageEnglish
Number of pages33
JournalEuropean Journal of Operational Research
Early online date28 Apr 2020
DOIs
Publication statusE-pub ahead of print - 28 Apr 2020

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Keywords

  • investment analysis
  • algorithmic trading
  • online portfolio selection
  • market impact cost
  • limit order book

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