Algorithmic Trading for Online Portfolio Selection under Limited Market Liquidity

Youngmin Ha, Hai Zhang

Research output: Working paper

Abstract

We propose an optimal intraday trading algorithm to reduce overall transaction costs through absorbing price shocks when an online portfolio selection (OPS) 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 minimise the overall transaction costs, including both the market impact costs and the proportional transaction costs. The proposed trading algorithm, compatible to any OPS methods, optimises the number of intraday trades as well as 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 in an environment of limited market liquidity.
LanguageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Number of pages35
Publication statusPublished - 30 Oct 2018

Fingerprint

Portfolio selection
Market liquidity
Algorithmic trading
Transaction costs
Limit order book
Market order
Backtesting
Market impact
Proportional transaction costs
Costs

Keywords

  • algorithmic trading
  • online portfolio selection
  • market impact cost
  • limit order book
  • OPS
  • financial securities

Cite this

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Algorithmic Trading for Online Portfolio Selection under Limited Market Liquidity. / Ha, Youngmin ; Zhang, Hai.

Glasgow : University of Strathclyde, 2018.

Research output: Working paper

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