The impact of transaction costs on forecast-based trading strategy performance

Lewis-Cheetham, James, Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, Liberatore, Federico ORCID: https://orcid.org/0000-0001-9900-5108 and Wang, Qingwei ORCID: https://orcid.org/0000-0002-3695-7846 2024. The impact of transaction costs on forecast-based trading strategy performance. Presented at: CIFEr 2024: IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, Hoboken, New Jersey, USA, 22-23 October 2024. Item availability restricted.

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Abstract

Active investing strategies have poor historical longterm performance compared to passive strategies. Furthermore, many active strategies use forecasting of various signals even though the efficient market hypothesis posits that stock prices rapidly incorporate information. Despite this, recent studies continue to explore the use of forecast-based strategies and report positive evaluation results.We closely investigate several inter-day active management strategies based on forecasting models via a test set and a U.S. market backtest. We approximated transaction costs via bid and ask prices and percentage costs. Test metrics indicated that models could not forecast over week-long horizons but demonstrated some forecasting ability over monthly and quarterly horizons. Backtest results indicate a null correlation between test metrics and strategy performance. For low transaction cost rates, several strategies achieved higher Sharpe ratios than a benchmark passive strategy. At 25 basis points a single strategy had comparable performance to the benchmark. It is unclear to what extent this observation of superior strategy performance was due to the quantity of strategies we evaluated. Our findings emphasise the importance of evaluating strategies under realistic trading conditions and suggest further investigation into the null correlation between forecast accuracy and strategy performance.

Item Type:	Conference or Workshop Item (Paper)
Status:	In Press
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Machine Learning, Algorithmic Trading, Financial Forecasting, Backtesting, Efficient Market Hypothesis, Temporal Arbitrage.
Date of First Compliant Deposit:	2 October 2024
Date of Acceptance:	2 July 2024
Last Modified:	03 Oct 2024 01:29
URI:	https://orca.cardiff.ac.uk/id/eprint/172410

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