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The Role of Algorithmic Trading in Shaping Futures Market Efficiency: A Review of Recent Research
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Author(s): Hamad Raza (Lyallpur Business School, Government College University Faisalabad, Pakistan), Nimra Riaz (Department of Management Science, Riphah International University Faisalabad, Pakistan), Hiral Vadagama (Arden University, Germany), Ahsan Riaz (Lyallpur Business School, Government College University Faisalabad, Pakistan)and Suresh Ramakrishnan (Faculty of Management, Universiti Teknologi Malaysia, Malaysia)
Copyright: 2025
Pages: 24
Source title:
Algorithmic Training, Future Markets, and Big Data for Finance Digitalization
Source Author(s)/Editor(s): Hamad Raza (Lyallpur Business School, Government College University, Faisalabad, Pakistan
), Ahsan Riaz (Lyallpur Business School, Government College University, Faisalabad, Pakistan
), Nimra Riaz (Department of Management Science, Riphah International University, Faisalabad, Pakistan
)and Suresh Ramakrishnan (Faculty of Management, Universiti Teknologi Malaysia, Malaysia)
DOI: 10.4018/979-8-3693-6386-7.ch009
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Abstract
This systematic review investigates the transformative impact of algorithmic trading (AT) on the efficiency of futures markets, integrating insights from contemporary research. Through the examination of 291 academic publications, predominantly peer-reviewed articles, the review demonstrates that AT generally improves market efficiency by expediting information processing, reducing bid-ask spreads, and enhancing liquidity. However, issues related to high-frequency trading (HFT) persist, given its capacity to increase short-term volatility and destabilise markets during stress. Moreover, cluster analysis underscores algorithmic trading and financial markets as significant research areas, with increasing focus on GARCH models, investor sentiment, and attention metrics. The identified gaps in the existing literature, particularly concerning long-term impacts and cross-market dynamics, emphasise the necessity for future investigations utilising advanced methodologies such as machine learning to gain a deeper understanding of AT's evolving significance.
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