1 Department of Data Analytics, Kansas State University, USA.
2 Department of Mathematics and Statistics, College of Art and Science School:Texas Tech University, USA.
3 Department of Accounting, Finance & Economics, Bournemouth University, Bournemouth UK.
4 Department of Applied Statistics and Decision Analytics, Western Illinois University, USA.
5 Ecobank Nigeria Limited, Nigeria.
6 Department of Computer Science, College of Business and Technology, Western Illinois University, USA.
7 School of business, Computer Management Information System, Southern Illinois University, Edwardsville. USA.
8 School of Business, University of Arkansas, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 979–990
Received on 29 June 2024, revised on 07 August 2024, and accepted on 10 August 2024
This paper provides an in-depth examination of advanced techniques in algorithmic trading and machine learning, focusing on their impact on market prediction and trading strategies. As financial markets evolve, the need for sophisticated analytical methods has become paramount to gaining a competitive edge. The study covers a range of techniques including time series analysis, natural language processing (NLP), and deep learning models, highlighting their contributions to enhancing predictive accuracy and trading efficiency.
The paper explores the importance of feature engineering, model selection, and risk management in developing robust trading strategies. It also addresses the challenges and limitations inherent in financial modeling, such as data quality, overfitting, and computational complexity. Additionally, the paper examines emerging trends and technologies, including quantum computing, federated learning, and ESG integration, which are poised to shape the future of financial markets.
By synthesizing insights from various advanced techniques and their practical applications, this paper offers a comprehensive overview of the current state and future directions of algorithmic trading and machine learning in finance. It underscores the importance of continuous innovation and adaptation in maintaining a competitive advantage in the dynamic landscape of financial trading.
Machine Learning; Big Data; Decentralized Finance (DeFi); Regulatory Changes; Blockchain Technology; Sustainability
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Mayowa Timothy Adesina, Sunday David Esebre, Abdullah Tunde Adewuyi, Moshood Yussuf, Oluwaseun Aaron Adigun, Temitope Dickson Olajide, Comfort Idongesit Michael and Divine ILOH. Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development. World Journal of Advanced Research and Reviews, 2024, 23(2), 979-990. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2405