Artificial Intelligence in stock broking: A systematic review of strategies and outcomes
1 Department of Accounting, City Power, Johannesburg, South Africa.
2 Walden University, USA.
3 Department of Information Management, Ahmadu Bello University, Zaria, Nigeria.
4 Independent Researcher, Nashville, Tennessee, USA.
5 Department of Business Administration, Skinner School of Business, Trevecca Nazarene University, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 1950–1957
Article DOI: 10.30574/wjarr.2024.21.2.0442
Publication history:
Received on 27 December 2023; revised on 03 February 2024; accepted on 05 February 2024
Abstract:
Artificial Intelligence (AI) has emerged as a transformative force in the field of stock broking, revolutionizing traditional trading strategies and reshaping financial markets. This systematic review delves into the diverse array of AI-driven strategies employed in stock broking and assesses their outcomes, shedding light on the evolving landscape of algorithmic trading. The study encompasses a comprehensive analysis of various AI models, including machine learning algorithms, deep neural networks, and natural language processing techniques, that have been harnessed to analyze market data, predict stock movements, and optimize trading decisions. By synthesizing existing literature, the review offers insights into the effectiveness and limitations of these strategies, providing a nuanced understanding of their impact on market dynamics. Key findings reveal that AI applications in stock broking exhibit a wide spectrum of approaches, ranging from predictive modeling for price forecasting to sentiment analysis for gauging market sentiment. The review also explores the integration of reinforcement learning in algorithmic trading, highlighting the adaptive nature of AI systems in responding to dynamic market conditions. Furthermore, the outcomes of AI-driven strategies are evaluated in terms of risk management, profitability, and overall market efficiency. The review identifies trends indicating increased efficiency and reduced human biases, but also acknowledges challenges related to model interpretability, ethical considerations, and the potential for algorithmic-driven market volatility. This systematic review contributes to the evolving discourse on the role of AI in stock broking, offering a holistic examination of strategies and outcomes. As financial markets continue to embrace technological advancements, understanding the nuances of AI applications becomes paramount for market participants, regulators, and researchers alike. This study serves as a valuable resource for stakeholders seeking to navigate the complex interplay between artificial intelligence and the stock broking landscape.
Keywords:
Stock Broking; AI; Finance; Trading; Risk Management; Review
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0