A Review of AI-Driven Stock Market Prediction Models: A Case Study of Investment Firms in Plateau State
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- NGN 5000
Background of the Study
Artificial intelligence (AI) has become a game-changer in the financial sector, particularly in stock market predictions. AI-driven models, leveraging machine learning and deep learning techniques, analyze historical data, market trends, and external factors to provide accurate predictions (Okonkwo & Eze, 2024).
Investment firms in Plateau State, like their counterparts globally, face challenges such as market volatility and the need for data-driven decision-making. AI-driven stock market prediction models have the potential to enhance forecasting accuracy, optimize investment strategies, and reduce risks. This study reviews the application of AI-driven prediction models by investment firms in Plateau State.
Statement of the Problem
Despite the global success of AI-driven stock market prediction models, their adoption in Nigeria, particularly in Plateau State, remains limited. Challenges such as high implementation costs, lack of technical expertise, and limited access to quality data hinder their widespread use (Ahmed & Musa, 2023). Consequently, investment firms often rely on traditional prediction methods, which may not account for the complexities of modern financial markets.
This study addresses the gap in understanding the adoption, benefits, and challenges of AI-driven prediction models in Plateau State’s investment firms.
Objectives of the Study
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To evaluate the adoption of AI-driven stock market prediction models by investment firms in Plateau State.
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To assess the impact of these models on investment decision-making and risk management.
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To identify challenges and propose solutions for enhancing the use of AI-driven prediction models.
Research Questions
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How widely are AI-driven stock market prediction models adopted by investment firms in Plateau State?
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What impact do these models have on investment decision-making and risk management?
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What challenges hinder the adoption of AI-driven stock market prediction models, and how can they be addressed?
Research Hypotheses
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AI-driven stock market prediction models have no significant impact on investment decision-making.
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The adoption of AI-driven models does not significantly improve risk management in investment firms.
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Strategies for enhancing the adoption of AI-driven models have no significant effect on their implementation.
Scope and Limitations of the Study
The study focuses on investment firms in Plateau State and their use of AI-driven stock market prediction models. Limitations include variability in model adoption, access to proprietary data, and rapidly changing market dynamics.
Definitions of Terms
AI-Driven Models: Algorithms powered by artificial intelligence, designed to analyze data and make predictions.
Stock Market Prediction: The process of forecasting future stock prices based on historical and current market data.
Investment Firms: Organizations that manage financial assets on behalf of clients or investors.
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