THE IMPACT OF MACHINE LEARNING IN PREDICTING STOCK MARKET TRENDS IN NIGERIA: A CASE STUDY OF ABUJA STOCK EXCHANGE
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- NGN 5000
Background of the Study
The stock market is a vital component of economic growth, providing a platform for investment and capital mobilization. However, predicting stock market trends remains one of the most complex and challenging tasks due to the dynamic and volatile nature of financial markets. In Nigeria, where the stock market serves as a critical indicator of economic health, accurate predictions can significantly impact investment decisions and market stability.
Machine learning, a subset of Artificial Intelligence, has emerged as a powerful tool for predicting stock market trends. Unlike traditional statistical methods, machine learning algorithms can process vast amounts of historical and real-time data, identify hidden patterns, and generate predictive models with higher accuracy. Applications of machine learning in stock market prediction include sentiment analysis, algorithmic trading, and risk assessment, all of which contribute to informed decision-making and increased market efficiency.
The Abuja Stock Exchange plays a central role in Nigeria’s financial ecosystem, attracting investors and facilitating capital formation. Integrating machine learning models into its operations could enhance the exchange’s analytical capabilities, empowering investors with data-driven insights and improving market transparency. This study examines the impact of machine learning on stock market predictions, focusing on its application in the Abuja Stock Exchange.
Statement of the Problem
Stock market volatility and the complexity of financial data make trend prediction challenging in Nigeria, often leading to suboptimal investment decisions. Machine learning offers a solution by providing accurate and dynamic predictive models, yet its adoption in Nigeria’s financial sector remains limited. This study explores the impact of machine learning on predicting stock market trends at the Abuja Stock Exchange, addressing a critical need for data-driven decision-making.
Aim and Objectives of the Study
Aim:
To assess the impact of machine learning on stock market trend prediction at the Abuja Stock Exchange.
Objectives:
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To evaluate the limitations of traditional stock market prediction methods at the Abuja Stock Exchange.
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To explore the application of machine learning algorithms in stock trend prediction.
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To assess the impact of machine learning predictions on investment decision-making and market efficiency.
Research Questions
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What are the limitations of traditional methods in predicting stock market trends?
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How can machine learning improve the accuracy of stock market trend predictions in Nigeria?
Research Hypotheses
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Machine learning significantly improves the accuracy of stock market trend predictions.
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The adoption of machine learning enhances investment decision-making at the Abuja Stock Exchange.
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Machine learning applications increase market efficiency by reducing prediction errors.
Significance of the Study
This study provides valuable insights into the role of machine learning in transforming stock market predictions in Nigeria. By focusing on the Abuja Stock Exchange, it highlights the potential of AI technologies to enhance investment strategies and market stability.
Scope and Limitation of the Study
The study focuses on the use of machine learning for stock market trend predictions at the Abuja Stock Exchange. Limitations include potential constraints in accessing comprehensive financial data and the study’s specific focus on one stock exchange.
Definition of Terms
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Machine Learning (ML): A branch of AI that uses algorithms to analyze data and make predictions.
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Stock Market Trends: Patterns and movements in stock prices influenced by various factors.
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Abuja Stock Exchange: A financial market platform in Nigeria facilitating securities trading.
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