Design of a Quantum-Based AI Model for Real-Time Stock Price Prediction at Nigerian Stock Exchange, Abuja
- Project Research
- 1-5 Chapters
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- NGN 5000
Background of the Study
Stock market prediction is a critical field in finance that uses algorithms and data analysis to predict stock prices and assist investors in making informed decisions. Traditional predictive models based on machine learning (ML) and statistical techniques have shown limited accuracy, especially in volatile market conditions. Quantum computing, with its ability to process large datasets and perform complex calculations at high speeds, offers the potential to enhance stock price prediction models by improving accuracy and prediction times.
This study aims to design a quantum-based AI model to predict stock prices in real-time at the Nigerian Stock Exchange (NSE), leveraging quantum computing’s potential to optimize the predictive capabilities of AI models.
Statement of the Problem
Traditional AI and machine learning models used for stock price prediction often face challenges related to large data volumes, market volatility, and limited computational resources. Quantum computing can provide enhanced computational power, enabling more accurate and timely stock price predictions. However, the integration of quantum-based AI models into real-time stock prediction remains largely unexplored in Nigeria's stock market.
Objectives of the Study
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To design a quantum-based AI model for predicting real-time stock prices at the Nigerian Stock Exchange.
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To evaluate the effectiveness of quantum computing in improving the accuracy and efficiency of stock price predictions.
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To assess the feasibility of implementing quantum-based AI models for real-time stock price prediction at the NSE.
Research Questions
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How can quantum computing improve the accuracy of stock price predictions at the Nigerian Stock Exchange?
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What quantum algorithms can be integrated into AI models for real-time stock prediction?
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What challenges and opportunities exist in implementing quantum-based AI models in the Nigerian stock market?
Significance of the Study
The findings of this study will provide a groundbreaking approach to stock price prediction, leveraging quantum computing’s power to provide investors and traders with more accurate and timely forecasts. This will have the potential to improve decision-making, reduce market risk, and enhance the efficiency of financial markets in Nigeria.
Scope and Limitations of the Study
This study will focus on designing and testing a quantum-based AI model for stock price prediction at the Nigerian Stock Exchange. Limitations may include access to quantum computing resources, the complexity of financial market data, and the volatility of stock prices.
Definitions of Terms
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Quantum Computing: A type of computing that uses quantum mechanics to process information, offering exponential improvements in computational power for certain problems.
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Artificial Intelligence (AI): The use of computer algorithms to simulate human intelligence, particularly in data analysis and pattern recognition.
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Stock Price Prediction: The process of forecasting future stock prices based on historical data, market trends, and statistical models.
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