Research Information System
International Journal of Engineering Technologies IJET, vol.9, no.3, pp.84-88, 2024 (Peer-Reviewed Journal)
Stock price prediction holds
paramount significance for individual investors, guiding crucial decisions in
financial planning and investment strategies. This research delves into the
methodology of Monte Carlo simulation, a versatile tool in financial modeling,
to assess its advantages and disadvantages in the context of predicting stock
prices. The study employs Python code to demonstrate the step-by-step
implementation of Monte Carlo simulations, emphasizing the mathematical
optimization of parameters for enhanced accuracy. Results showcase a
characteristic bell curve, offering a probabilistic perspective on potential
outcomes. Comparative analyses with other forecasting models, such as graphic
analysis, underscore the superior reliability of Monte Carlo simulation in
evaluating risks and rewards. Furthermore, the paper explores the application
of Monte Carlo simulation in real-world scenarios, such as portfolio
optimization and retirement planning, highlighting its pragmatic value for
individual investors navigating the complexities of the stock market. The
research concludes by acknowledging the limitations of the approach and
advocating for a comprehensive consideration of all relevant factors in
financial decision-making. This exploration serves as a valuable resource for
individual investors seeking informed insights into probabilistic forecasting
methods for effective stock price predictions.