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Optimizing Value at Risk and Forecasting Financial Trends Using Taguchi L16 Method and Linear Trend Analysis: A Study Across Multiple Companies |
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PP: 121-131 |
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doi:10.18576/amis/200108
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Author(s) |
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Amir Ahmad Dar,
Mohammad Shahfaraz Khan,
Naushad Alam,
Imran Azad,
Amit Kumar Pathak,
Aseel Smerat,
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Abstract |
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| In order to maximize Value at Risk (VaR) and predict future financial trends for four companies—Apple, Coca-Cola, Amazon, and McDonald’s—this study combines statistical methods, a basic linear trend model, and the Taguchi L16 method. The Taguchi orthogonal array was used to analyze the impact of three important parameters on VaR at four levels: mean return (μ), standard deviation σ , and stock price (St ). The most important parameters were found, their impacts were ranked, and their interactions were investigated using analysis of variance (ANOVA) and analysis of means (ANOM). After that, historical data was subjected to a basic linear trend model in order to forecast future VaR behavior. Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) were used to validate the correctness of the model. The findings provide a solid framework that blends experimental design and predictive modeling for data-driven financial decision-making, exposing the key factors influencing VaR and providing insights into future risk trajectories. |
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