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All Journal Asian Journal of Science, Technology, Engineering, and Art Mikailalsys Journal of Mathematics and Statistics
Daniel, Adashu Jacob
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Arts Computer Science & IT Engineering Mathematics Mechanical Engineering Social Sciences

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Modeling Stock Data Using Multiple Linear Regression and LASSO Regression Analysis Daniel, Adashu Jacob; Ibrahim, Musa Dahiru; Josaphat, Anule Aondulum
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 2 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i2.5927

Abstract

This study evaluates and compares the model fitting and predictive performance of Multiple Linear Regression (MLR) and Least Absolute Shrinkage and Selection Operator (LASSO) regression in the context of stock price prediction for four leading Nigerian companies. A dataset comprising 1,300 observations from 2019 to 2025 was obtained from Yahoo Finance and Investing.com. Multicollinearity assessment using the Variance Inflation Factor (VIF) revealed substantial collinearity among certain predictors, particularly for the variables "Open" (Honeywell: 55.45; Zenith: 920.30) and "Low" (Oando: 621.81), indicating the need for variable selection or dimensionality reduction. Comparative analysis based on model selection criteria, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) demonstrated the superior performance of LASSO over MLR across all companies. For example, Honeywell's LASSO model recorded an AIC of –12,112.64 and an MSE of 0.000021, compared to MLR's AIC of –2,690.54 and MSE of 0.00998. LASSO regression also identified key predictors such as "High" price, which exhibited strong statistical significance for Oando (z = 18.991, p < 0.001) and Zenith (z = 7.066, p < 0.001), whereas trading volume generally showed weak predictive power. The study concludes that LASSO provides a more parsimonious and accurate predictive model for financial time-series data. It is recommended for use in financial forecasting and investment analysis, particularly when dealing with multicollinear datasets and high-dimensional predictor variables.
Modeling Volatility Using Bayesian GARCH with Student-t and Generalized Error Distributions: A Case Study of Bitcoin Daniel, Adashu Jacob; Josaphat, Anule Aondolum
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.5926

Abstract

This study investigates the optimal model for capturing and forecasting volatility in the cryptocurrency market, with a specific focus on Bitcoin (BTC). Various Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models are evaluated to determine the most effective approach for modeling the stylized facts commonly observed in financial time series data. While the Maximum Likelihood Estimation (MLE) method is widely employed for estimating GARCH model parameters, this study introduces a Bayesian framework, utilizing the Metropolis-Hastings algorithm to estimate parameters of the symmetric GARCH(1,1) model. Under this approach, model parameters are treated as random variables with known prior distributions. The analysis is based on 2,000 daily BTC observations from January 2018 to June 2023, obtained from Yahoo Finance. Model selection criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Hannan–Quinn Criterion (HQC), identified the EGARCH(1,1) model under the Student-t and Generalized Error Distributions as the most suitable for capturing BTC volatility. Results further indicate the presence of volatility asymmetry and persistence, characteristic of cryptocurrency markets. In terms of predictive performance, the Bayesian GARCH(1,1) model under the Generalized Error Distribution and the EGARCH(1,1) model under the Student-t distribution exhibited the lowest values for RMSE, MAE, MAPE, and ME, confirming their suitability for future volatility forecasting in the cryptocurrency space.
Co-Authors Ibrahim, Musa Dahiru Josaphat, Anule Aondolum Josaphat, Anule Aondulum