Linear Brand Dominance in Toyota Brand Positioning High-Resolution Analysis (1980-2024)
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Abstract
This research aims to explore the effectiveness of Linear Regression in predicting the stock price of Toyota Motor Corporation. It compares its performance with more complex models, including Long Short-Term Memory (LSTM) and Random Forest. The study utilizes historical stock price data from 1980 to 2024, sourced from Yahoo Finance, and employs rigorous data preprocessing and feature engineering techniques, such as moving averages, volatility measures, and lagged features. The sample community consists of over 11,000 data points representing Toyota's stock price history. By analyzing the stability of Toyota's historical performance and leveraging both statistical and machine learning models, the study provides insights into the relationship between simplicity and predictive accuracy in financial forecasting. The results reveal that Linear Regression outperformed LSTM and Random Forest, achieving an R-squared value of 0.9990, making it a highly effective tool for predicting stock prices in stable market environments. Based on these findings, the study recommends that investors and analysts consider simpler models like Linear Regression for forecasting stock prices in contexts characterized by stable historical trends while exploring hybrid approaches for more volatile markets.
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