BBCA Stock Price Prediction Using Linear Regression Method
DOI:
https://doi.org/10.63158/IJAIS.v1.i1.7Keywords:
linear regression, stock price prediction, bbca, financial forecasting, volatile marketsAbstract
This study focuses on predicting the stock price of Bank Central Asia (BBCA) using linear regression techniques, a widely utilized statistical method in financial forecasting. Stock price prediction is critical for investors, particularly in volatile markets like Indonesia. This research analyzes the relationship between key variables, such as adjusted closing prices and trading volume, based on historical data. The methodology includes data collection, preprocessing, model construction, and evaluation using metrics like Root Mean Square Error (RMSE) to assess the model's accuracy. The results indicate that linear regression can effectively predict BBCA stock prices with reasonable accuracy, providing a practical and interpretable tool for investors. These findings contribute to financial forecasting by demonstrating the utility of linear regression in stock price prediction, particularly in emerging markets.
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Copyright (c) 2024 Shannon Dominique Saputra, Albertus Dwiyoga Widiantoro (Author)
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