Utilizing linear regression to forecast the stock price fluctuations of top-rated companies - Eka, Andi Primafira Bumandava; Bakri, Asri Ady; Yuliyani, Leny (Jurnal Info Sains : Informatika dan Sains, 2024)
The purpose of this study is to use linear regression to forecast the closing stock price of the top 10 issuers from the LQ45 Index. When it is appropriate to purchase or sell the stock, it is determined by comparing the forecasted close price with the actual stock price. The relationship between independent factors (such past stock prices) and dependent variables (stock prices) is modelled using the linear regression approach. The prediction error rate is then calculated by comparing the expected and actual outcomes using the Root Mean Square Error (RMSE). It is evident from the data that the close stock price's anticipated growth does not consistently rise each month. For this reason, this prediction is crucial in assisting investors in their decision-making. It makes sense to sell the stock if the estimated growth of its closing price is expected to climb; conversely, if the projection is expected to fall, it makes sense to purchase the stock. According to the test findings, the linear regression model is capable of producing precise predictions that help investors decide what to buy on the Indonesian stock market.