Moving Average untuk Prediksi Harga Saham dengan Linear Regression

Abstract

Stocks as investment instruments in the capital market can provide benefits in capital gains but also have the risk of capital loss. Analysis and forecasting methods are needed to support investors. To achieve this, historical data and moving averages are used to reduce short-term random fluctuations in stock prices, and a linear regression algorithm to obtain accurate results by reducing the error rate and Mean Squared Error (MSE) value. The evaluation results show good accuracy with a strong correlation and a low Mean Absolute Percent Error (MAPE) value. In addition, testing on historical data is carried out to test the model and generate significant profits based on predictions from the model. According to the findings derived from the assessment, predicting stocks using the moving average and linear regression methods can help investors gain profits and reduce risk.