Peramalan Harga Saham Menggunakan Metode Autoregressive Dan Web Scrapping Pada Indeks Saham Lq45 Dengan Python - Dessy Tri Anggraeni (LPPM Universitas Abdurrab, 2020)
The Stock Exchange gives investors or traders the possibility to gain a profit (capital gains) or losses (capital loss) due to stock prices fluctuation. This uncertainty can be circumvented by applying forecasting methods to predict future stock prices. One of the method is Autoregressive. This method uses stock data in the past to get a formula to predict future stock prices. The stock price data history can be seen at several stock data provider pages and can be retrieved automatically using the Web Scrapper technique. This tehcnique make the result can be obtained quickly, easily, and accurately. The forecasting accuracy is measured using the MAPE (Mean Absolute Percent Error) method. This method was chosen because it is easier for commoner to understand. As a result, forecasting program are succed to give stock price predictions and their accuracy. The data tested in this study are all stocks incorporated in the LQ45 index. The average accuracy level obtained was 94,62%. The highest accuracy level is BKSL stock of 99,92% and the smallest one is ASRI stock of 90.13%.