Geometric Fractional Brownian Motion Model in Stock Price Forecasting PT Indofood Sukses Makmur Tbk Using Python Programming

Abstract

Accurate stock price forecasting is needed by investors. Several methods can be used to forecast stock prices, such as trend models, Autoregressive Integrated Moving Average, Double Moving Average, and Exponential Smoothing. Apart from that, there are also more complex models, such as the Geometric Brownian Motion (GBM) model and the Geometric Fractional Brownian Motion (GFBM) model. The GBM and GFBM models have several advantages, including being able to predict stock prices over short time periods, the suitability of the model to stock price movements which are always positive and do not require a lot of data testing. Moreover, GFBM model can also overcome the problem of actual stock data, most of which are not independent of each other. This research aims to forecast the stock price of PT Indofood Sukses Makmur Tbk (INDF) using the Geometric Fractional Brownian Motion (GFBM) model. The Hurst index in the GFBM model is estimated using Rescaled Range (R/S) with the help of Python programming. The results of forecasting the share price movement of PT Indofood Sukses Makmur Tbk (INDF) using the GFBM model provide very accurate values based on the MAPE value.