Presentation av Examensarbete E: Stock price predictions using Geometric Brownian Motion
- Plats: Ångströmlaboratoriet 64119
- Föreläsare: Joel Lidén
- Kontaktperson: Maciej Klimek
In this study Geometric Brownian Motion (GBM) has been used to predict the closing prices of the Apple stock price and also the S&P500 index. Additionally, closing prices have also been predicted by using mixed ARMA(p,q)+GARCH(r,s) time series models. Using 10 years of historical closing prices between 2008-2018, the predicted prices have also been compared to observed stock prices, in order to evaluate the validity of the prediction models. Predictions have been made using Monte Carlo methods in order to simulate price paths of a GBM with estimated drift and volatility, as well as by using fitted values based on an ARMA(p,q)+GARCH(r,s) time series model. The results of the predictions show an accuracy rate of slightly above 50% of predicting an up- or a down move in the price, by both using a GBM with estimated drift and volatility and also a mixed ARMA(p,q)+GARCH(r,s) model, which is also consistent with the results of K. Reddy and V. Clinton from 2016.