Author(s)

Masimba E. Sonono^1*, Hopolang P. Mashele^2,3

¹Unit for Business Mathematics and Informatics, North-West University, Potchefstroom Campus, Potchefstroom, Republic of South Africa.
²Centre for Business Mathematics and Informatics, North-West University, Potchefstroom Campus, Potchefstroom, Republic of South Africa.
³African Institute for Mathematical Sciences, Muizenberg, Cape Town, Republic of South Africa.

Predicting stock price movement is generally accepted to be challenging such that until today it is continuously being attempted. This paper attempts to address the problem of stock price movement using continuous time models. Specifically, the paper provides comparative analysis of continuous time models—General Brownian Motion (GBM) and Variance Gamma (VG) in predicting the direction and accurate stock price levels using Monte Carlo methods—Quasi Monte Carlo (QMC) and Least Squares Monte Carlo (LSMC). The hit ratio and mean-absolute percentage error (MAPE) were used to evaluate the models. The empirical tests suggest that either the GBM model or VG model in any Monte Carlo method can be used to predict the direction of stock price movement. In terms of predicting the stock price values, the empirical findings suggest that the GBM model performs well in the QMC method and the VG model performs well in the LSMC method.

Monte Carlo, Stock Price, Geometric Brownian Motion, Variance Gamma, Maximum Likelihood Estimation, MAPE, Hit Ratio

Sonono, M. and Mashele, H. (2015) Prediction of Stock Price Movement Using Continuous Time Models. Journal of Mathematical Finance, 5, 178-191. doi: 10.4236/jmf.2015.52017.