@article{(Open Science Index):https://publications.waset.org/pdf/10013177,
	  title     = {Mathematical Analysis of Stock Prices Prediction in a Financial Market Using Geometric Brownian Motion Model},
	  author    = {Edikan E. Akpanibah and  Ogunmodimu Dupe Catherine},
	  country	= {},
	  institution	= {},
	  abstract     = {The relevance of geometric Brownian motion (GBM) in modelling the behaviour of stock market prices (SMP) cannot be over emphasized taking into consideration the volatility of the SMP. Consequently, there is need to investigate how GBM models are being estimated and used in financial market to predict SMP. To achieve this, the GBM estimation and its application to the SMP of some selected companies are studied. The normal and log-normal distributions were used to determine the expected value, variance and co-variance. Furthermore, the GBM model was used to predict the SMP of some selected companies over a period of time and the mean absolute percentage error (MAPE) were calculated and used to determine the accuracy of the GBM model in predicting the SMP of the four companies under consideration. It was observed that for all the four companies, their MAPE values were within the region of acceptance. Also, the MAPE values of our data were compared to an existing literature to test the accuracy of our prediction with respect to time of investment. Finally, some numerical simulations of the graphs of the SMP, expectations and variance of the four companies over a period of time were presented using MATLAB programming software.},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {17},
	  number    = {7},
	  year      = {2023},
	  pages     = {78 - 84},
	  ee        = {https://publications.waset.org/pdf/10013177},
	  url   	= {https://publications.waset.org/vol/199},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 199, 2023},
	}