Simulation of geometric Brownian motion in stock price

Shuonan Chen; Ziting Huang; Yuchen Lai; Xingyi Lu

doi:10.1117/12.2628052

22 April 2022 Simulation of geometric Brownian motion in stock price

Shuonan Chen, Ziting Huang, Yuchen Lai, Xingyi Lu

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 1216334 (2022) https://doi.org/10.1117/12.2628052
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Geometric Brownian motion model is overwhelmingly advantageous as a model to simulate stock price. GBM is a Markov process and could be a martingale under certain situations, which is a feature consistent with stock price volatility; additionally, the GBM model accentuates the percentage instead of an absolute number of changes in stock price, which is another feature consistent with the stock market. Accordingly, “overall time horizons the chances of a stock price simulated using GBM moving in the same direction as real stock prices was a little greater than 50 percent.” In reality, the GBM model has been widely applied in many aspects. This research focuses on combining mathematics, finance, and computer science, applying the GBM model on Python in relatively basic methods to forecast certain stock price changes.

Citation Download Citation

Shuonan Chen, Ziting Huang, Yuchen Lai, and Xingyi Lu "Simulation of geometric Brownian motion in stock price", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 1216334 (22 April 2022); https://doi.org/10.1117/12.2628052

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