UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
| ||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| A mathematical model known as Geometric Brownian motion has proven to be an effective tool that can be deployed to forecast the price of goods in the future due to the presence of random terms, which represent the stochastic or random fluctuation of prices over a given period of time. The success of this model revolves around the estimation of its governing parameters. To efficiently predict the price of goods, using the Geometric Brownian Motion model (GBM), one needs to determine the value of returns and use the calculated returns to estimate the value of drift as well as volatility. This research used the value of volatility and drift terms obtained from real data of sugar in the months under review. The method has shown to be very reliable in capturing the intelligent trend in the price of sugar in Malaysia. The model was able to stimulate the pattern of the predicted price that shares a great resemblance to the actual price of sugar. The result obtained is very encouraging and places this study with a good note now that the country is just returning to normalcy from the pandemic that has crippled the economies of most developing nations in the world. We used the model to predict the price of sugar for a period of 20 months and the result of our prediction as well as the graph of the predicted prices confirmed the practicability of the model. The obtained values of MAPE and MSE, two of the popular performance metrics, also justified the effectiveness of the GBM in capturing the trend in the data. This model can be classified as a good model that can be deployed to forecast the price of goods that exhibit high volatility.
Keywords: Brownian motion, Drift value, MAPE, MSE, Volatility, Performance metric |
| References |
Abidin SN, Jaffar MM. (2014). Forecasting share prices of small size companies in Bursa Malaysia using geometric Brownian motion. Applied Mathematics & Information Sciences, 8(1), 107. Agustini WF, Affianti IR, Putri ER. (2018). Stock price prediction using geometric Brownian motion. Journal of physics: conference series. IOP Publishing, 012047. Always AE, Zamri, Mohd Kasihmuddin MS, Mansor A, Sathasivam S. (2020). Palm oil trend analysis via logic mining with discrete hopfield neural network. Pertanika Journal of Science & Technology, 28(3), 967-981. Chang TH, Wang N, Chuang WB. (2021). Stock price prediction based on data mining combination model. Journal of Global Information Management, 30(7), 1-19. Chirwa TG, Odhiambo NM. (2019). An empirical test of exogenous growth models: Evidence from three southern African countries. Economic Annals, 64, 7-38. Deaconu A, Buiga A, Tothazan H. (2022). Real estate valuation models performance in price prediction. International Journal of Strategic Property Management, 26(20, 86-105. Debus P. (2013). Application of stochastic volatility models in option pricing. GRIN Verlag, Germany. Dmouj A. (2006). Stock price modelling: Master Thesis, Vrije Universiteit. Farida AW, Affianti IR, Putri ER. (2018). Stock price prediction using geometric Brownian motion. In Journal of Physics: Conference Series,IOP Publishing, 012047. Hamdan ZN, Ibrahim SNI, Mustafa M. (2020). Modelling malaysian gold prices using geometric Brownian motion model. Advances in Mathematics: Scientific Journal, 9, 7463-7469. Hanke JE, Wichern DW. (2005). Business Forecasting. New Jersey, 2005 : Pearson Education Inc. Hassler U. (2016). Stochastic Processes and Calculus: An Elementary Introduction with Applications Stochastic Processes and Calculus. Springer. Hndi BM, Maitah M, Mustofa J. (2016). Trade impacts of selected free trade agreements on agriculture: the case of selected North African countries. Agris on-line Papers in Economics and Informatics, 1, 39-50. Jay P, Kalariya V, Parmar P, Tanwar S, Kumar N, Alazab M. (2020). Stochastic neural networks for cryptocurrency price prediction. IEEE Access, 8, 2804-2818. Jordan S, Philips AQ. (2018). Cointegration testing and dynamic simulations of autoregressive distributed lag models. The Stata Journal, 18(4), 902-923. Khashei M, Bijari M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with Applications, 37(1), 479-489. Madziwa L, Pillalamarry M, Chatterjee S. (2022). Gold price forecasting using multivariate stochastic model. Resources Policy, 76, 102544. Maitah M, Smutka L. (2016). Restoration and growth of the Russian sugar market. Sugar Tech, 18, 115-123. Nemes MD, Butoi A. (2013). Data mining on Romanian stock market using neural networks for price prediction. Informatica Economica, 17, 125-136 Rojas EM, Moreno HB, Soto MD, Nuñez SO. (2018). Static model and neural networks in the prediction of price using time series. In Proceedings of the International Conference on Algorithms, Computing and Artificial Intelligence. Shabri A. (2001). Comparison of time series forecasting methods using neural networks and Box-Jenkins model. Matematika. Malaysian Journal of Industrial and Applied Mathematics, 17, 25-32. Shahrour M, Dekmak M. (2022). Intelligent Stock Prediction: a neural network approach. International Journal of Financial Engineering, 10(1), 2250016. Tukaew S, Datta A, Shivakoti GP, Jourdain D. (2016). Production practices influenced yield and commercial cane sugar level of contract sugarcane farmers in Thailand. Sugar Tech, 18, 299-308. Tursoy T, Faisal F. (2018). The impact of gold and crude oil prices on stock market in Turkey: Empirical evidences from ARDL bounds test and combined cointegration. Resources Policy, 55, 49-54. |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |