Forecasting Cyclical and Non-cyclical Stock Prices on the Stock Exchange of Thailand




Forecasting, Geometric Brownian motion, Fourier’s series, Cauchy initial value problem


Forecasting is an important role in organizations for decision making and planning. This research is to forecast the cyclical and non-cyclical weekly stock prices on the Stock Exchange of Thailand by using the models of Geometric Brownian motion, Fourier’s series, and Cauchy initial value problem. The accuracy and performance of the models are based on the minimum root mean squared percentage error which is the error between actual and forecasted stock prices. The results showed that Geometric Brownian motion is suitable for forecasting both cyclical and non-cyclical stock prices because of minimum error. Moreover, the confidence intervals of forecasted stock prices are demonstrated. Therefore, Geometric Brownian motion should be selected to describe the movement of stock prices in Thailand.


G. Box and G. Jenkins, “Time series analysis: forecasting and control,” San Francisco: Holden-Day; 1970.

J. Contreras, R, Espinola, F, Nogales and A. Conejo, “ARIMA Models to Predict Next day Electricity prices,” IEEE Transactions on Power Systems, vol. 18, no.3, pp. 1014- 1020, 2003.

E. Weiss, “Forecasting commodity prices using ARIMA,” Technical Analysis of Stocks & Commodities, vol. 18, no. 1, pp. 18-19, 2000.

A. Rahman and M. Hasan, “Modeling and Forecasting of Carbon Dioxide Emissions in Bangladesh Using Autoregressive Integrated Moving Average (ARIMA) Models,” Open Journal of Statistics, vol. 7, pp. 560-566, 2007.

N. Thabani, O. Hamadziripi and M. Chipo, “Modelling and forecasting carbon dioxide emissions in China using autoregressive integrated moving average (ARIMA) models,” International Journal of Multidisciplinary Research, vol. 5, no. 4, pp. 2455–3662, 2019.

S. Ketut and J. Miftahul, “Forecasting Model Selection of Curly Red Chili Price at Retail Level,” Indonesian Journal of Agricultural Research, vol. 2, no.1, pp. 1-12, 2019.

R. Anokye , E. Acheampong, I. Owusu and E. Obang, “Time series analysis of malaria in Kumasi: Using ARIMA models to forecast future incidence,” Cogent Social Sciences, vol. 4, no. 1, pp. 1–13, 2018.

K. Divya, S. Rajeswari, D. Bhavani and P. Sumathi, “Forecasting Monthly Prices of Bengalgram in Selected Markets of Andhra Pradesh,” International Journal of Research in Agricultural Sciences, vol. 4, no. 4, pp. 2348–3997, 2017.

S. Nop, “Trading Gold Future with ARIMA-GARCH model,” Thai Journal of Mathematics, Special Issue (2018), pp. 227–238, 2017.

H. Pham and B. Yang, “Estimation and forecasting of machine health condition using ARMA/GARCH model,” Mechanical Systems and Signal Processing, vol. 24, pp. 546–558, 2010.

H. Liu and J. Shi. “Applying ARMA-GARCH approaches to forecasting short-term electricity prices,” Energy Economics, vol. 37, pp. 152-166, 2013.

B. Zou, D. He and Z. Sun, “Traffic Modeling and Prediction using ARIMA/GARCH Model in Modeling and Simulation Tools for Emerging Telecommunication Networks,” Boston: Springer; 2006.

D. Chaido, “The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price,” International Journal of Energy Economics and Policy, vol. 8, no. 3, pp. 14-21, 2018.

P. Pai and C. Lin, “A hybrid ARIMA and support vector machines model in stock price forecasting,” Omega, vol. 33, pp. 497–505, 2005.

G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neuro computing, vol. 50, no. 50, pp. 159–175, 2003.

M. Fang M, H. Gwo, C. Hsiao and J. Benjamin, “Fuzzy ARIMA model for forecasting the foreign exchange market,” Fuzzy Sets and Systems, vol. 118, no. 1, pp. 9-19, 2001.

T. Sheida, K. Mehdi and B. Mehdi, “A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets,” Economic Analysis and Policy, vol. 58, pp. 22-31, 2018.

M. Fang and H. Gwo, “A fuzzy seasonal ARIMA model for forecasting,” Fuzzy Sets and Systems, vol. 126, pp. 367–376, 2002.

The Stock Exchange of Thailand, “Securities Analysis using Fundamental Analysis,” 4th edition Bangkok: The Stock Exchange of Thailand; 2002.

D. Higham, “An Introduction to Financial Option Valuation: Mathematics, Stochastics and Computation,” UK: Cambridge University Press; 2004.

P. O’Neil, “Advanced Engineering Mathematics,” 7th edition, Boston: PWS Publishing; 2011.

L. Marcela, “The analysis of the commodity price forecasting success considering different numerical models sensitivity to prognosis error,” Acta Logistica, vol. 3, no. 4, pp. 7-15, 2016.