Selection of cumulative distribution function for trend in model formation for the analysis of simultaneous multiple time series data with autocorrelation, trend and outliers using bayesian method : case study in fluctuate stock price in the Stock Exchange of Thailand

Abstract

The objective of this research is to select a proper cumulative function for trend in an analysis of simultaneous multiple time series data with autocorrelation, trend, and outliers using a Bayesian method. The proposed model is applied to fluctuate stock price data in the stock exchange of Thailand. The data consist of the closing prices of 5 stocks. The algorithms are written in OpenBUGS and the performance of the model is evaluated via a simulation study in R. The cumulative distribution functions considered here are a Weibull cumulative distribution function and an exponential cumulative distribution. They are compared using RMSE, MAPE, and MAE as criteria. The research found that an exponential cumulative function for trends gives smaller values of the three criteria in both model fitting and model validation for the4 stocks, EA, NWR, and KTB. However, a Weibull cumulative distribution function gives smaller values of three criteria for the PTT in both model fitting and model validation.