Which Crucial Economic Variables Do Drive Specific Sector in Stock Exchange of Thailand Indexes? Evidences from Bayesian Perspective

In this paper, we present the implementation of Dynamic model averaging (DMA) algorithm where the posterior inclusion probability is crucial estimated to deliver the results from the model questions of what actually drive specific Stock Exchange Thailand (SET) indexes. The predictors we use in this work are the fundamental economic variables: Borrowing Rates, Policy Rate, Minimum Overdraft Rates and Lending Rates. These factors are ones of the most important monetary tools from Bank of Thailand. The empirical results demonstrate that each SET index sectors response to those economic variables differently. Some of them are not actually correlated in some specific periods of time. For the others periods of time, however, they do affect to SET indexes.