A New Self Adaptive Differential Evolution: Its Application in Forecasting the Index of Stock Exchange of Thailand

Novel Dynamic and Scalable Storage Management Architecture

“…The assumption underlying this study was that the missing data on non trading day will be fulfilled with the previous data. The MAPE (Mean Average Percentage Error) of two year is more convenient and smoother with respect to MAPE of one year [18,19]. The longer period of training data can yield lower error, except the lowest error level.…”

Analysis of Calendar Effects: Day-of-the-Week Effect on the Stock Exchange of Thailand (SET)

“…The empirical findings are reported in Section 4, and the final section provides a summary of the paper. [8][9][10][11][12][13][14][15]. Since countries are linked together, movement on one stock market may have an impact on other stock markets.…”

“…Naturally, the Thai stock market has unique characteristics, so the factors influencing the prices of stocks traded in this market are different from the factors influencing other stock markets [14]. An example of factors that influence the Thai stock market are foreign stock indexes, the value of the Thai Baht, the price of oil, the price of gold, the Minimum Loan Rate (MLR) and many others [8,9,10,11,12,13]. There were some researchers that used these factors to forecast the Stock Exchange of Thailand (SET) index such as Tantinakom [8] who used trading value, trading volume, interbank overnight rate, inflation, net trading value of investment, value of the Thai Baht, price earning ration, the Dow Jones index, the Hang Seng index, the Nikkei index, the Straits Times Industrial index and the Kuala Lumpur Stock Exchange Composite index.…”

“…In 2005, Rimcharoen et al [12] used the Dow Jones index, the Nikkei index, the Hang Seng index, the price of gold and the Minimum Loan Rate (MLR). In 2007, Worasucheep [13] used the Minimum Loan Rate (MLR), the exchange rate of the Thai Baht and the US dollar, daily effective over-night federal fund rates in the USA, the Dow Jones index and the price of oil. In 2008, Chaigusin et al [14] used the Dow Jones index, the Nikkei index, the Hang Seng index, the price of gold, the Minimum Loan Rate (MLR) and the exchange rate of the Thai Baht and the US dollar.…”

Forecasting FTSE Bursa Malaysia KLCI trend with Hybrid Particle Swarm Optimization and Support Vector Machine technique

“…Major stock market dataset are commonly used by researchers in predicting the stock trend, i.e. the United States's stock indices including the Nasdaq index, the Dow Jones index and the S&P 500 index, the Nikkei index, Hang Seng index, etc [24][25][26][27][28]. However, other stock indices are equally important especially to the local community as they are better indicators to reflect the local economy status.…”