Prediction of the Stock Exchange of Thailand using adaptive evolution strategies

Abstract

In this paper we present a prediction process of the Stock Exchange of Thailand index using adaptive evolution strategies. The prediction process does not require the knowledge of the functional form a priori. In each recursion step, genetic algorithm is used to evolve the structure of the prediction function, whereas the coefficient is evolved by evolution strategies. The proposed method has been shown to successfully predict the Stock Exchange of Thailand and returns an error less than 3%. This methodology is also a tool for knowledge discovery in a specific application. We have found that the SET index can be reasonably forecasted with only two factors: the Hang Seng index and Minimum Loan Rate. The proposed method also achieves a lower prediction error when compared with multiple regression method.

Discovering Pattern Associations in Hang Seng Index Constituent Stocks

“…Artificial intelligence techniques have often been used for stock ranking (Yan, R.J. & Ling, C.X., 2007), price prediction (Xiaowei Lin, Zehong Yang & Song Y., 2009 or markets trend (Sheta, A., 2006 andRimcharoen, S., Sutivong, D., &Chongstitvatana, P., 2005) and turning-point analysis (Liu, X. Wu, X, Wang, H, & Wang Y., 2008). These applications were developed based on different assumptions about the extent that the stock market follows a random-walk model.…”

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, Chaereonkithuttakorn [11] used United State stock indices including the Nasdaq index, the Dow Jones index and the S&P 500 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.…”

Novel Dynamic and Scalable Storage Management Architecture

“…It is paramount to store only relevant data for a relevant period; otherwise, researching information can become ineffective. Also, one of the biggest issues that organizations are facing with the voluminous amounts of unstructured and semi-structured data it creates is to mine all the data [16].…”