Prediction Method for Real Thai Stock Index Based on Neurofuzzy Approach

  • Monruthai Radeerom
  • Chonawat Srisa-an
  • M. L. Kulthon Kasemsan
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

The prediction of financial market indicators is a topic of considerable practical interest and, if successful, may involve substantial pecuniary rewards. People tend to invest in equity because of its high returns over time. Stock markets are affected by many highly interrelated economic, political, and even psychological factors, and these factors interact in a very complex manner. Therefore, it is, generally, very difficult to forecast the movements of stock markets. Neural networks have been used for several years in the selection of investments. Neural networks have been shown to enable decoding of nonlinear time series data to adequately describe the characteristics of the stock markets [1]. Examples using neural networks in equity market applications include forecasting the value of a stock index [2–5] recognition of patterns in trading charts [6, 7], rating of corporate bonds [8], estimation of the market price of options [9], and the indication of trading signals of selling and buying [10, 11], and so on. Feedforward backpropagation networks as discussed in Sect. 24.2 are the most commonly used networks and meant for the widest variety of applications.

The Stock Exchange of Thailand (SET) is the stock market in Thailand whereby stocks may be bought and sold. As with every investment, raising funds in the stock exchange entails some degree of risk. There are two types of risk: systematic risk and an erroneous one. The erroneous risk can be overcome by a sound investment strategy, called diversification. However, by using a better prediction model to forecast the future price variation of a stock, the systematic risk can be minimized if not totally eliminated.

This chapter describes a feedforward neural network and neurofuzzy system in Sect. 24.2. Subsequently, details for the methodology of stock prediction are explained in Sect. 24.3. Next, several results are presented involving neurofuzzy predictions in comparison to feedforward neural networks. Some conclusions based on the results presented in this chapter are drawn, with remarks on future directions.


Membership Function Stock Market Stock Price Stock Index Closing Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Monruthai Radeerom
    • 1
  • Chonawat Srisa-an
    • 1
  • M. L. Kulthon Kasemsan
    • 1
  1. 1.Science Program in Information Technology (MSIT)Faculty of Information Technology, Rangsit UniversityPathumtaniThailand

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