The Profitability of Moving Average Trading Strategies in the Thailand SET50 Index: Past and Future

  • Rujira Chaysiri School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University
  • Sucha Boontaricponpun School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University
  • Piraya Sujjavanich School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University
  • Kornchanok Ua-ampon School of Management Technology, Sirindhorn International Institute of Technology, Thammasat University
Keywords: Buy and hold strategy, Moving average, Logistic regression, Artificial neural network, Thailand SET 50 Index

Abstract

          Technical analysis is one of the most popular methods that some investors believe can generate profit from stock markets. However, there is no consensus that technical analysis strategies can always make profits in different asset conditions. This study focuses on finding whether moving average trading strategies can outperform the buy and hold strategy in particular asset conditions. These asset conditions are constructed from the volatility and volume of a trading period of a stock in the Thailand SET50 index. In addition, this study forecasts the asset conditions of a stock for the next period by comparing the logistic regression and artificial neural network, to make the technical trading strategies useful in practice. The results show that the moving average trading strategies outperforms the buy and hold strategy in one asset condition. For forecasting the results of an asset condition, an artificial neural network has a higher accuracy rate than logistic regression for predicting asset conditions.

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Published
2019-12-16