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An Analyze on Effectiveness of Candlestick Reversal Patterns for Vietnamese Stock Market

Authors:
Le Van Thanh at VinAI Research
Le Van Thanh
  • VinAI Research
Bui Ngoc Anh at FPT University
Bui Ngoc Anh
Quy Ban Tran at FPT University
Quy Ban Tran
Hoa Duong Xuan at FPT University
Hoa Duong Xuan
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Abstract

The Japanese candlestick model is a common technical analysis used to understand the behavior and predict trend in the financial market. There are many studies have been conducted to assess the effectiveness of candle patterns in different markets. This paper describes an empirical research to examine the predictability and profitability of the candlestick reversal patterns analysis on Vietnamese stock market over the period from Jan 2, 2013 to May 15, 2018. The purpose is to determine whether candlesticks patterns can be used to achieve significant gains or to predict future price trends. We also apply the use of Pivot Mean-Average to indicate the reversal signals of the market. Our experiments illustrate the accuracy of the patterns as well as the profitability of each pattern measured along after the pattern occurred. Thereby, our result can contribute to the existing researches on technical analysis as predictors of the trend and profitability of analyzed stocks.
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... Among the literature that investigated the effectiveness of Japanese candlestick patterns on U.S. stock markets, many researchers found that only a small fraction of the candlestick patterns could provide correct direction predictions for market traders (Cohen, 2021;Lu et al., 2015;Lu & Shiu, 2016;Qiu & Liu, 2019). Apart from the investigation of stock markets in developed countries and areas (Naranjo and Santos, 2019;Xie et al., 2012), the profitability of candlestick patterns has also been examined in the stock markets of developing countries such as the Vietnamese stock market (Thanh et al., 2018), Thailand stock market (Tharavanij et al., 2017), Brazilian stock market (Prado et al., 2013), and Chinese stock market (Chen et al., 2016;Zhu et al., 2016). ...
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