Profitability of moving-average technical analysis over the firm life cycle: Evidence from Taiwan

2021 ◽  
pp. 101633
Author(s):  
Kuan-Hau Chen ◽  
Xuan-Qi Su ◽  
Li-Feng Lin ◽  
Yi-Cheng Shih
Author(s):  
Shishir Kumar Gujrati

Stock markets are always taken as the barometer of the economy. The price movement of their indices reflects every ups and downs of the economy. Although seem to be random, these price movements do follow a certain track which can be identified using appropriate tool over long range data. One such method is of Technical Analysis wherein future price trends are forecasted using past data. Momentum Oscillators are the important tools of technical analysis. The current paper aims to identify the previous price movements of sensex by using Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) tools and also aims to check whether these tools are appropriate in forecasting the price trends or not.


2021 ◽  
Vol 14 (1) ◽  
pp. 37
Author(s):  
Byung-Kook Kang

Much research has examined performance or market efficiency by using the moving average convergence divergence (MACD) technical analysis tool. However, most tests fail to verify efficiency with the traditional parameter settings of 12, 26, and 9 days. This study confirms that applying the traditional model to Japan’s Nikkei 225 futures prices produces negative performance over the period of 2011–2019. Yet, it also finds that the MACD tool can earn significant positive returns when it uses optimized parameter values. This suggests that the Japanese market is not weak-form efficient in the sense that futures prices do not reflect all public information. Hence, the three parameters values of the MACD tool should be optimized for each market and this should take precedence over finding other strategies to reduce false trade signals. This study also tests which models are able to improve profitability by applying additional criteria to avoid false trade signals. From simulations using 19,456 different MACD models, we find that the number of models with improved performance resulting from these strategies is far greater for models with optimized parameter values than for models with non-optimized values. This approach has not been discussed in the existing literature.


2020 ◽  
Vol 33 ◽  
pp. 101226 ◽  
Author(s):  
Debarati Bhattacharya ◽  
Chia-Wen Chang ◽  
Wei-Hsien Li

2020 ◽  
Vol 17 (4) ◽  
pp. 44-60
Author(s):  
Alberto Antonio Agudelo Aguirre ◽  
Ricardo Alfredo Rojas Medina ◽  
Néstor Darío Duque Méndez

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.


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