MACD technical analysis: a refined macd indicator

Rigorous testing of the widely used MACD technical analysis indicator results in a surprisingly low success rate of 32.73% for the individually tested NASDAQ-100 stocks over a 10-year period. This study derives two methods, which address the shortcomings of the MACD indicator. The methods are tested out-of-sample to address data-snooping concerns, i.e. to reduce the chance of falsely rejecting the null-hypothesis of no predictability. One version of the second derived method, named MACDR2, results in a success-rate of 89.39%.

The performance of method MACDR2 is positively correlated to the volatility of the stock and can be enhanced with option trading. However, the risk-adjusted Sharpe ratio, which is highly sensitive to the implied volatility used in the Black-Merton model, shows mixed results. Shorter or longer exponential moving averages do not improve the success rate of the traditional MACD divergence indicator. Yet the success rate of method MACDR2 is slightly positively correlated to longer exponential moving averages.

Most versions of the method MACR2 outperform the benchmark of holding a riskless security, the Treasury bond and holding the underlying asset, the NASDAQ-100. Thus, this study provides evidence against the Random Walk Hypothesis. However, the results are weakened significantly, if transaction costs and maximum trading constraints are incorporated in the study.

Technical Analysis is a methodology that tries to forecast the prices of financial securities by observing the pattern that the security has followed in the past. There are numerous methods within Technical Analysis, which are principally independent from each other.

Over the last two decades, Technical Analysis has become a popular way to predict stock prices in trading practice. Many investors and traders are using methods of Technical Analysis to support their trading decisions. Technical Analysis has its main justification in the field of psychology, i.e. self-fulfilling prophecy: Due to the fact that many traders trade according to the rules of Technical Analysis, and computers programs give buy and sell signals based on that theory, the market is assumed to move according to the principles of Technical Analysis.

Due to its heuristic nature, Technical Analysis can hardly be proven mathematically. Consequently the proof has to be done empirically. It is quite surprising though that hardly any rigorous empirical testing of the methods of Technical Analysis has been done. Among the few who tested the MACD indicator are Brock, Lakonishok and LeBaron (1992), who tested several moving averages and found them useful in predicting stock prices. However, their benchmark was merely holding cash.

Seyoka (1991) tested the MACD indicator from 1989 to 1991 on the S&P 500. His results questioned the indicators’ usefulness. Sullivan, Timmermann and White (1999) found superior performance of moving averages for the Dow Jones Industrial Average for in-sample data. However, their results showed no evidence of outperformance for out-of- sample data.

The objective of the study is to investigate, whether a refined method of the MACD chart can outperform a benchmark of holding a riskless security as a Treasury bond or holding the underlying asset, i.e., individual stocks of the NASDAQ-100. Thus, this study is challenging the random walk hypothesis.

Rigorous testing of the widely used MACD indicator results in a surprisingly low success rate of 32.73% for the individually tested NASDAQ-100 stocks over a 10-year period. This study derives two methods, which address the shortcomings of the MACD indicator.

Macd

My experience has been that if you set RSI limits to 60/40 and then use it, it quite synchronises with the MACD though not exactly. In the graph you have given, you have taken only centralline crossovers as buy and sell signals but conventionaly the macd crossover can also be a buy signal though not as reliable as the central line crossover. These two also coincide with Martin pring's long term momentum indicator in Metastock.

I have found MACD to work wonderfully in trending markets but fail miserably in sideways market. Technique, I am working on is to first determine if the market is trending using Klinger OSc or Linear Regression Slope or VHF. If Market is trending use MACD else use Stochastic Osc. I use Metastock and I have seen in system test that MACD and Mov Avg average cross over fail in trending market. Have you worked with Guppy's MMA? I have jsut started exploring it. Results are impressive.

In his book on trading which has been discussed elsewhere in this forum Mr. Ashwani Gujral has written about the use of the ADX indicator to determine if the market is trending or not. In a trending market, ADX values rise from very low values say from below 15 to around 30 and more than that is a consolidation phase or a trading market. Hope this is of some use but I have not used it personally and would like to know if metastock has it.

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