scholarly journals Trading Performance Analysis: A Comparisons Between the Original MA Crossover and Modified MA Crossover Strategy

2018 ◽  
pp. 933-941
Author(s):  
Afiruddin Tapa ◽  
Mohd Hasimi Yaacob ◽  
Ahmad Husni Hamzah ◽  
Yean Soh Chuen
Keyword(s):  
Performance Analysis ◽  
Moving Average ◽  
Technical Analysis ◽  
Lower Number ◽  
Analysis Approach ◽  
Trading Rule ◽  
Trading Rules ◽  
Stop Loss

This paper empirically analyses the Trading Performance by using technical analysis approach. The original moving-average (MA) crossover strategy as compare with the modified moving-average crossover strategy. The modified trading rules are the rules that been established to trading rules such as entry rule, exit rule, holding rule, and stop-loss rule. The results show The MAshort of 10-period for modified strategy underperform the original strategy, except for MA (10,100). The modified MA (20,200), (50,100), (50,200), and (100,200) underperform the original strategy. Only modified MA (20,50) and (20,100) outperform the original strategy. The outperformance and underperformance due to the stricter additional trading rule that reduces trading signals, and thus lower number of trades.

scholarly journals A Combined Signal Approach To Technical Analysis On The S&P 500

2011 ◽  
Vol 6 (8) ◽  
Author(s):  
Camillo Lento
Keyword(s):  
Transaction Costs ◽  
Moving Average ◽  
Statistical Significance ◽  
Trading Rule ◽  
Font Size ◽  
Technical Trading Rules ◽  
Period Analysis ◽  
Trading Rules ◽  
Signal Approach ◽  
The Individual

<p class="MsoNormal" style="line-height: normal; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">This paper examines the effectiveness of nine technical trading rules on the S&amp;P 500 from January 1950 to March 2008 (14,646 daily observations).<span style="mso-spacerun: yes;">&nbsp; </span>The annualized returns from each trading rule are compared to a na&iuml;ve buy-and-hold strategy to determine profitability. Over the 59 year period, only the moving-average cross-over (1,200) and (5,150) trading rules were able to outperform the buy-and-hold trading strategy after adjusting for transaction costs. However, excess returns were generated by employing a Combined Signal Approach (CSA) on the individual trading rules. Statistical significance was confirmed through bootstrap simulations and robustness through sub-period analysis.<span style="mso-spacerun: yes;">&nbsp; </span></span></span></p>

scholarly journals Moving Average Convergence-Divergence (MACD) Trading Rule: An Application in Nepalese Stock Market "NEPSE"

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Rashesh Vaidya
Keyword(s):  
Stock Market ◽  
Intrinsic Value ◽  
Moving Average ◽  
Technical Analysis ◽  
Future Trend ◽  
Trading Rule ◽  
Minimal Period ◽  
Current Pattern ◽  
Signal Line ◽  
Market Convergence

There are two types of analysis done for a stock market. One is fundamental analysis, where an investor looks at an intrinsic value of the stock, and another is technical analysis, where investors determine the future trend of the market looking at the current pattern or trend of the market. This paper is focused on one of the technical analysis tools, i.e., Moving Average Convergence-Divergence. It is a tool based on the three exponential moving average (9-12-26 EMA Rule). The MACD analysis, with the help of a single line, was helpful to find out the exact bullish and the bearish trend of the Nepse. A signal line is a benchmark to determine the stock market moving either to a bullish or bearish trend. It can help an investor, where the market is going in a direction. A market convergence, divergence, and crossover were better identified with the help of the MACD histogram. The paper found that the Nepse return was stable for a very minimal period from 1998-99 to 2019-20. The shift from the bullish to bearish or vice-verse were seen easily identified with the help of a MACD histogram. Finally, a better-combined knowledge of moving average and candlestick chart analysis will help an investor, to put a clear picture of a market trend with the help of MACD analysis.

MOMENTUM OSCILLATORS - A TOOL TO PREDICT STOCK PRICE BEHAVIOUR

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
Shishir Kumar Gujrati
Keyword(s):  
Stock Price ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Future Price ◽  
Range Data ◽  
Strength Index ◽  
Price Trends ◽  
Past Data ◽  
Price Movements

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.

scholarly journals Improving MACD Technical Analysis by Optimizing Parameters and Modifying Trading Rules: Evidence from the Japanese Nikkei 225 Futures Market

2021 ◽  
Vol 14 (1) ◽  
pp. 37
Author(s):  
Byung-Kook Kang
Keyword(s):  
Moving Average ◽  
Public Information ◽  
Technical Analysis ◽  
Weak Form ◽  
Futures Market ◽  
Traditional Model ◽  
Analysis Tool ◽  
Futures Prices ◽  
Improved Performance ◽  
Parameter Values

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.

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