Optimizing Moving-Average Trading Strategy: Evidence in Malaysia Equity Market

Ahmad, Shahrul Nizam; Yean, Soh Chuen; Tapa, Afiruddin

doi:10.15405/epsbs.2016.08.111

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…Many academic researchers have studied moving averages relating to market performance (de Souza et al, 2018;Ishfaq Ahmad et al, 2017;Tapa et al, 2016). However, long-term data have been used.…”

Section: Figurementioning

confidence: 99%

“…The moving averages (MA) method is a well well-known method to traders as the indicator is precise and non-debatable, unlike the other 4 Source. TradingView Many academic researchers have studied moving averages relating to market performance (de Souza et al, 2018;Ishfaq Ahmad et al, 2017;Tapa et al, 2016). However, long-term data have been used.…”

Section: Moving Averagesmentioning

confidence: 99%

“…Each type has its own adherents, depending on each trader's risk appetite and trading style (Grinblatt et al, 2012;Ng & Wu, 2007;Ryu et al, 2017). According to Tapa et al, (2016), the results of their study show that the suggested modified crossover moving averages strategy must be accompanied with additional trading rules such as entry, exit, holding and stop-loss rules, in order to get the best return.…”

Section: Moving Averagesmentioning

confidence: 99%

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Analyzing the Double Crossover Moving Averages Strategy Before, During and After the Lockdown Period

Ghapar

2024

IJBF

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Trading is a business, not an investment. Traders focus on minor to secondary trends, while investors focus on the primary trend. Thetrading timeframe is crucial in technical analysis. As such a trader who trades with a minor trend timeframe, must have a trading strategy. We aim to examine the impact of the moving averages double crossover strategy on traders’ profit factor based on minor and secondary trend timeframes before, during and after the lockdown periods. Dow Theory was adopted for this study as a trend should be in effect until it gives a definite signal for a reversal. Data was collected from the Nasdaq Composite Index for a five-year period, from 2018 to 2022. During this time frame, the required data for the study was obtained at the point of the golden crossover and exiting at the dead crossover, in which 2018 to 2019 was the period before the lockdown, 2020 to 2021 was the period during lockdown, and 2022 was the year after lockdown. By using trading profits and loss for both longs and shorts, we back tested three strategies of double crossover moving averages that were as follows: i) EMA5 crossover EMA10, ii) EMA5 crossover EMA20 and iii) EMA20 crossover EMA50. A non-parametric analysis of independent samples was used. The result shows that there was a significant difference among those three strategies before the lockdown period, but no significant difference during and after the lockdown period.

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Section: Figurementioning

confidence: 99%

Section: Moving Averagesmentioning

confidence: 99%

Section: Moving Averagesmentioning

confidence: 99%

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Analyzing the Double Crossover Moving Averages Strategy Before, During and After the Lockdown Period

Ghapar

2024

IJBF

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“…They considered different turning points and calculated the retracement and projection ratios and concluded that Fibonacci retracement proved that the stock prices almost reversed around the Fibonacci ratios and Fibonacci trading could be very useful for traders in the African stock market Otake and Fallou (2013). Tapa et al (2016) examined the profitability of technical analysis using moving average crossover strategy compared with the simple buy and hold strategy on Malaysian equity market Tapa et al (2016). Gurrib (2016) investigated the use of an optimized moving average crossover strategy for the S&P 500 Gurrib (2016).…”

Section: Technical Indicatormentioning

confidence: 99%

Comparative Study of Hybrid Strategies With Moving Average Crossover

Panchal

Gor

2022

Int. J. Eng. Sci. Tech

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According to traders and investors, combined strategy gives better returns than individual strategy for the security analysis in the stock market. But traders and investors have a trend of first using one of oldest strategy; Moving Average Crossover (Crossover) because this strategy gives upcoming trend direction directly and easily. In this paper, we compare the return of hybrid strategy namely Hybrid Strategy of Donchian Channel and RSI (DCRSI), Hybrid Strategy of Parabolic SAR and EMA (PSAREMA) and Moving Fibonacci Strategy (MF) with the oldest strategy Crossover. Also, we check the profitability of these strategies in different market conditions and demonstrate on NSE data through R-Programming.

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“…Noteworthy, fund managers tend to use technical analysis as one of their analytical tools in managing the complex portfolio investments (Ahmad et al, 2017). Previous studies have also shown that certain technical indicators are capable in producing abnormal returns in the stock market (Tapa et al, 2016;Ling et al, 2020). However, the main drawback of the previous findings is that they are database specific and may not generalized to other stocks.…”

Section: Introductionmentioning

confidence: 99%

New Approach for E-Commerce Stock Prices Prediction: Combination of Machine Learning and Technical Analysis

Ming¹,

Jais²,

Soon³

2022

IJARAFMS

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Forecasting stock market is always a challenge task for the investors. This study aimed to develop a new approach for forecasting the price movements of e-commerce stocks. The signals emitted by the technical indicators are used as the features for two machine learning algorithms in predicting the stocks movements. The technical indicators used in this study were Moving Average (MA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Stochastic Oscillator (SO). Meanwhile, the machine learning algorithms used in this study were Random Forest (RF) and K-Neighbor Nearest (KNN). The findings of this study indicated that the inclusion of signals emitted by MA rule with 5-days short MA and 20-days long MA helps to reduce the error values for the prediction model. Besides that, this study also found that the signals from MA, MACD, RSI and SO fit the prediction model well. The investors are recommended to use machine learning algorithms to predict the price movements of e-commerce stocks. Lastly, investors are recommended to consider the signals from these four technical indicators, MA (5-days short MA & 20 long-MA), MACD, RSI and SO as the reference for their investment strategies in e-commerce stocks.

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Optimizing Moving-Average Trading Strategy: Evidence in Malaysia Equity Market

Cited by 3 publications

References 19 publications

Analyzing the Double Crossover Moving Averages Strategy Before, During and After the Lockdown Period

Analyzing the Double Crossover Moving Averages Strategy Before, During and After the Lockdown Period

Comparative Study of Hybrid Strategies With Moving Average Crossover

New Approach for E-Commerce Stock Prices Prediction: Combination of Machine Learning and Technical Analysis

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