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Revisiting the Performance of MACD and RSI Oscillators

Author

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  • Terence Tai-Leung Chong

    (Hong Kong Institute of Asia-Pacific Studies, Department of Economics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
    Department of International Economics and Trade, Nanjing University, Nanjing, Jiangsu 210093, China)

  • Wing-Kam Ng

    (Department of Economics, The Chinese University of Hong Kong, Hong Kong, China)

  • Venus Khim-Sen Liew

    (Faculty of Economics and Business, Universiti Malaysia Sarawak, Sarawak, Malaysia)

Abstract

Chong and Ng (2008) find that the Moving Average Convergence–Divergence ( MACD ) and Relative Strength Index ( RSI ) rules can generate excess return in the London Stock Exchange. This paper revisits the performance of the two trading rules in the stock markets of five other OECD countries. It is found that the MACD (12,26,0) and RSI (21,50) rules consistently generate significant abnormal returns in the Milan Comit General and the S&P/TSX Composite Index. In addition, the RSI (14,30/70) rule is also profitable in the Dow Jones Industrials Index. The results shed some light on investors’ belief in these two technical indicators in different developed markets.

Suggested Citation

  • Terence Tai-Leung Chong & Wing-Kam Ng & Venus Khim-Sen Liew, 2014. "Revisiting the Performance of MACD and RSI Oscillators," JRFM, MDPI, vol. 7(1), pages 1-12, February.
    • Handle: RePEc:gam:jjrfmx:v:7:y:2014:i:1:p:1-12:d:33440
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    References listed on IDEAS

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    3. Jie Zou & Jiashu Lou & Baohua Wang & Sixue Liu, 2022. "A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks," Papers 2212.02721, arXiv.org, revised Jul 2023.
    4. Rachid Guennouni Hassani & Alexis Gilles & Emmanuel Lassalle & Arthur Dénouveaux, 2020. "Predicting Stock Returns with Batched AROW," Working Papers hal-02496048, HAL.
    5. Byung-Kook Kang, 2021. "Improving MACD Technical Analysis by Optimizing Parameters and Modifying Trading Rules: Evidence from the Japanese Nikkei 225 Futures Market," JRFM, MDPI, vol. 14(1), pages 1-21, January.
    6. Taylan Kabbani & Ekrem Duman, 2022. "Deep Reinforcement Learning Approach for Trading Automation in The Stock Market," Papers 2208.07165, arXiv.org.
    7. Mohammed Bouasabah & Oshamah Ibrahim Khalaf, 2023. "A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(3), pages 1-13, September.
    8. Charl Maree & Christian W. Omlin, 2022. "Balancing Profit, Risk, and Sustainability for Portfolio Management," Papers 2207.02134, arXiv.org.
    9. Jun-Cheng Chen & Cong-Xiao Chen & Li-Juan Duan & Zhi Cai, 2022. "DDPG based on multi-scale strokes for financial time series trading strategy," Papers 2207.10071, arXiv.org.
    10. Rachid Guennouni Hassani & Alexis Gilles & Emmanuel Lassalle & Arthur D'enouveaux, 2020. "Predicting Stock Returns with Batched AROW," Papers 2003.03076, arXiv.org, revised Mar 2020.
    11. D. Th. Vezeris & C. J. Schinas & G. Papaschinopoulos, 2018. "Profitability Edge by Dynamic Back Testing Optimal Period Selection for Technical Parameters Optimization, in Trading Systems with Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 761-807, April.

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    More about this item

    Keywords

    relative strength index; trading rules; moving average convergence–divergence;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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