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Applying Technical Trading Rules to Beat Long-Term Investing: Evidence from Asian Markets

Abstract

We provide evidence that the use of technical trading rules provides traders the opportunity to generate profits from actively buying and selling individual stocks across Asian markets. We test the trading performance of three widely used technical trading strategies, the Arithmetic Moving Average, the Relative Strength Index, and the Stochastic Oscillator, as well as variations to each trading strategy. We compare the results of these trading rules to a long-term buy-and-hold strategy across 4822 stocks traded in 39 Asian countries. Our results, when applying a simple behavior intervention filter of only selling a position when a trade is profitable, show that these technical trading rules, on average, were able to outperform the buy-and-hold strategy for 66% of the stocks listed in our sample. Additionally, given any of the listed Asian stocks, we found that, on average, a trader could apply any technical trading strategy and have a greater than 50–50 chance of outperforming the buy-and hold strategy for that stock for 63% of all stocks.

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Notes

  1. 1.

    Throughout our methodology, we assume that traders can only act upon a change in signal. This avoids over-accumulating or over-borrowing on long or short positions. Similar restrictions hold for the Relative Strength Index and the Stochastic Oscillator techniques.

  2. 2.

    We also compare results from this mechanical approach by incorporating a simple behavioral filter to our trading rules, to provide human intervention before closing a trading position. We discuss below.

  3. 3.

    The price ranges for some stocks may not trigger any “buy” signal from a trading rule to begin trading. So, not all 31 trading rules can be counted. While possible for any trading rule, it is more common for those trading rules with stricter filters on less volatile stocks.

References

  1. Arnold, C. (1994). Reading between the (chart) lines. Futures, the Magazine of Commodities and Options, 23, 36–38

    Google Scholar 

  2. Ben-Zion, U., Klein, P., Shachmurove, Y., & Yagil, J. (2003). Efficiency differences between the S&P 100 and the Tel-Aviv 25 indices: A moving average comparison. International Journal of Business, 8, 267–284

    Google Scholar 

  3. Bessembinder, H., & Chan, K. (1995). The profitability of technical trading rules in the Asian stock markets. Pacific Basin Finance Journal, 3, 257–284

    Article  Google Scholar 

  4. Bessembinder, H., & Chan, K. (1998). Market efficiency and the returns to technical analysis. Financial Management, 27, 5–17

    Article  Google Scholar 

  5. Brock, W. A., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47, 1731–1764

    Article  Google Scholar 

  6. Colby, R. W. (2003). The encyclopedia of technical market indicators. (2nd ed.). McGraw-Hill

    Google Scholar 

  7. Elder, A. (1987). Using stochastics to catch early trends and reversals. Futures, the Magazine of Commodities and Options, 16, 68–72

    Google Scholar 

  8. Etzkhorn, M. (1995). Getting an indication. Futures, the Magazine of Commodities and Options, 24, 38–39

    Google Scholar 

  9. Fama, E. (1965). The behavior of stock market prices. Journal of Business, 38, 34–105

    Article  Google Scholar 

  10. Gencay, R. (1996). Non-linear predictions of security returns with moving average rules. Journal of Forecasting, 15, 165–174

    Article  Google Scholar 

  11. Kahn, Michael (2019). How investors can prosper with technical analysis. Kiplinger’s Personal Finance. https://www.kiplinger.com/article/investing/t052-c008-s001-how-investors-can-prosper-with-technical-analysis.html

  12. Khand, S., Anand, V., & Qureshi, M. N. (2020). The predictability and profitability of simple moving averages and trading range breakout rules in the Pakistan stock market. Review of Pacific Basin Financial Markets and Policies. https://doi.org/10.1142/S0219091520500010

    Article  Google Scholar 

  13. Kwon, K., & Kish, R. J. (2002). Technical trading strategies and return predictability: NYSE. Applied Financial Economics, 12, 639–653

    Article  Google Scholar 

  14. Lai, M.-M., & Lau, S.-H. (2006). The profitability of the simple moving averages and trading range breakout in the Asian stock markets. Journal of Asian Economics, 17(1), 144–170

    Article  Google Scholar 

  15. Mitchell, C. (2020). Trend trading: the four most common indicators. https://www.investopedia.com/articles/active-trading/041814/four-most-commonlyused-indicators-trend-trading.asp

  16. Nor, S. M., & Wickremasinghe, G. (2014). The profitability of MACD and RSI trading rules in the Australian stock market. Investment Management and Financial Innovation, 11(4), 194–199

    Google Scholar 

  17. Papathanasiou, S., & Samitas, A. (2010). Profits from technical trading rules: The case of Cyprus stock exchange. Journal of Money, Investment and Banking, 13, 35–43

    Google Scholar 

  18. Poterba, J. M., & Summers, L. (1988). Mean reversion in stock prices: Evidence and implications. Journal of Financial Economics, 22, 27–59

    Article  Google Scholar 

  19. Ratner, M., & Leal, R. P. C. (1999). Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. Journal of Banking and Finance, 23(12), 1887–1905

    Article  Google Scholar 

  20. Seiler, M. J. (2001). Optimizing technical trading strategies: Making the ludicrous lucrative. American Business Review, 19, 20–25

    Google Scholar 

  21. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. Journal of Finance, 40(3), 777–790

    Article  Google Scholar 

  22. Stein, J. (1989). What divergence indicates about real price value. Futures, the Magazine of Commodities and Options, 18, 32–34

    Google Scholar 

  23. Tharavanij, P., Siraprapasiri, V., & Rajchamaha, K. (2015). Performance of technical trading rules: evidence from Southeast Asian stock markets. Springerplus. https://doi.org/10.1186/s40064-015-1334-7

    Article  Google Scholar 

  24. Wilder, J.W. (1978) New concepts in technical trading systems. Trend Research

  25. Wong, W., Manzur, M., & Chew, B. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13, 543–551

    Article  Google Scholar 

  26. Yoon, F. (2021). A ‘Mind-Boggling’ Individual Investor Boom Stirs Up Markets in Asia. Wall Street Journal. March 4, 2021 https://www.wsj.com/articles/a-mind-boggling-individual-investor-boom-stirs-up-markets-in-asia-11614853804. Accessed April 10, 2021

  27. Yu, H., Nartea, G. V., Gan, C., & Yao, L. J. (2013). Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets. International Review of Economics and Finance, 25, 356–371

    Article  Google Scholar 

  28. Yu, T., and Chen, S-H (2004). Using genetic programming with lambda abstraction to find technical trading rules. Computing in Economics and Finance. Society for Computational Economics, https://EconPapers.repec.org/RePEc:sce:scecf4:200

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Correspondence to Thomas S. Coe.

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Coe, T.S., Laosethakul, K. Applying Technical Trading Rules to Beat Long-Term Investing: Evidence from Asian Markets. Asia-Pac Financ Markets (2021). https://doi.org/10.1007/s10690-021-09337-5

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Keywords

  • Asian Stock Markets
  • Behavioral Finance
  • Technical Trading
  • Weak-form Market Efficiency

JEL Classifications

  • G11
  • G14
  • G15
  • G41