Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
권호 표지
DOI QR코드

DOI QR Code

Comparative Study of Automatic Trading and Buy-and-Hold in the S&P 500 Index Using a Volatility Breakout Strategy

변동성 돌파 전략을 사용한 S&P 500 지수의 자동 거래와 매수 및 보유 비교 연구

  • Received : 2023.09.24
  • Accepted : 2023.11.23
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This research is a comparative analysis of the U.S. S&P 500 index using the volatility breakout strategy against the Buy and Hold approach. The volatility breakout strategy is a trading method that exploits price movements after periods of relative market stability or concentration. Specifically, it is observed that large price movements tend to occur more frequently after periods of low volatility. When a stock moves within a narrow price range for a while and then suddenly rises or falls, it is expected to continue moving in that direction. To capitalize on these movements, traders adopt the volatility breakout strategy. The 'k' value is used as a multiplier applied to a measure of recent market volatility. One method of measuring volatility is the Average True Range (ATR), which represents the difference between the highest and lowest prices of recent trading days. The 'k' value plays a crucial role for traders in setting their trade threshold. This study calculated the 'k' value at a general level and compared its returns with the Buy and Hold strategy, finding that algorithmic trading using the volatility breakout strategy achieved slightly higher returns. In the future, we plan to present simulation results for maximizing returns by determining the optimal 'k' value for automated trading of the S&P 500 index using artificial intelligence deep learning techniques.

본 연구는 미국 S&P 500 지수를 변동성 돌파 전략을 활용하여 Buy and Hold 방식과 비교 분석한 연구이다. 변동성 돌파 전략은 시장의 상대적 안정 또는 집중된 시기 후의 가격 움직임을 활용하는 거래 전략이다. 특히, 낮은 변동성 기간 후에 큰 가격 움직임이 더 자주 발생한다는 것이 관찰된다. 주식이 한동안 좁은 가격 범위에서 움직이다가 가격이 갑작스레 상승 또는 하락하는 경우, 그 주식이 해당 방향으로 계속 움직일 것으로 예상된다. 이러한 움직임을 활용하기 위해 거래자들은 변동성 돌파 전략을 채택한다. 'k' 값은 최근 시장 변동성의 측정값에 곱하는 배수로서 활용된다. 변동성의 측정 방법 중 하나로는 최근 거래일의 최고가와 최저가 차이를 나타내는 평균 진정 범위(ATR)가 있다. 'k' 값은 거래자들이 거래 임계값을 설정하는 데 중요한 역할을 한다. 본 연구는 'k' 값을 일반적인 값으로 연산하여 Buy and Hold 전략과 수익률을 비교 하여, 변동성 돌파전략을 사용한 알고리즘 트레이딩이 약간은 높은 수익률을 이룩하였다. 추후에는 인공 지능 딥러닝 기법을 이용하여 S&P 500 지수의 자동 거래를 위한 최적의 K 값을 구하고, 이를 통해 수익률을 극대화하기 위한 시뮬레이션 결과를 제시할 예정이다.

Keywords

Acknowledgement

This paper was written with the support of the research grant from Baekseok University for the academic year 2023.

References

  1. Deng, Wei, Wang, Yan, and Wang, Xiaolei, "The challenges of using a fixed K value in volatility breakout strategies", Expert Systems with Applications, Vol.115, pp.144-154, 2019.
  2. Aljawarneh, S., Aldwairi, M., & Yassein, M. B. (2018). Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, pp.152-160. https://doi.org/10.1016/j.jocs.2017.03.006
  3. Li, Y., & Wang, X. (2022). Optimal K value selection for William's oscillator in stock market prediction. Journal of Financial Research, 45(2), 239-262..
  4. Zhang, Yu, Wang, Xiaolei, and Wang, Yan, "A dynamic K value volatility breakout strategy for stock market forecasting", International Journal of Forecasting, Vol. 36, No. 3, pp.1013-1026, 2020.
  5. Corcoran, C. M. (2007). Long/short market dynamics: trading strategies for today's markets. John Wiley & Sons.
  6. Wu, Yu, Li, Jian, and Yang, Yi, "A new volatility breakout strategy with adaptive K value based on machine learning", Physica A: Statistical Mechanics and its Applications, Vol.577, pp.126173, 2022.
  7. Amihud, Y., & Mendelson, H. (1987). Trading mechanisms and stock returns: An empirical investigation. The Journal of Finance, 42(3), pp.533-553.
  8. N. Chande, The New Technical Trader: Tools and Strategies for Timing the Market, New Jersey: Chartwell Books, 1994.
  9. Ne'ma, N. H., & Mohammed, A. I. (2020). BUILD AN EFFICIENT INVESTMENT PORTFOLIO USING THE WILLIAM RATIO (EMPIRICAL STUDY) IN IRAQ STOCK EXCHANGE. iraq journal of market research and consumer protection, 12(1).
  10. Schwager, J. D., The New Market Wizards, HarperBusiness, New York, 1992.
  11. Nedeltcheva, G. N. (2015). Forecasting stock market trends. Economic Quality Control, 30(1), pp.21-38. https://doi.org/10.1515/eqc-2015-6003
  12. Achelis, S. B. (2000). Technical analysis from A to Z (2nd ed.). McGraw-Hill.
  13. Bulkowski, T. N. (2014). Encyclopedia of technical indicators (3rd ed.). McGraw-Hill.
  14. Colby, R. W., & Meyers, T. (2011). The technical analysis of stock trends (9th ed.). John Wiley & Sons.
  15. Williams, L. (2013). Technical analysis indicators: The definitive guide for traders and investors (2nd ed.). John Wiley & Sons.