Advanced Industrial SCIence (산업과 과학)

Convergence Society for SMB (중소기업융합학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Essential Concepts, Tools, Techniques and Methods of Stock Market Trading: A Guide to Traders and Investors

주식 거래의 필수 개념, 도구, 기법 및 방법에 관한 연구: 거래자와 투자자를 위한 안내서

  • Received : 2023.02.20
  • Accepted : 2023.03.20
  • Published : 2023.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

An attempt has been made in this article to discuss the fundamentals of technical analysis of the stock market. A retail investor or trader may not have the wherewithal to source that kind of information. Technical analysis requires a candlestick chart only. Most of the brokers in India provide charting solutions as well. Studying the price action of a security or commodity or Forex generally indicates a price pattern. Prices react at certain levels and widely known as support and resistance levels. Since whatever is happening with the price of the security is considered to be a part of a pattern or cycle which has already played out sometime in the past, these studies help a keen technical analyst to identify with certain probability, the future movement of the price. Study of the candlestick patterns, price action, volumes and indicators offer the opportunities to identify a high probability trade with probable target and a stop loss. A trader or investor can take high probability trade or position and control only her losses.

본 논문에서는 주식 시장의 기술적 분석의 기본에 대해 제시하였다. 소매 투자자나 거래자는 다양한 정보원으로부터 나오는 외부 정보를 얻을 수 있는 수단이 제한적이다. 일반적으로 기술적 분석에는 캔들 차트가 주로 활용된다. 인도의 대부분의 브로커는 차트 솔루션도 제공하고 있다. 보안이나 원자재 또는 Forex의 가격 변동을 분석해 보면 일반적인 주가 변동 패턴을 예측 할 수 있다. 주가는 특정 수준에서 반영되며 지지 및 저항 수준으로 널리 알려져 있다. 유가 증권의 가격에 발생하는 모든 일이 과거 언젠가 이미 진행된 패턴 또는 주기의 일부로 간주되기 때문에 이러한 연구는 영리한 애널리스트가 특정 확률로 가격의 미래 변동을 예측하는 데 도움을 줄 수 있다. 캔들스틱의 패턴, 가격 변동, 거래량 및 지표에 대한 연구는 가능한 목표 및 손절매로 높은 확률의 거래를 할 수 있는 기회를 제공한다. 본 연구 결과를 활용하여 트레이더나 투자자는 확률이 높은 거래나 조건을 취하고 투자 손실을 통제할 수 있게 된다.

Keywords

References

  1. Nazario, R. T. F., e Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets. The Quarterly Review of Economics and Finance, 66, 115-126.
  2. Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. DOI : 10.1016/j.cosrev.2019.08.001
  3. Wang, J. L., & Chan, S. H. (2007). Stock market trading rule discovery using pattern recognition and technical analysis. Expert Systems with Applications, 33(2), 304-315. DOI : 10.1016/j.eswa.2006.05.002
  4. Yamaguchi, T. (1989). A technical analysis expert system in the stock market. Future Generation Computer Systems, 5(1), 21-27. DOI : 10.1016/0167-739X(89)90016-2
  5. Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A systematic review. Expert Systems with Applications, 156, 113464. DOI : 10.1016/j.eswa.2020.113464
  6. https://www.investopedia.com/articles/trading/05/011705.asp
  7. Rantapuska, E. (2008). Ex-dividend day trading: Who, how, and why?: Evidence from the Finnish market. Journal of Financial Economics, 88(2), 355-374. DOI : 10.1016/j.jfineco.2007.12.001
  8. https://www.thinkmarkets.com/en/learn-to-trade /intermediate/candlestick-trading-guide/
  9. Ni, Y., Cheng, Y., Huang, P., & Day, M. Y. (2018). Trading strategies in terms of continuous rising (falling) prices or continuous bullish (bearish) candlesticks emitted. Physica A: Statistical Mechanics and its Applications, 501, 188-204. DOI : 10.1016/j.physa.2018.02.038
  10. Marshall, B. R., Young, M. R., & Rose, L. C. (2006). Candlestick technical trading strategies: Can they create value for investors?. Journal of Banking & Finance, 30(8), 2303-2323. DOI : 10.1016/j.jbankfin.2005.08.001
  11. https://learn.bybit.com/candlestick/best-candlestick-patterns/
  12. Hu, W., Si, Y. W., Fong, S., & Lau, R. Y. K. (2019). A formal approach to candlestick pattern classification in financial time series. Applied Soft Computing, 84, 105700. DOI : 10.1016/j.asoc.2019.105700
  13. Moorad Choudhry. (2001). 63 - Technical Analysis. In Securities Institution Professional Reference Series, The Bond & Money Markets, Butterworth-Heinemann, P. 983-1025.
  14. https://www.fidelity.com/bin-public/060_www_fidelity_com/documents/learning-center/Idenitfying-Chart-Patterns.pdf
  15. Biggerstaff, L., Cicero, D., & Wintoki, M. B. (2020). Insider trading patterns. Journal of Corporate Finance, 64, 101654. DOI : 10.1016/j.jcorpfin.2020.101654
  16. Downs, E. (2000). The 7 Chart Patterns That Consistently Make Money. Marketplace Books, Inc.
  17. Ladley, D., Liu, G., & Rockey, J. (2020). Losing money on the margin. Journal of Economic Behavior & Organization, 172, 107-136. DOI : 10.1016/j.jebo.2020.01.027
  18. Hurst, J. M. (2000). The Profit Magic of Stock Transaction Timing. Englewood Cliffs, NJ: Prentice-Hall.
  19. Hirschey, M. (2003). 3 - Investment advice on the Internet. Tech Stock Valuation, Academic Press, Pages 51-80.
  20. Teall, J. L. (2023). Chapter 1 - Introduction to Securities Trading and Markets. Financial Trading and Investing (Third Edition), Academic Press, P. 1-34,
  21. Chalmers, J., Liu, Y. S., & Wang, Z. J. (2021). The difference a day makes: Timely disclosure and trading efficiency in the muni market. Journal of Financial Economics, 139(1), 313-335. DOI : 10.1016/j.jfineco.2020.07.015
  22. Ngene, G. M., & Mungai, A. N. (2022). Stock returns, trading volume, and volatility: The case of African stock markets. International Review of Financial Analysis, 82, 102176. DOI : 10.1016/j.irfa.2022.102176
  23. Abudy, M. M. (2020). Retail investors' trading and stock market liquidity. The North American Journal of Economics and Finance, 54, 101281. DOI : 10.1016/j.najef.2020.101281
  24. Baklaci, H. F., Aydogan, B., & Yelkenci, T. (2020). Impact of stock market trading on currency market volatility spillovers. Research in International Business and Finance, 52, 101182. DOI : 10.1016/j.ribaf.2020.101182
  25. Dai, Z., Zhu, H., & Kang, J. (2021). New technical indicators and stock returns predictability. International Review of Economics & Finance, 71, 127-142. DOI : 10.1016/j.iref.2020.09.006
  26. Ali, A., Ramakrishnan, S., Faisal, F., Sulimany, H. G. H., & Bazhair, A. H. (2022). Stock market resource curse: The moderating role of institutional quality. Resources Policy, 78, 102929. DOI : 10.1016/j.resourpol.2022.102929
  27. Farimani, S. A., Jahan, M. V., Fard, A. M., & Tabbakh, S. R. K. (2022). Investigating the informativeness of technical indicators and news sentiment in financial market price prediction. Knowledge-Based Systems, 247, 108742. DOI : 10.1016/j.knosys.2022.108742
  28. Wen, D., Liu, L., Wang, Y., & Zhang, Y. (2022). Forecasting crude oil market returns: Enhanced moving average technical indicators. Resources Policy, 76, 102570. DOI : 10.1016/j.resourpol.2022.102570
  29. Salisu, A. A., Demirer, R., & Gupta, R. (2022). Financial turbulence, systemic risk and the predictability of stock market volatility. Global Finance Journal, 52, 100699. DOI : 10.1016/j.gfj.2022.100699
  30. Ma, F., Lu, X., Liu, J., & Huang, D. (2022). Macroeconomic attention and stock market return predictability. Journal of International Financial Markets, Institutions and Money, 79, 101603. DOI :
  31. Ahmed, E. A. S., & Goyal, S. B. (2021). Impact of Technical Parameters for Short-and Long-term Analysis of Stock Behavior. Materials Today: Proceedings. DOI : 10.1016/j.matpr.2021.05.474
  32. Da Silva, P. P. (2022). Market efficiency and the capacity of stock prices to track a firm's future profitability. Borsa Istanbul Review, 22(3), 452-464. DOI : 10.1016/j.bir.2021.06.010
  33. Bustos, O., & Pomares-Quimbaya, A. (2020). Stock market movement forecast: A systematic review. Expert Systems with Applications, 156, 113464. DOI : 10.1016/j.eswa.2020.113464
  34. Salisu, A. A., Sikiru, A. A., & Vo, X. V. (2020). Pandemics and the emerging stock markets. Borsa Istanbul Review, 20, S40-S48. DOI : 10.1016/j.bir.2020.11.004
  35. Fang, J., Qin, Y., & Jacobsen, B. (2014). Technical market indicators: An overview. Journal of behavioral and experimental finance, 4, 25-56. DOI : 10.1016/j.jbef.2014.09.001
  36. Marshall, B. R., Young, M. R., & Rose, L. C. (2006). Candlestick technical trading strategies: Can they create value for investors?. Journal of Banking & Finance, 30(8), 2303-2323. DOI : 10.1016/j.jbankfin.2005.08.001
  37. Hu, W., Si, Y. W., Fong, S., & Lau, R. Y. K. (2019). A formal approach to candlestick pattern classification in financial time series. Applied Soft Computing, 84, 105700.
  38. Lan, Q., Zhang, D., & Xiong, L. (2011). Reversal pattern discovery in financial time series based on fuzzy candlestick lines. Systems Engineering Procedia, 2, 182-190. DOI : 10.1016/j.sepro.2011.10.021
  39. Lee, K. H. & Jo, G. S. (1999). Expert system for predicting stock market timing using a candlestick chart, Expert Systems with Applications, 16(4), 357-364, https://doi.org/10.1016/S0957-4174(99)00011-1
  40. Stewart, T. H. (1996). 7 - Point and figure and Japanese candlestick charts. How Charts Can Make You Money (Third Edition), Woodhead Publishing, P. 76-89,
  41. David R. Meyer. (2020). Financial Centers, International, Editor(s): Audrey Kobayashi, International Encyclopedia of Human Geography (Second Edition), Elsevier, 121-129.
  42. He, X. Z., & Zheng, M. (2010). Dynamics of moving average rules in a continuous-time financial market model. Journal of Economic Behavior & Organization, 76(3), 615-634. DOI : 10.1016/j.jebo.2010.08.005
  43. Ni, Y., Liao, Y. C., & Huang, P. (2015). MA trading rules, herding behaviors, and stock market overreaction. International Review of Economics & Finance, 39, 253-265. DOI : 10.1016/j.iref.2015.04.009
  44. London, M. D., Evans, A. K., & Turner, M. J. (2002). Why study financial time series?. In Fractal Geometry (pp. 68-113). Woodhead Publishing.
  45. Schmidhuber, C. (2021). Trends, reversion, and critical phenomena in financial markets. Physica A: Statistical Mechanics and its Applications, 566, 125642. DOI : 10.1016/j.physa.2020.125642
  46. Ming-Ming, L., & Siok-Hwa, L. (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. DOI : 10.1016/j.asieco.2005.12.001
  47. Otchere, I., & Abukari, K. (2020). Are super stock exchange mergers motivated by efficiency or market power gains?. Journal of International Financial Markets, Institutions and Money, 64, 101164. DOI : 10.1016/j.intfin.2019.101164
  48. Sahin, U., & Ozbayoglu, A. M. (2014). TN-RSI: Trend-normalized RSI indicator for stock trading systems with evolutionary computation. Procedia Computer Science, 36, 240-245. DOI : 10.1016/j.procs.2014.09.086
  49. Hsieh, C. H. (2022). Generalization of affine feedback stock trading results to include stop-loss orders. Automatica, 136, 110051. DOI : 10.1016/j.automatica.2021.110051
  50. Ales Kresta, Jiri Franek, Analysis of Moving Average Rules Applicability in Czech Stock Market, Procedia Economics and Finance, Volume 30, Pages 364-371, 2015. https://doi.org/10.1016/S2212-5671(15)01303-9
  51. Ma, Y., Yang, B., & Su, Y. (2021). Stock return predictability: Evidence from moving averages of trading volume. Pacific-Basin Finance Journal, 65, 101494. DOI : 10.1016/j.pacfin.2021.101494
  52. Zhang, X., Wang, Z., Hao, J., & Liu, J. (2022). Stock market entry timing and retail investors' disposition effect. International Review of Financial Analysis, 82, 102205. DOI : 10.1016/j.irfa.2022.102205
  53. Ma, F., Lu, X., Liu, J., & Huang, D. (2022). Macroeconomic attention and stock market return predictability. Journal of International Financial Markets, Institutions and Money, 79, 101603. DOI : 10.1016/j.intfin.2022.101603