On the Design of Profitable Index Based on the Mechanism of Random Tradings

  • Jia-Hao SyuEmail author
  • Mu-En Wu
  • Shin-Huah Lee
  • Jan-Ming Ho
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)


Designing profitable trading strategies is an issue of interest to many experts and scholars. There are thousands of strategies in financial markets, and almost all of them can be divided into two types: momentum-type and contrarian-type. However, there is no formal way to determine which type of strategies are suitable for each stock. This study proposes a method to quantify and classify the momentum-type and the contrarian-type stocks for investors, which makes the trading strategies more quantitative. Our approach uses the technique of random trading and the proposed profitable index to quantify the stock attributes. We take the constituted stocks of Taiwan’s 50 (TW50) as research objects. According to the experimental results, there are 8 stocks in TW50 that are suitable for contrarian-type trading strategies, and the others 42 stocks are suitable for momentum-type trading strategies. We also use simple momentum and contrarian strategies to evaluate the effectiveness of the proposed algorithms and index. The results show the positive correlation between the momentum-type (contrarian-type) profitable index and the trading performance, and the correlation coefficient achieves 77.3% (80.3%). In conclusion, the scale of momentum-type and contrarian-type profitability index actually represents the profitability and the attribute of the stock.


Profitable index Random trading Momentum Contrarian 


  1. 1.
    Bollerslev, T., Chou, R.Y., Kroner, K.F.: Arch modeling in finance: a review of the theory and empirical evidence. J. Econometrics 52(1–2), 5–59 (1992)CrossRefGoogle Scholar
  2. 2.
    Brown, B.M., et al.: Martingale central limit theorems. Ann. Math. Stat. 42(1), 59–66 (1971)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Danyliv, O., Bland, B., Argenson, A.: Random walk model from the point of view of algorithmic trading. arXiv preprint arXiv:1908.04333 (2019)
  4. 4.
    Holmberg, U., Lönnbark, C., Lundström, C.: Assessing the profitability of intraday opening range breakout strategies. Finan. Res. Lett. 10(1), 27–33 (2013)CrossRefGoogle Scholar
  5. 5.
    Hu, W., Liu, G., Zhang, W., Wu, T.: Study on random trading behavior, herd behavior and asset price volatility. In: 2016 Chinese Control and Decision Conference (CCDC), pp. 3157–3163. IEEE (2016)Google Scholar
  6. 6.
    Huang, Z., Martin, F.: Pairs trading strategies in a cointegration framework: back-tested on cfd and optimized by profit factor. Appl. Econ. 51(22), 2436–2452 (2019)CrossRefGoogle Scholar
  7. 7.
    Parambalath, G., Mahesh, E., Balasubramanian, P., Kumar, P.N.: Big data analytics: a trading strategy of NSE stocks using bollinger bands analysis. In: Balas, V.E., Sharma, N., Chakrabarti, A. (eds.) Data Management, Analytics and Innovation. AISC, vol. 839, pp. 143–154. Springer, Singapore (2019). Scholar
  8. 8.
    Syu, J.H., Wu, M.E., Lee, S.H., Ho, J.M.: Modified orb strategies with threshold adjusting on taiwan futures market. In: 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), pp. 1–7. IEEE (2019)Google Scholar
  9. 9.
    Tsai, Y.C., et al.: Assessing the profitability of timely opening range breakout on index futures markets. IEEE Access 7, 32061–32071 (2019)CrossRefGoogle Scholar
  10. 10.
    Wu, M.E., Wang, C.H., Chung, W.H.: Using trading mechanisms to investigate large futures data and their implications to market trends. Soft Comput. 21(11), 2821–2834 (2017)CrossRefGoogle Scholar
  11. 11.
    Yan, X.X., Zhang, Y.B., Lv, X.K., Li, Z.Y., et al.: Improvement and test of stock index futures trading model based on bollinger bands. Int. J. Econ. Finan. 9(1), 78–87 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of Information and Finance ManagementNational Taipei University of TechnologyTaipeiTaiwan
  3. 3.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

Personalised recommendations