Algorithmic Daily Trading Based on Experts’ Recommendations

  • Andrzej RutaEmail author
  • Dymitr Ruta
  • Ling Cen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10352)


Trading financial products evolved from manual transactions, carried out on investors’ behalf by well informed market experts to automated software machines trading with millisecond latencies on continuous data feeds at computerised market exchanges. While high-frequency trading is dominated by the algorithmic robots, mid-frequency spectrum, around daily trading, seems left open for deep human intuition and complex knowledge acquired for years to make optimal trading decisions. Banks, brokerage houses and independent experts use these insights to make daily trading recommendations for individual and business customers. How good and reliable are they? This work explores the value of such expert recommendations for algorithmic trading utilising various state of the art machine learning models in the context of ISMIS 2017 Data Mining Competition. We point at highly unstable nature of market sentiments and generally poor individual expert performances that limit the utility of their recommendations for successful trading. However, upon a thorough investigation of different competitive classification models applied to sparse features derived from experts’ recommendations, we identified several successful trading strategies that showed top performance in ISMIS 2017 Competition and retrospectively analysed how to prevent such models from over-fitting.


Algorithmic trading Feature selection Classification Gradient boosting decision trees Sparse features K-nn 


  1. 1.
  2. 2.
    Kannan, K.S., Sekar, P.S., Sathik, M.M., Arumugam, P.: Financial stock market forecast using data mining techniques. In: International MultiConference of Engineers and Computer Scientists, vol. 1 (2010)Google Scholar
  3. 3.
    Li, H., Yang, Z.J., Li, T.L.: Algorithmic Trading Strategy Based on Massive Data Mining. Stanford University, Stanford (2014)Google Scholar
  4. 4.
    Shao, C.X., Zheng, Z.M.: Algorithmic trading using machine learning techniques: final report (2013)Google Scholar
  5. 5.
    Dai, Y., Zhang, Y.: Machine Learning in Stock Price Trend Forecasting. Stanford University, Stanford (2013)Google Scholar
  6. 6.
    Khaidem, L., Saha, S., Dey, S.R.: Predicting the direction of stock market prices using random forest. Appl. Math. Finan. (2016)Google Scholar
  7. 7.
    Giacomel, F., Galante, R., Pareira, A.: An algorithmic trading agent based on a neural network ensemble: a case of study in North American and Brazilian stock markets. In: International Conference on Web Intelligence and Intelligent Agent Technology (2015)Google Scholar
  8. 8.
    Boonpeng, S., Jeatrakul, P.: Decision support system for investing in stock market by using OAA-neural network. In: 8th International Conference on Advanced Computational Intelligence (2016)Google Scholar
  9. 9.
    Deng, L.: Three classes of deep learning architectures and their applications: a tutorial survey. APSIPA Trans. Sig. Inf. Process. (2012)Google Scholar
  10. 10.
    Takeuchi, L., Lee, Y.A.: Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks (2013)Google Scholar
  11. 11.
    Jegadeesh, N., Titman, S.: Returns to buying winners and selling losers: implications for stock market efficiency. J. Finan. 48(1), 65–91 (1993)CrossRefGoogle Scholar
  12. 12.
    Arévalo, A., Niño, J., Hernández, G., Sandoval, J.: High-frequency trading strategy based on deep neural networks. In: Huang, D.-S., Han, K., Hussain, A. (eds.) ICIC 2016. LNCS, vol. 9773, pp. 424–436. Springer, Cham (2016). doi: 10.1007/978-3-319-42297-8_40 CrossRefGoogle Scholar
  13. 13.
    Malkiel, B.G., Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finan. 25(2), 383–417 (1970)CrossRefGoogle Scholar
  14. 14.
    Schumakera, R.P., Hsinchun, C.: A quantitative stock prediction system based on financial news. Inf. Process. Manag. 45(5), 571–583 (2009)CrossRefGoogle Scholar
  15. 15.
    Koochakzadeh, N., Kianmehr, K., Sarraf, A., Alhajj, R.: Stock market investment advice: a social network approach. In: International Conference on Advances in Social Networks Analysis and Mining, pp. 71–78 (2012)Google Scholar
  16. 16.
    Zhu, J., Zou, H., Rosset, S., Hastie, T.: Multi-class adaboost. Stat. Interface 2, 349–360 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Friedman, J.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Jegadeesh, N., Kim, J., Krische, S.D., Lee, C.M.C.: Analyzing the analysts: when do recommendations add value? J. Finan. 59(3), 1083–1124 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ING Bank SlaskiKatowicePoland
  2. 2.Emirates ICT Innovation Center, EBTICKhalifa UniversityAbu DhabiUAE

Personalised recommendations