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Frontiers of Computer Science

, Volume 8, Issue 4, pp 596–608 | Cite as

An intelligent market making strategy in algorithmic trading

  • Xiaodong Li
  • Xiaotie Deng
  • Shanfeng Zhu
  • Feng WangEmail author
  • Haoran Xie
Research Article

Abstract

Market making (MM) strategies have played an important role in the electronic stock market. However, the MM strategies without any forecasting power are not safe while trading. In this paper, we design and implement a twotier framework, which includes a trading signal generator based on a supervised learning approach and an event-driven MM strategy. The proposed generator incorporates the information within order book microstructure and market news to provide directional predictions. The MM strategy in the second tier trades on the signals and prevents itself from profit loss led by market trending. Using half a year price tick data from Tokyo Stock Exchange (TSE) and Shanghai Stock Exchange (SSE), and corresponding Thomson Reuters news of the same time period, we conduct the back-testing and simulation on an industrial near-to-reality simulator. From the empirical results, we find that 1) strategies with signals perform better than strategies without any signal in terms of average daily profit and loss (PnL) and sharpe ratio (SR), and 2) correct predictions do help MM strategies readjust their quoting along with market trending, which avoids the strategies triggering stop loss procedure that further realizes the paper loss.

Keywords

algorithmic trading market making strategy order book microstructure news impact analysis market simulation 

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References

  1. 1.
    Radcliffe R. Investment: Concepts, Analysis, Strategy. Boston: Addison-Wesley, 1997Google Scholar
  2. 2.
    Brahma A, Chakraborty M, Das S, Lavoie A, Magdon-Ismail M. A bayesian market maker. In: Proceedings of the 13th ACM Conference on Electronic Commerce. 2012, 215–232CrossRefGoogle Scholar
  3. 3.
    O’hara M. Market Microstructure Theory. Cambridge, Mass.: Blackwell Publishers, 1995Google Scholar
  4. 4.
    Othman A, Sandholm T. Automated market-making in the large: the gates hillman prediction market. In: Proceedings of the 11th ACM Conference on Electronic Commerce. 2010, 367–376Google Scholar
  5. 5.
    Othman A, Sandholm T, Pennock D, Reeves D. A practical liquiditysensitive automated market maker. In: Proceedings of the 11th ACM Conference on Electronic Commerce. 2010, 377–386Google Scholar
  6. 6.
    Das S, Magdon-Ismail M. Adapting to a market shock: optimal sequential market-making. Advances in Neural Information Processing Systems, 2008, 361–368Google Scholar
  7. 7.
    Chakraborty T, Kearns M. Market making and mean reversion. In: Proceedings of the 12th ACM Conference on Electronic Commerce. 2011, 307–314CrossRefGoogle Scholar
  8. 8.
    Kim K. Financial time series forecasting using support vector machines. Neurocomputing, 2003, 55(1-2): 307–319CrossRefGoogle Scholar
  9. 9.
    Cao L, Tay F. Financial forecasting using support vector machines. Neural Computing&Applications, 2001, 10(2): 184–192CrossRefzbMATHGoogle Scholar
  10. 10.
    Cao L. Support vector machines experts for time series forecasting. Neurocomputing, 2003, 51: 321–339CrossRefGoogle Scholar
  11. 11.
    Huang W, Nakamori Y, Wang S. Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 2005, 32(10): 2513–2522CrossRefzbMATHGoogle Scholar
  12. 12.
    Fung G, Yu J, Lu H. The predicting power of textual information on financial markets. IEEE Intelligent Informatics Bulletin, 2005, 5(1): 1–10Google Scholar
  13. 13.
    Schumaker R, Chen H. Textual analysis of stock market prediction using financial news articles. In: Proceedings of the 12th Americas Conference on Information Systems. 2006, 185Google Scholar
  14. 14.
    Schumaker R, Chen H. A quantitative stock prediction system based on financial news. Information Processing & Management, 2009, 45(5): 571–583CrossRefGoogle Scholar
  15. 15.
    Schumaker R, Chen H. Textual analysis of stock market prediction using breaking financial news: the AZFin text system. ACM Transactions on Information Systems, 2009, 27(2): 12CrossRefGoogle Scholar
  16. 16.
    Schumaker R, Chen H. A discrete stock price prediction engine based on financial news. Computer, 2010, 43(1): 51–56CrossRefGoogle Scholar
  17. 17.
    Li X, Wang C, Dong J, Wang F, Deng X, Zhu S. Improving stock market prediction by integrating both market news and stock prices. In: Hameurlain A, Küng J, Wagner R, Liddle S W, Schewe K D, Zhou X, eds. Database and Expert Systems Applications. Berlin: Springer, 2011, 279–293CrossRefGoogle Scholar
  18. 18.
    Chen N, Deng X, Zhang J. How profitable are strategic behaviors in a market? In: Demetrescu D, Halldórsson MM eds. Algorithms-European Symposium on Algorithms. Berlin: Springer, 2011, 106–118Google Scholar
  19. 19.
    Abernethy J, Chen Y, Vaughan J. An optimization-based framework for automated market-making. In: Proceedings of the 12th ACM Conference on Electronic Commerce. 2011, 297–306CrossRefGoogle Scholar
  20. 20.
    Bu T M, Deng X, Qi Q. Arbitrage opportunities across sponsored search markets. Theoretical Computer Science, 2008, 407(1): 182–191CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    Bu T M, Deng X, Lin Q, Qi Q. Strategies in dynamic pari-mutual markets. In: Papadimitriou C, Zhang S, eds. Internet and Network Economics. Berlin: Springer, 2008, 138–153CrossRefGoogle Scholar
  22. 22.
    Salton G, McGill M J. Introduction to Modern Information Retrieval. New York: McGraw-Hill Inc., 1986Google Scholar
  23. 23.
    Li X, Wang R, Cao J, Xie H. Empirical analysis: stock market prediction via extreme learning machine. In: Proceedings of the 2013 International Conference on Extreme Learning Machines. 2013, 1–12Google Scholar
  24. 24.
    Easley D, Prado L. dMM, O’Hara M. Themicrostructure of the “flash crash”: flow toxicity, liquidity crashes, and the probability of informed trading. The Journal of Portfolio Management, 2011, 37(2): 118–128CrossRefGoogle Scholar
  25. 25.
    Abad D, Yagüe J. From pin to vpin: An introduction to order flow toxicity. The Spanish Review of Financial Economics, 2012, 10(2): 74–83CrossRefGoogle Scholar
  26. 26.
    Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. San Francisco: Morgan kaufmann, 2000Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Xiaodong Li
    • 1
  • Xiaotie Deng
    • 2
    • 3
  • Shanfeng Zhu
    • 3
    • 4
  • Feng Wang
    • 5
    Email author
  • Haoran Xie
    • 6
  1. 1.Department of Computer ScienceCity University of Hong KongHong KongChina
  2. 2.AIMS Lab, Department of Computer Science and EngineeringShanghai Jiaotong UniversityShanghaiChina
  3. 3.Shanghai Key Lab of Intelligent Information ProcessingFudan UniversityShanghaiChina
  4. 4.School of Computer ScienceFudan UniversityShanghaiChina
  5. 5.State Key Lab of Software Engineering, School of Computer ScienceWuhan UniversityWuhanChina
  6. 6.Department of Computer ScienceHong Kong Baptist UniversityHong KongChina

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