An intelligent market making strategy in algorithmic trading

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 two-tier 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

References

[1] Radcliffe R. Investment: Concepts, Analysis, Strategy. Boston: Addison-Wesley, 1997

[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, 215-232

[3] O’hara M. Market Microstructure Theory. Cambridge, Mass.: Blackwell Publishers, 1995

[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, 367-376

[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, 377-386

[6] Das S, Magdon-Ismail M. Adapting to a market shock: optimal sequential market-making. Advances in Neural Information Processing Systems, 2008, 361-368

[7] Chakraborty T, Kearns M. Market making and mean reversion. In: Proceedings of the 12th ACM Conference on Electronic Commerce. 2011, 307-314

[8] Kim K. Financial time series forecasting using support vector machines. Neurocomputing, 2003, 55(1-2): 307-319

[9] Cao L, Tay F. Financial forecasting using support vector machines. Neural Computing & Applications, 2001, 10(2): 184-192

[10] Cao L. Support vector machines experts for time series forecasting. Neurocomputing, 2003, 51: 321-339

[11] Huang W, Nakamori Y, Wang S. Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 2005, 32(10): 2513-2522

[12] Fung G, Yu J, Lu H. The predicting power of textual information on financial markets. IEEE Intelligent Informatics Bulletin, 2005, 5(1): 1-10

[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, 185

[14] Schumaker R, Chen H. A quantitative stock prediction system based on financial news. Information Processing & Management, 2009, 45(5): 571-583

[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): 12

[16] Schumaker R, Chen H. A discrete stock price prediction engine based on financial news. Computer, 2010, 43(1): 51-56

[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, 279-293

[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, 106-118

[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, 297-306

[20] Bu T M, Deng X, Qi Q. Arbitrage opportunities across sponsored search markets. Theoretical Computer Science, 2008, 407(1): 182-191

[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, 138-153

[22] Salton G, McGill M J. Introduction to Modern Information Retrieval. New York: McGraw-Hill Inc., 1986

[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, 1-12

[24] Easley D, Prado L. dMM, O_Hara M. The microstructure of the “flash crash”: flow toxicity, liquidity crashes, and the probability of informed trading. The Journal of Portfolio Manag<?Pub Caret?>ement, 2011, 37(2): 118-128

[25] Abad D, Yagüe J. From pin to vpin: An introduction to order flow toxicity. The Spanish Review of Financial Economics, 2012, 10(2): 74-83

[26] Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. San Francisco: Morgan kaufmann, 2000

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Higher Education Press and Springer-Verlag Berlin Heidelberg