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.
|Number of pages||13|
|Journal||Frontiers of Computer Science|
|Early online date||11 Apr 2014|
|Publication status||Published - Aug 2014|
Bibliographical noteThis work was supported by the National Natural Science Foundation of China (Grant Nos. 61173011, 61103125). Thanks for Charles River Advisors Ltd. who provide their commercial exchange simulator for research use. Xiaotie Deng is supported by the National Natural Science Foundation of China (Grant No. 61173011) and a 985 project of Shanghai Jiaotong University, China.
- algorithmic trading
- market making strategy
- market simulation
- news impact analysis
- order book microstructure