According to rapid development in information technology, limit order books(LOB) mechanism has emerged to prevail in today's nancial market. In this paper, we propose ensemble machine learning architectures for capturing the dynamics of high-frequency limit order books such as predicting price spread crossing opportunities in a future time interval. The paper is more data-driven oriented, so experiments with ve real-time stock data from NASDAQ, measured by nanosecond, are established. The models are trained and validated by training and validation data sets. Compared with other models, such as logistic regression, support vector machine(SVM), our out-of-sample testing results has shown that ensemble methods had better performance on both statistical measurements and computational eciency. A simple trading strategy that we devised by our models has shown good prot and loss(P&L) results. Although this paper focuses on limit order books, the similar frameworks and processes can be extended to other classication research area. Keywords: limit order books, high-frequency trading, data analysis, ensemble methods, F1 score. / A Dissertation submitted to the Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2017. / July 18, 2017. / Includes bibliographical references. / Jinfeng Zhang, Professor Co-Directing Dissertation; Giray Okten, Professor Co-Directing Dissertation; Alec Kercheval, Committee Member; Washington Mio, Committee Member; Capstick Simon, University Representative.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_552146 |
| Contributors | Wang, Jian (authoraut), Zhang, Jinfeng (professor co-directing dissertation), Ökten, Giray (professor co-directing dissertation), Kercheval, Alec N. (committee member), Mio, Washington (committee member), Simon, Capstick C. (university representative), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Mathematics (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (108 pages), computer, application/pdf |
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