×

zbMATH — the first resource for mathematics

Encoding of high-frequency order information and prediction of short-term stock price by deep learning. (English) Zbl 1420.91435
Summary: Predicting the price trends of stocks based on deep learning and high-frequency data has been studied intensively in recent years. Especially, the limit order book which describes supply-demand balance of a market is used as the feature of a neural network; however these methods do not utilize the properties of market orders. On the other hand, the order-encoding method of our prior work can take advantage of these properties. In this paper, we apply some types of convolutional neural network architectures to order-based features to predict the direction of mid-price trends. The results show that smoothing filters which we propose to employ rather than embedding features of orders improve accuracy. Furthermore, inspection of the embedding layer and investment simulation are conducted to demonstrate the practicality and effectiveness of our model.
MSC:
91G10 Portfolio theory
91-08 Computational methods for problems pertaining to game theory, economics, and finance
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Cont, R., Kukanov, A. and Stoikov, S., Price impact of order book events. J. Financ. Econ., 2014, 12, 47-48.
[2] Dixon, M., Polson, N. and Sokolov, V., Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading. Appl. Stoch. Models. Bus. Ind., 2017, 1-20.
[3] Dixon, M., Sequence classification of the limit order book using recurrent neural networks. J. Comput. Sci., 2018, 24, 277-286. doi: 10.1016/j.jocs.2017.08.018
[4] Eisler, Z., Bouchaud, J.P. and Kockelkoren, J., The price impact of order book events: Market orders, limit orders and cancellations. Quant. Finance, 2012, 12, 1395-1419. doi: 10.1080/14697688.2010.528444 · Zbl 1279.91072
[5] Fletcher, T. and Taylor, J.S., Multiple kernel learning with fisher kernels for high frequency currency prediction. Comput. Econ., 2015, 15, 1315-1329.
[6] Hearst, M., Dumais, S., Osuna, E., Platt, J. and Scholkopf, B., Support vector machines. IEEE Intell. Syst. Appl., 1998, 13, 18-28. doi: 10.1109/5254.708428
[7] Hochreiter, S. and Schmidhuber, J., Long short-term memory. Neural. Comput., 1997, 9, 1735-1780. doi: 10.1162/neco.1997.9.8.1735
[8] Johnson, R. and Zhang, T., Effective use of word order for text categorization with convolutional neural networks. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 103-112, 2015.
[9] Kercheval, A. and Zhang, Y., Modeling high-frequency limit order book dynamics with support vector machines. Quant. Finance, 2015, 15, 1315-1329. doi: 10.1080/14697688.2015.1032546 · Zbl 1406.91511
[10] Kim, Y., Convolutional neural networks for sentence classification. Empirical Methods Nat. Lang. Process., 2014, 1746-1751.
[11] Sirignano, J.A., Deep learning for limit order books. Quant. Finance, 2019, 19, 549-570. doi: 10.1080/14697688.2018.1546053
[12] Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. and Iosifidis, A., Using deep learning to detect price change indications in financial markets. Proceedings of the 25th European Signal Processing Conference, pp. 2511-2515, 2017.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.