Abstract
This work introduces how to use Limit Order Book Data (LOB) and transaction data for short-term forecasting of stock prices. LOB registers all trade intentions from market participants, as a result, it contains more market information that could enhance predictions. We will be using Deep Convolutional Neural Networks (CNN), which are good at pattern recognition on images. In order to accomplish the proposed task we will make an image-like representation of LOB and transaction data, which will feed up into the CNN, therefore it can recognize hidden patterns to classify Financial Time Series (FTS) in short-term periods. Data enclose information from 11 NYSE instruments, including stocks, ETF and ADR. We will present step by step methodology for encoding financial time series into an image-like representation. Results present an impressive performance, 74.15% in Directional Accuracy (DA).
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Notes
- 1.
Some considerations should be done, particularly related to the dimensionality of the FTS.
- 2.
Bivariate if volumes are included.
- 3.
For full details please refer to [18].
- 4.
We used other DL topologies.
- 5.
Data provided by DataDrivenMarket Corporation.
References
Arévalo, A., Nino, J., León, D., Hernandez, G., Sandoval, J.: Deep learning and wavelets for high-frequency price forecasting. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10861, pp. 385–399. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93701-4_29
Arévalo, A., Niño, J., Hernández, G., Sandoval, J.: High-frequency trading strategy based on deep neural networks. In: Huang, D.-S., Han, K., Hussain, A. (eds.) ICIC 2016. LNCS (LNAI), vol. 9773, pp. 424–436. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42297-8_40
Arnold, L., Rebecchi, S., Chevallier, S., Paugam-Moisy, H.: An introduction to deep learning. In: ESANN (2011). https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2011-4.pdf
Chao, J., Shen, F., Zhao, J.: Forecasting exchange rate with deep belief networks. In: The 2011 International Joint Conference on Neural Networks, pp. 1259–1266. IEEE, July 2011. http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6033368 ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6033368
Chen, M., et al.: Data, information, and knowledge in visualization. IEEE Comput. Graph. Appl. 29(1), 12–19 (2009)
Cont, R., Stoikov, S., Talreja, R.: A stochastic model for order book dynamics. Oper. Res. 58, 549–563 (2010)
De Goijer, J., Hyndman, R.: 25 years of time series forecasting. J. Forecast. 22, 443–473 (2006)
Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (ICJAI) (2015). http://ijcai.org/papers15/Papers/IJCAI15-329.pdf
Gould, M.D., Porter, M.A., Williams, S., McDonald, M., Fenn, D.J., Howison, S.D.: Limit order books. Quant. Finance 13, 42 (2010)
Hamid, S., Habib, A.: Financial forecasting with neura networks. Acad. Account. Financ. Stud. J. 18, 37–56 (2014)
Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)
Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognit. Lett. 42, 11–24 (2014). http://www.sciencedirect.com/science/article/pii/S0167865514000221
Niño-Peña, J.H., Hernández-Pérez, G.J.: Price direction prediction on high frequency data using deep belief networks. In: Figueroa-García, J.C., López-Santana, E.R., Ferro-Escobar, R. (eds.) WEA 2016. CCIS, vol. 657, pp. 74–83. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50880-1_7
Sandoval, J.: Empirical Shape Function of the Limit-Order Books of the USD/COP Spot Market. In: ODEON, no. 7 (2013). https://ssrn.com/abstract=2408087
Sandoval, J., Nino, J., Hernandez, G., Cruz, A.: Detecting informative patterns in financial market trends based on visual analysis. Procedia Comput. Sci. 80, 752–761 (2016). http://www.sciencedirect.com/science/article/pii/S1877050916308407. International Conference on Computational Science 2016, ICCS 2016, 6–8 June 2016, San Diego, California, USA
Shen, F., Chao, J., Zhao, J.: Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomput. 167(C), 243–253 (2015). https://doi.org/10.1016/j.neucom.2015.04.071
Takeuchi, L., Lee, Y.: Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks (2013)
Wang, Z., Oates, T.: Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks (2015). https://pdfs.semanticscholar.org/32e7/b2ddc781b571fa023c205753a803565543e7.pdf
Yeh, S., Wang, C., Tsai, M.: Corporate Default Prediction via Deep Learning (2014). http://teacher.utaipei.edu.tw/~cjwang/slides/ISF2014.pdf
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Niño, J., Arévalo, A., Leon, D., Hernandez, G., Sandoval, J. (2018). Price Prediction with CNN and Limit Order Book Data. In: Figueroa-García, J., López-Santana, E., Rodriguez-Molano, J. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-00350-0_11
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