References
- Angel, J.J., Who gets price improvement on the NYSE. Working Paper, 1994.
- Ban, G.Y., Karoui, N.E. and Lim, A.E.B., Machine learning and portfolio optimization. Manage. Sci., March, 2018, 64(3), 1136–1154.
- Biais, B., Hillion, P. and Spatt, C., An empirical analysis of the limit order book and the order flow in the Paris bourse. J. Finance., December, 1995, 50(5), 1655–1689.
- Carr, P., Wu, L. and Zhang, Z., Using machine learning to predict realized variance. Working Paper, 2019.
- Cho, J.W. and Nelling, E., The probability of limit-order execution. Financ. Anal. J., September, 2000, 56(5), 28–33.
- Cont, R., Stoikov, S. and Talreja, R., A stochastic model for order book dynamics. Oper. Res., May–June, 2010, 58(3), 549–563.
- Dixon, M. and London, J., Financial forecasting with α-rnns: A time series modeling approach. Front. Appl. Math. Stat., February, 2021, 6, 551138.
- Dixon, M., Klabjan, D. and Bang, J.H., Classification-based financial markets prediction using deep neural networks. Working Paper, 2017.
- Heaton, J.B., Polson, N.G. and Witte, J.H., Deep learning in finance. Working Paper, 2016.
- Hollifield, B., Miller, R.A. and Sandas, P., Econometric analysis of limit-order executions. Rev. Econ. Stud., October, 2004, 71(4), 1027–1063.
- Lo, A.W., MacKinlay, A.C. and Zhang, J., Econometric models of limit-order executions. J. Financ. Econ., July, 2002, 65(1), 31–71.
- Moallemi, C.C. and Yuan, K., A model for queue position valuation in a limit order book. Working Paper, 2016.
- Moghar, A. and Hamiche, M., Stock market prediction using lstm recurrent neural network. Procedia. Comput. Sci., 2020, 170, 1168–1173.
- NASDAQ, Nasdaq totalview-itch 4.1, 2010. http://www.nasdaqtrader.com/content/technicalsupport/specifications/dataproducts/nqtv-itch-v4_1.pdf.
- Ntakaris, A., Magris, M., Kanniainen, J., Gabbouj, M. and Iosifidis, A., Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods. Working Paper, 2018.
- Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. and Iosifidis, A., Temporal bag-of-features learning for predicting mid price movements using high frequency limit order book data. IEEE Trans. Emerging Top. Comput. Intell., October, 2018, 4(6), 774–785.
- Petersen, M.A. and Fialkowski, D., Posted versus effective spreads: Good prices or bad quotes. J. Financ. Econ., June, 1994, 35(3), 269–292.
- Sirignano, J. and Cont, R., Universal features of price formation in financial markets: Perspectives from deep learning. Quant. Finance, July, 2019, 19(9), 1449–1459.
- Sirignano, J.A., Deep learning for limit order books. Quant. Finance, 2019, 19(4), 549–570.
- Toke, I.M., The order book as a queueing system: Average depth and influence of the size of limit orders. Quant. Finance, November, 2013, 15(5), 795–808.
- Tran, D.T., Magris, M., Kanniainen, J., Gabbouj, M. and Iosifidis, A., Tensor representation in high-frequency financial data for price change prediction. In IEEE Symposium Series on Computational Intelligence, pp. 1–7, November, 2017.
- Tran, D.T., Iosifidis, A., Kanniainen, J. and Gabbouj, M., Temporal attention-augmented bilinear network for financial time-series data analysis. IEEE. Trans. Neural. Netw. Learn. Syst., May, 2019, 30(5), 1407–1418.
- Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. and Iosifidis, A., Forecasting stock prices from the limit order book using convolutional neural networks. In IEEE Business Informatics (CBI), pp. 7–12, 2017.
- Xiao, S., Yan, J., Yang, X., Zha, H. and Chu, S., Modeling the intensity function of point process via recurrent neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31, 2017.
- Xiong, R., Nichols, E.P. and Shen, Y., Deep learning stock volatility with Google domestic trends. Working Paper, 2015.
- Zhang, Z., Zohren, S. and Roberts, S., Deeplob: Deep convolutional neural networks for limit order books. Working Paper, 2019.