Order book mid-price movement inference by CatBoost classifier from convolutional feature maps
Tài liệu tham khảo
Aldridge, 2009
Durbin, 2010
Angel, 2013, Fairness in financial markets: The case of high frequency trading, J. Bus. Ethics, 112, 585, 10.1007/s10551-012-1559-0
Kercheval, 2015, Modelling high-frequency limit order book dynamics with support vector machines, Quant. Finance, 15, 1315, 10.1080/14697688.2015.1032546
Widegren, 2017
Di Persio, 2016, Artificial neural networks approach to the forecast of stock market price movements, Int. J. Econ. Manag. Syst., 1, 158
Tsantekidis, 2017, Forecasting stock prices from the limit order book using convolutional neural networks, 7
Doering, 2017, Convolutional neural networks applied to high-frequency market microstructure forecasting, 31
Mäkinen, 2018
Zhang, 2019, DeepLOB: Deep convolutional neural networks for limit order books, IEEE Trans. Signal Process.
Tsantekidis, 2017, Using deep learning to detect price change indications in financial markets, 2511
Dixon, 2017, Sequence classification of the limit order book using recurrent neural networks, J. Comput. Sci.
Cao, 2013, A hybrid CNN-RF method for electron microscopy images segmentation, Tissue Eng. J. Biomim. Biomater. Tissue Eng., 18, 2
Nijhawan, 2018, A hybrid CNN+ random forest approach to delineate debris covered glaciers using deep features, J. Indian Soc. Remote Sens., 46, 981, 10.1007/s12524-018-0750-x
Alalshekmubarak, 2013, A novel approach combining recurrent neural network and support vector machines for time series classification, 42
Ntakaris, 2017
Tran, 2018, Temporal attention-augmented bilinear network for financial time-series data analysis, IEEE Trans. Neural Netw. Learn. Syst.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2818–2826.
Sirignano, 2019, Universal features of price formation in financial markets: perspectives from deep learning, Quant. Finance, 19, 1449, 10.1080/14697688.2019.1622295
Zhang, 2020, A hybrid model based on bidirectional long short-term memory neural network and catboost for short-term electricity spot price forecasting, J. Oper. Res. Soc., 1
S. Jhaveri, I. Khedkar, Y. Kantharia, S. Jaswal, Success prediction using Random Forest, CatBoost, XGBoost and AdaBoost for kickstarter campaigns, in: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), 2019, pp. 1170–1173.
Prokhorenkova, 2017
Dorogush, 2018
Lundberg, 2017, A unified approach to interpreting model predictions, 4765
Roth, 1988
Stathakis, 2009, How many hidden layers and nodes?, Int. J. Remote Sens., 30, 2133, 10.1080/01431160802549278
Duong, 2018, The effect of anonymity on price efficiency: Evidence from the removal of broker identities, Pac.-Basin Finance J., 51, 95, 10.1016/j.pacfin.2018.06.004