Fetching the paper…
Reading the bibliography…
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities.
Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE transactions on neural networks , vol. 5, no. 2, pp. 157–166, 1994
1994
Earlier work this paper cites.
Y. LeCun, Y. Bengio et al. , “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks , vol. 3361, no. 10, p. 1995, 1995
1995
Earlier work this paper cites.
M. O’Hara, Market microstructure theory . Blackwell Publishers Cambridge, MA, 1995, vol. 108
1995
Earlier work this paper cites.
S. J. Orfanidis, Introduction to signal processing . Prentice-Hall, Inc., 1995
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
A. Abraham, B. Nath, and P. K. Mahanti, “Hybrid intelligent systems for stock market analysis,” in International Conference on Computational Science . Springer, 2001, pp. 337–345
2001
Earlier work this paper cites.
L. Harris, Trading and exchanges: Market microstructure for practitioners . Oxford University Press, USA, 2003
2003
Earlier work this paper cites.
Q. Cao, K. B. Leggio, and M. J. Schniederjans, “A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market,” Computers Operations Research , vol. 32, no. 10, pp. 2499–2512, 2005
2005
Earlier work this paper cites.
E. Zivot and J. Wang, “Vector autoregressive models for multivariate time series,” Modeling Financial Time Series with S-PLUS® , pp. 385–429, 2006
2006
Earlier work this paper cites.
C. Carrie, “The new electronic trading regime of dark books, mashups and algorithmic trading,” Trading , vol. 2006, no. 1, pp. 14–20, 2006
2006
Earlier work this paper cites.
A. Ang and G. Bekaert, “Stock return predictability: Is it there?” The Review of Financial Studies , vol. 20, no. 3, pp. 651–707, 2006
2006
Earlier work this paper cites.
Y. Nevmyvaka, Y. Feng, and M. Kearns, “Reinforcement learning for optimized trade execution,” in Proceedings of the 23rd international conference on Machine learning . ACM, 2006, pp. 673–680
2006
Earlier work this paper cites.
B. Mandelbrot and R. L. Hudson, The Misbehavior of Markets: A fractal view of financial turbulence . Basic books, 2007
2007
Earlier work this paper cites.
C. A. Parlour and D. J. Seppi, “Limit order markets: A survey,” Handbook of financial intermediation and banking , vol. 5, pp. 63–95, 2008
2008
Earlier work this paper cites.
B. B. Mandelbrot, “How Fractals Can Explain What’s Wrong with Wall Street,” Scientific American , vol. 15, no. 9, p. 2008, 2008
2008
Earlier work this paper cites.
P. Bacchetta, E. Mertens, and E. Van Wincoop, “Predictability in financial markets: What do survey expectations tell us?” Journal of International Money and Finance , vol. 28, no. 3, pp. 406–426, 2009
2009
Earlier work this paper cites.
G. S. Atsalakis and K. P. Valavanis, “Surveying stock market forecasting techniques–Part II: Soft computing methods,” Expert Systems with Applications , vol. 36, no. 3, pp. 5932–5941, 2009
2009
Earlier work this paper cites.
C. Cao, O. Hansch, and X. Wang, “The information content of an open limit-order book,” Journal of futures markets , vol. 29, no. 1, pp. 16–41, 2009
2009
Earlier work this paper cites.
I. Rosu et al. , “Liquidity and information in order driven markets,” Tech. Rep., 2010
2010
Earlier work this paper cites.
J. Gatheral and R. C. Oomen, “Zero-intelligence realized variance estimation,” Finance and Stochastics , vol. 14, no. 2, pp. 249–283, 2010
2010
Earlier work this paper cites.
M. A. Ferreira and P. Santa-Clara, “Forecasting stock market returns: The sum of the parts is more than the whole,” Journal of Financial Economics , vol. 100, no. 3, pp. 514–537, 2011
2011
Earlier work this paper cites.
T. Hendershott, C. M. Jones, and A. J. Menkveld, “Does algorithmic trading improve liquidity?” The Journal of Finance , vol. 66, no. 1, pp. 1–33, 2011
2011
Earlier work this paper cites.
M. Avellaneda, J. Reed, and S. Stoikov, “Forecasting prices from Level-I quotes in the presence of hidden liquidity,” Algorithmic Finance , vol. 1, no. 1, pp. 35–43, 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Cited alongside, same era.
M. Sundermeyer, R. Schlüter, and H. Ney, “LSTM neural networks for language modeling,” in Thirteenth Annual Conference of the International Speech Communication Association , 2012
2012
Cited alongside, same era.
Y. Burlakov, M. Kamal, and M. Salvadore, “Optimal limit order execution in a simple model for market microstructure dynamics,” 2012
2012
Cited alongside, same era.
T. J. Moskowitz, Y. H. Ooi, and L. H. Pedersen, “Time series momentum,” Journal of financial economics , vol. 104, no. 2, pp. 228–250, 2012
2012
Cited alongside, same era.
M. D. Gould, M. A. Porter, S. Williams, M. McDonald, D. J. Fenn, and S. D. Howison, “Limit order books,” Quantitative Finance , vol. 13, no. 11, pp. 1709–1742, 2013
M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you?: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . ACM, 2016, pp. 1135–1144
2016
Later among the works it cites.
R. C. Cavalcante, R. C. Brasileiro, V. L. Souza, J. P. Nobrega, and A. L. Oliveira, “Computational intelligence and financial markets: A survey and future directions,” Expert Systems with Applications , vol. 55, pp. 194–211, 2016
2016
Later among the works it cites.
J.-F. Chen, W.-L. Chen, C.-P. Huang, S.-H. Huang, and A.-P. Chen, “Financial time-series data analysis using deep convolutional neural networks,” in Cloud Computing and Big Data (CCBD), 2016 7th International Conference on . IEEE, 2016, pp. 87–92
2016
Later among the works it cites.
L. Di Persio and O. Honchar, “Artificial neural networks architectures for stock price prediction: Comparisons and applications,” International Journal of Circuits, Systems and Signal Processing , vol. 10, pp. 403–413, 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
J. Agrawal, V. Chourasia, and A. Mittra, “State-of-the-art in stock prediction techniques,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering , vol. 2, no. 4, pp. 1360–1366, 2013
2013
Cited alongside, same era.
N. Wang and D.-Y. Yeung, “Learning a deep compact image representation for visual tracking,” in Advances in neural information processing systems , 2013, pp. 809–817
2013
Cited alongside, same era.
L. Harris, “Maker-taker pricing effects on market quotations,” USC Marshall School of Business Working Paper. Avalable at http://bschool. huji. ac. il/. upload/hujibusiness/Maker-taker. pdf , 2013
2013
Cited alongside, same era.
2013
Cited alongside, same era.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. icml , vol. 30, no. 1, 2013, p. 3
2013
Cited alongside, same era.
A. A. Ariyo, A. O. Adewumi, and C. K. Ayo, “Stock price prediction using the ARIMA model,” in Computer Modelling and Simulation (UKSim), 2014 UKSim-AMSS 16th International Conference on . IEEE, 2014, pp. 106–112
2014
Cited alongside, same era.
T. Bollerslev, J. Marrone, L. Xu, and H. Zhou, “Stock return predictability and variance risk premia: Statistical inference and international evidence,” Journal of Financial and Quantitative Analysis , vol. 49, no. 3, pp. 633–661, 2014
2014
Cited alongside, same era.
2016
Later among the works it cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016, http://www.deeplearningbook.org
2016
Later among the works it cites.
D. T. Tran, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Tensor representation in high-frequency financial data for price change prediction,” in Computational Intelligence (SSCI), 2017 IEEE Symposium Series on . IEEE, 2017, pp. 1–7
2017
Later among the works it cites.
D. T. Tran, M. Gabbouj, and A. Iosifidis, “Multilinear class-specific discriminant analysis,” Pattern Recognition Letters , vol. 100, pp. 131–136, 2017
2017
Later among the works it cites.
A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Forecasting stock prices from the limit order book using convolutional neural networks,” in Business Informatics (CBI), 2017 IEEE 19th Conference on , vol. 1. IEEE, 2017, pp. 7–12
2017
Later among the works it cites.
——, “Using deep learning to detect price change indications in financial markets,” in Signal Processing Conference (EUSIPCO), 2017 25th European . IEEE, 2017, pp. 2511–2515
2017
Later among the works it cites.
M. Dixon, D. Klabjan, and J. H. Bang, “Classification-based financial markets prediction using deep neural networks,” Algorithmic Finance , vol. 6, no. 3-4, pp. 67–77, 2017
2017
Later among the works it cites.
J. Doering, M. Fairbank, and S. Markose, “Convolutional neural networks applied to high-frequency market microstructure forecasting,” in Computer Science and Electronic Engineering (CEEC), 2017 . IEEE, 2017, pp. 31–36
2017
Later among the works it cites.
W. Bao, J. Yue, and Y. Rao, “A deep learning framework for financial time series using stacked autoencoders and long-short term memory,” PloS one , vol. 12, no. 7, p. e0180944, 2017
2017
Later among the works it cites.
S. Selvin, R. Vinayakumar, E. Gopalakrishnan, V. K. Menon, and K. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model,” in Advances in Computing, Communications and Informatics (ICACCI), 2017 International Conference on . IEEE, 2017, pp. 1643–1647
2017
Later among the works it cites.
D. M. Nelson, A. C. Pereira, and R. A. de Oliveira, “Stock market’s price movement prediction with LSTM neural networks,” in Neural Networks (IJCNN), 2017 International Joint Conference on . IEEE, 2017, pp. 1419–1426
2017
Later among the works it cites.
N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, and P. T. P. Tang, “On large-batch training for deep learning: Generalization gap and sharp minima,” in International Conference on Learning Representations , 2017
2017
Later among the works it cites.
A. Ntakaris, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods,” Journal of Forecasting , vol. 37, no. 8, pp. 852–866, 2018
2018
Closest in time.
2018
Closest in time.
N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Temporal bag-of-features learning for predicting mid price movements using high frequency limit order book data,” IEEE Transactions on Emerging Topics in Computational Intelligence , 2018
2018
Closest in time.
D. T. Tran, A. Iosifidis, J. Kanniainen, and M. Gabbouj, “Temporal attention-augmented bilinear network for financial time-series data analysis,” IEEE transactions on neural networks and learning systems , 2018
2018
Closest in time.
2018
Closest in time.
T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research , vol. 270, no. 2, pp. 654–669, 2018
2018
Closest in time.
M. Dixon, “Sequence classification of the limit order book using recurrent neural networks,” Journal of computational science , vol. 24, pp. 277–286, 2018
2018
Closest in time.
Z. Zhang, S. Zohren, and S. Roberts, “BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books,” in Third workshop on Bayesian Deep Learning (NeurIPS 2018) , 2018
2018
Closest in time.