Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Ozbayoglu, 2020 Aliev, 2004, Soft computing and its applications in business and economics Dymowa, 2011 Kovalerchuk, 2000 2008 Bahrammirzaee, 2010, A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems, Neural Comput. Appl., 19, 1165, 10.1007/s00521-010-0362-z Zhang, 2004, Discovering golden nuggets: Data mining in financial application, IEEE Trans. Syst. Man Cybern. C, 34, 513, 10.1109/TSMCC.2004.829279 Mochón, 2007, Soft computing techniques applied to finance, Appl. Intell., 29, 111, 10.1007/s10489-007-0051-5 Mullainathan, 2017, Machine learning: An applied econometric approach, J. Econ. Perspect., 31, 87, 10.1257/jep.31.2.87 2002 Tapia, 2007, Applications of multi-objective evolutionary algorithms in economics and finance: A survey Ponsich, 2013, A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications, IEEE Trans. Evol. Comput., 17, 321, 10.1109/TEVC.2012.2196800 Aguilar-Rivera, 2015, Genetic algorithms and Darwinian approaches in financial applications: a survey, Expert Syst. Appl., 42, 7684, 10.1016/j.eswa.2015.06.001 Rada, 2008, Expert systems and evolutionary computing for financial investing: A review, Expert Syst. Appl., 34, 2232, 10.1016/j.eswa.2007.05.012 Li, 2010, Applications of artificial neural networks in financial economics: A survey Tkáč, 2016, Artificial neural networks in business: Two decades of research, Appl. Soft Comput., 38, 788, 10.1016/j.asoc.2015.09.040 Elmsili, 2018, Artificial neural networks applications in economics and management research: An exploratory literature review, 1 Mittermayer, 2006 Mitra, 2012, Applications of news analytics in finance: A review, 1 Nassirtoussi, 2014, Text mining for market prediction: A systematic review, Expert Syst. Appl., 41, 7653, 10.1016/j.eswa.2014.06.009 Kearney, 2014, Textual sentiment in finance: A survey of methods and models, Int. Rev. Financ. Anal., 33, 171, 10.1016/j.irfa.2014.02.006 Kumar, 2016, A survey of the applications of text mining in financial domain, Knowl.-Based Syst., 114, 128, 10.1016/j.knosys.2016.10.003 Xing, 2017, Natural language based financial forecasting: a survey, Artif. Intell. Rev., 50, 49, 10.1007/s10462-017-9588-9 Vanstone, 2003, A survey of the application of soft computing to investment and financial trading, 211 Hajizadeh, 2010 Nair, 2014, Artificial intelligence applications in financial forecasting – a survey and some empirical results, Intell. Decis. Technol., 9, 99, 10.3233/IDT-140211 Cavalcante, 2016, Computational intelligence and financial markets: A survey and future directions, Expert Syst. Appl., 55, 194, 10.1016/j.eswa.2016.02.006 Bjoern Krollner, Bruce J. Vanstone, Gavin R. Finnie, Financial time forecasting with machine learning techniques: a survey, in: ESANN, 2010. Yoo, 2005, Machine learning techniques and use of event information for stock market prediction: A survey and evaluation, 835 Preethi, 2012, Stock market forecasting techniques: A survey, J. Theor. Appl. Inf. Technol., 46, 24 Atsalakis, 2009, Surveying stock market forecasting techniques – Part II: Soft computing methods, Expert Syst. Appl., 36, 5932, 10.1016/j.eswa.2008.07.006 Chatterjee, 2000, Artificial neural network and the financial markets: A survey, Manag. Finance, 26, 32 Katarya, 2017, A survey of neural network techniques in market trend analysis, 873 Hu, 2015, Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review, Appl. Soft Comput., 36, 534, 10.1016/j.asoc.2015.07.008 Huang, 2004, Forecasting foreign exchange rates with artificial neural networks: A review, Int. J. Inf. Technol. Decis. Mak., 03, 145, 10.1142/S0219622004000969 Pradeepkumar, 2018, Soft computing hybrids for forex rate prediction: A comprehensive review, Comput. Oper. Res., 99, 262, 10.1016/j.cor.2018.05.020 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Schmidhuber, 2015, Deep learning in neural networks: An overview, Neural Netw., 61, 85, 10.1016/j.neunet.2014.09.003 Goodfellow, 2016 Cybenko, 1989, Approximation by superpositions of a sigmoidal function, Math. Control Signals Systems, 2, 303, 10.1007/BF02551274 Kalman, 1992, Why tanh: choosing a sigmoidal function, 578 Vinod Nair, Geoffrey E. Hinton, Rectified linear units improve restricted Boltzmann machines, in: Proceedings of the 27th International Conference on Machine Learning, ICML-10, 2010, pp. 807–814. Maas, 2013, Rectifier nonlinearities improve neural network acoustic models, vol. 30, 3 Ramachandran, 2017 Deng, 2014, Deep learning: methods and applications, Found. Trends Signal Process., 7, 197, 10.1561/2000000039 Gardner, 1998, Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627, 10.1016/S1352-2310(97)00447-0 Robbins, 1951, A stochastic approximation method, Ann. Math. Stat., 400, 10.1214/aoms/1177729586 Ilya Sutskever, James Martens, George Dahl, Geoffrey Hinton, On the importance of initialization and momentum in deep learning, in: International Conference on Machine Learning, 2013, pp. 1139–1147. Duchi, 2011, Adaptive subgradient methods for online learning and stochastic optimization, J. Mach. Learn. Res., 12, 2121 Tieleman, 2012, Lecture 6.5-RMSProp: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural Netw. Mach. Learn., 4, 26 Kingma, 2014 Bengio, 1994, Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Netw., 5, 157, 10.1109/72.279181 Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1026–1034. Xavier Glorot, Yoshua Bengio, Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010, pp. 249–256. Bergstra, 2011, Algorithms for hyper-parameter optimization, 2546 Bergstra, 2012, Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281 Razvan Pascanu, Tomas Mikolov, Yoshua Bengio, On the difficulty of training recurrent neural networks, in: International Conference on Machine Learning, 2013, pp. 1310–1318. Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Wu, 2016 Greff, 2016, LSTM: A search space odyssey, IEEE Trans. Neural Netw. Learn. Syst., 28, 2222, 10.1109/TNNLS.2016.2582924 Reimers, 2017 Ji, 2012, 3D convolutional neural networks for human action recognition, IEEE Trans. Pattern Anal. Mach. Intell., 35, 221, 10.1109/TPAMI.2012.59 Szegedy, 2013, Deep neural networks for object detection, 2553 Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431–3440. Xueheng Qiu, Le Zhang, Ye Ren, P. Suganthan, Gehan Amaratunga, Ensemble deep learning for regression and time forecasting, in: 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning, CIEL, 2014, pp. 1–6. Hrasko, 2015, Time series prediction using restricted Boltzmann machines and backpropagation, Procedia Comput. Sci., 55, 990, 10.1016/j.procs.2015.07.104 Salakhutdinov, 2007, Restricted Boltzmann machines for collaborative filtering, 791 Yoshua Bengio, Deep learning of representations for unsupervised and transfer learning, in: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 2012, pp. 17–36. Abdel-rahman Mohamed, George Dahl, Geoffrey Hinton, Deep belief networks for phone recognition, in: Nips Workshop on Deep Learning for Speech Recognition and Related Applications, vol. 1, Vancouver, Canada, 2009, pp. 39. Lee, 2009, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, 609 Van Der Maaten, 2009, Learning a parametric embedding by preserving local structure, 384 Yao, 2016, Hyperparameters adaptation for restricted Boltzmann machines based on free energy, 243 Carreira-Perpinan, 2005, On contrastive divergence learning., 33 Tamilselvan, 2013, Failure diagnosis using deep belief learning based health state classification, Reliab. Eng. Syst. Saf., 115, 124, 10.1016/j.ress.2013.02.022 Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527 Meng, 2017, Relational autoencoder for feature extraction, 364 Vincent, 2008, Extracting and composing robust features with denoising autoencoders, 1096 Sutton, 1998 Nguyen-Tuong, 2011, Model learning for robot control: a survey, Cogn. Process., 12, 319, 10.1007/s10339-011-0404-1 Chong, 2017, Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies, Expert Syst. Appl., 83, 187, 10.1016/j.eswa.2017.04.030 Chen, 2015, A LSTM-based method for stock returns prediction: A case study of China stock market Dezsi, 2016, Can deep machine learning outsmart the market? A comparison between econometric modelling and long- short term memory, Rom. Econ. Bus. Rev., 11, 54 Samarawickrama, 2017, A recurrent neural network approach in predicting daily stock prices an application to the Sri Lankan stock market Hiransha, 2018, Nse stock market prediction using deep-learning models, Procedia Comput. Sci., 132, 1351, 10.1016/j.procs.2018.05.050 Selvin, 2017, Stock price prediction using LSTM, rnn and CNN-sliding window model Lee, 2018, Threshold-based portfolio: the role of the threshold and its applications, J. Supercomput. Li, 2017, Time series prediction of stock price using deep belief networks with intrinsic plasticity Chen, 2018, Which artificial intelligence algorithm better predicts the Chinese stock market?, IEEE Access, 6, 48625, 10.1109/ACCESS.2018.2859809 Krauss, 2017, Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S&P 500, European J. Oper. Res., 259, 689, 10.1016/j.ejor.2016.10.031 Chandra, 2016, Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance, Appl. Soft Comput., 49, 462, 10.1016/j.asoc.2016.08.029 Liu, 2017, CNN-LSTM Neural network model for quantitative strategy analysis in stock markets, 198 Heaton, 2016 Batres-Estrada, 2015 Yuan, 2018, Deep and wide neural networks on multiple sets of temporal data with correlation Zhang, 2017, Stock price prediction via discovering multi-frequency trading patterns Abe, 2018, Deep learning for forecasting stock returns in the cross-section, 273 Feng, 2018 Fan, 2014, Sufficient forecasting using factor models, SSRN Electron. J., 10.2139/ssrn.2607666 Kraus, 2017, Decision support from financial disclosures with deep neural networks and transfer learning, Decis. Support Syst., 104, 38, 10.1016/j.dss.2017.10.001 Minami, 2018, Predicting equity price with corporate action events using LSTM-RNN, J. Math. Finance, 08, 58, 10.4236/jmf.2018.81005 Zhang, 2018, Deep stock ranker: A LSTM neural network model for stock selection, 614 Zhuge, 2017 Akita, 2016, Deep learning for stock prediction using numerical and textual information Ozbayoglu, 2007, Neural based technical analysis in stock market forecasting, 261 Khare, 2017, Short term stock price prediction using deep learning Zhou, 2018, Stock market prediction on high-frequency data using generative adversarial nets, Math. Probl. Eng., 2018, 1 Singh, 2016, Stock prediction using deep learning, Multimedia Tools Appl., 76, 18569, 10.1007/s11042-016-4159-7 Karaoglu, 2017, A deep learning approach for optimization of systematic signal detection in financial trading systems with big data, Int. J. Intell. Syst. Appl. Eng., 31, 10.18201/ijisae.2017SpecialIssue31421 Zhou, 2018, Deep learning and the cross-section of stock returns: Neural networks combining price and fundamental information, SSRN Electron. J. and, 2017, Neural networks for financial market risk classification, Front. Signal Process., 1 . Google, System and Method for Computer Managed Funds to Outperform Benchmarks, US Patent, 2016. Tran, 2017, Tensor representation in high-frequency financial data for price change prediction Feng, 2018 Ding, 2015, Deep learning for event-driven stock prediction, 2327 Vargas, 2017, Deep learning for stock market prediction from financial news articles Lee, 2017, Predict stock price with financial news based on recurrent convolutional neural networks Iwasaki, 2018, Topic sentiment asset pricing with DNN supervised learning, SSRN Electron. J., 10.2139/ssrn.3228485 Das, 2018, Real-time sentiment analysis of Twitter streaming data for stock prediction, Procedia Comput. Sci., 132, 956, 10.1016/j.procs.2018.05.111 Li, 2017, Sentiment-aware stock market prediction: A deep learning method Zhongshengz, 2018 Nascimento, 2015, The impact of structured event embeddings on scalable stock forecasting models Han, 2018, An event-extraction approach for business analysis from online Chinese news, Electron. Commer. Res. Appl., 28, 244, 10.1016/j.elerap.2018.02.006 Bao, 2017, A deep learning framework for financial time series using stacked autoencoders and long-short term memory, PLoS One, 12, 10.1371/journal.pone.0180944 Parida, 2016, Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system for prediction of financial and energy market data, J. Finance Data Sci., 2, 202, 10.1016/j.jfds.2016.10.001 Fischer, 2018, Deep learning with long short-term memory networks for financial market predictions, European J. Oper. Res., 270, 654, 10.1016/j.ejor.2017.11.054 Widegren, 2017, Deep learning-based forecasting of financial assets Borovykh, 2018, Dilated convolutional neural networks for time series forecasting, J. Comput. Finance, 10.21314/JCF.2019.358 Althelaya, 2018, Evaluation of bidirectional LSTM for short-and long-term stock market prediction Dingli, 2017, Financial time series forecasting–A deep learning approach, Int. J. Mach. Learn. Comput., 7, 118, 10.18178/ijmlc.2017.7.5.632 Rout, 2017, Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach, J. King Saud Univ. Comput. Inf. Sci., 29, 536 Jeong, 2019, Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning, Expert Syst. Appl., 117, 125, 10.1016/j.eswa.2018.09.036 Baek, 2018, Modaugnet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module, Expert Syst. Appl., 113, 457, 10.1016/j.eswa.2018.07.019 Hansson, 2017 Elliot, 2017 Li, 2017, Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes Siami-Namini, 2018 Hsieh, 2011, Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm, Appl. Soft Comput., 11, 2510, 10.1016/j.asoc.2010.09.007 Zhang, 2015, Genetic deep neural networks using different activation functions for financial data mining Bekiros, 2013, Irrational fads, short-term memory emulation, and asset predictability, Rev. Financial Econ., 22, 213, 10.1016/j.rfe.2013.05.005 Pang, 2018, An innovative neural network approach for stock market prediction, J. Supercomput. Deng, 2017, Deep direct reinforcement learning for financial signal representation and trading, IEEE Trans. Neural Netw. Learn. Syst., 28, 653, 10.1109/TNNLS.2016.2522401 Yang, 2017, Stock market index prediction using deep neural network ensemble Lachiheb, 2018, A hierarchical deep neural network design for stock returns prediction, Procedia Comput. Sci., 126, 264, 10.1016/j.procs.2018.07.260 Yong, 2017, A stock market trading system using deep neural network, 356, 10.1007/978-981-10-6463-0_31 Yümlü, 2005, A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction, Pattern Recognit. Lett., 26, 2093, 10.1016/j.patrec.2005.03.026 Yan, 2017, Financial time series prediction based on deep learning, Wirel. Pers. Commun., 102, 683, 10.1007/s11277-017-5086-2 Takahashi, 2017, Long memory and predictability in financial markets Bildirici, 2010, TAR-cointegration neural network model: An empirical analysis of exchange rates and stock returns, Expert Syst. Appl., 37, 2, 10.1016/j.eswa.2009.07.077 Psaradellis, 2016, Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices, Int. J. Forecast., 32, 1268, 10.1016/j.ijforecast.2016.05.004 Chen, 2016 Mourelatos, 2018, Financial indices modelling and trading utilizing deep learning techniques: The ATHENS SE FTSE/ASE large cap use case Chen, 2018, Stock market embedding and prediction: A deep learning method Si, 2017, A multi-objective deep reinforcement learning approach for stock index future’s intraday trading Chen, 2018, Leveraging social media news to predict stock index movement using RNN-boost, Data Knowl. Eng., 10.1016/j.datak.2018.08.003 Dixon, 2016, Classification-based financial markets prediction using deep neural networks, SSRN Electron. J., 10.2139/ssrn.2756331 Lasheras, 2015, Forecasting the COMEX copper spot price by means of neural networks and ARIMA models, Resour. Policy, 45, 37, 10.1016/j.resourpol.2015.03.004 Zhao, 2017, A deep learning ensemble approach for crude oil price forecasting, Energy Econ., 66, 9, 10.1016/j.eneco.2017.05.023 Chen, 2017, Forecasting crude oil prices: a deep learning based model, Procedia Comput. Sci., 122, 300, 10.1016/j.procs.2017.11.373 Doering, 2017, Convolutional neural networks applied to high-frequency market microstructure forecasting Tino, 2001, Financial volatility trading using recurrent neural networks, IEEE Trans. Neural Netw., 12, 865, 10.1109/72.935096 Xiong, 2015 Zhou, 2018 Kim, 2018, Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models, Expert Syst. Appl., 103, 25, 10.1016/j.eswa.2018.03.002 Nikolaev, 2013, Time-dependent series variance learning with recurrent mixture density networks, Neurocomputing, 122, 501, 10.1016/j.neucom.2013.05.014 Harvey, 1989, Forecasts of economic growth from the bond and stock markets, Financ. Anal. J., 45, 38, 10.2469/faj.v45.n5.38 Bianchi, 2018, Bond risk premia with machine learning, SSRN Electron. J., 10.2139/ssrn.3232721 Warren, 2019 Zhang, 2014, A model with fuzzy granulation and deep belief networks for exchange rate forecasting Chao, 2011, Forecasting exchange rate with deep belief networks Zheng, 2017, Research on exchange rate forecasting based on deep belief network, Neural Comput. Appl. Shen, 2015, Forecasting exchange rate using deep belief networks and conjugate gradient method, Neurocomputing, 167, 243, 10.1016/j.neucom.2015.04.071 Hua Shen, Xun Liang, A time series forecasting model based on deep learning integrated algorithm with stacked autoencoders and SVR for FX prediction, in: ICANN, 2016. Sermpinis, 2012, Forecasting and trading the EUR/USD exchange rate with gene expression and PSI sigma neural networks, Expert Syst. Appl., 39, 8865, 10.1016/j.eswa.2012.02.022 Sermpinis, 2012, Forecasting and trading the EUR/USD exchange rate with stochastic neural network combination and time-varying leverage, Decis. Support Syst., 54, 316, 10.1016/j.dss.2012.05.039 Sermpinis, 2014, Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects, J. Int. Financ. Mark. Inst. Money, 30, 21, 10.1016/j.intfin.2014.01.006 Bo, 2009, RMB exchange rate forecasting in the context of the financial crisis, Syst. Eng. Theory Pract., 29, 53, 10.1016/S1874-8651(10)60090-7 Maknickienė, 2013, Financial market prediction system with evolino neural network and deplhi method, J. Bus. Econ. Manag., 14, 403, 10.3846/16111699.2012.729532 Maknickiene, 2014 Di Persio, 2016, Artificial neural networks approach to the forecast of stock market price movements, Int. J. Econ. Manag. Syst., 158 Korczak, 2017, Deep learning for financial time series forecasting in a-trader system Lopes, 2018 McNally, 2018, Predicting the price of bitcoin using machine learning Das, 2018, Are markets truly efficient? Experiments using deep learning algorithms for market movement prediction, Algorithms, 11, 138, 10.3390/a11090138 Navon, 2017 Saad, 1998, Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks, IEEE Trans. Neural Netw., 9, 1456, 10.1109/72.728395 Di Persio, 2017, Recurrent neural networks approach to the financial forecast of Google assets, Int. J. Math. Comput. Simul., 11, 713 Shen, 2018, Deep learning with gated recurrent unit networks for financial sequence predictions, Procedia Comput. Sci., 131, 895, 10.1016/j.procs.2018.04.298 Chen, 2016, Financial time-series data analysis using deep convolutional neural networks Sezer, 2019 Zhou, 2019, EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction, Expert Syst. Appl., 115, 136, 10.1016/j.eswa.2018.07.065 Ausmees, 2017 Raza, 2017, Prediction of stock market performance by using machine learning techniques Sezer, 2017, A deep neural-network based stock trading system based on evolutionary optimized technical analysis parameters, Procedia Comput. Sci., 114, 473, 10.1016/j.procs.2017.09.031 Liang, 2017, Restricted Boltzmann machine based stock market trend prediction Troiano, 2018, Replicating a trading strategy by means of LSTM for financial industry applications, IEEE Trans. Ind. Inf., 14, 3226, 10.1109/TII.2018.2811377 Nelson, 2017, Stock markets price movement prediction with LSTM neural networks Song, 2018 Gudelek, 2017, A deep learning based stock trading model with 2-D CNN trend detection Sezer, 2018, Algorithmic financial trading with deep convolutional neural networks: time series to image conversion approach, Appl. Soft Comput., 70, 525, 10.1016/j.asoc.2018.04.024 Gunduz, 2017, Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations, Knowl.-Based Syst., 137, 138, 10.1016/j.knosys.2017.09.023 Huang, 2016, Exploiting Twitter moods to boost financial trend prediction based on deep network models, 449 Peng, 2016, Leverage financial news to predict stock price movements using word embeddings and deep neural networks Huynh, 2017, A new model for stock price movements prediction using deep neural network Dang, 2018, Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network, IEEE Access, 1 Verma, 2017, Detecting, quantifying and accessing impact of news events on Indian stock indices dos Santos Pinheiro, 2017, Stock market prediction with deep learning: A character-based neural language model for event-based trading, 6 Prosky, 2017, Sentiment predictability for stocks, CoRR, abs/1712.05785 Liu, 2018, Stock price movement prediction from financial news with deep learning and knowledge graph embedding, 102 Yoshihara, 2014, Predicting stock market trends by recurrent deep neural networks, 759, 10.1007/978-3-319-13560-1_60 Shi, 2018, DeepClue: Visual interpretation of text-based deep stock prediction, IEEE Trans. Knowl. Data Eng., 1 Zhang, 2018, Improving stock market prediction via heterogeneous information fusion, Knowl.-Based Syst., 143, 236, 10.1016/j.knosys.2017.12.025 Hu, 2018, Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction, 261 Wang, 2018, Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles, Neurocomputing, 299, 51, 10.1016/j.neucom.2018.02.095 Matsubara, 2018, Stock price prediction by deep neural generative model of news articles, IEICE Trans. Inf. Syst., E101.D, 901, 10.1587/transinf.2016IIP0016 Li, 2018, Market impact analysis via deep learned architectures, Neural Comput. Appl. Tsantekidis, 2017, Using deep learning to detect price change indications in financial markets Sirignano, 2018, Universal features of price formation in financial markets: perspectives from deep learning, SSRN Electron. J., 10.2139/ssrn.3141294 Buczkowski, 2017, Predicting stock trends based on expert recommendations using gru/LSTM neural networks, 708, 10.1007/978-3-319-60438-1_69 Tsantekidis, 2017, Forecasting stock prices from the limit order book using convolutional neural networks Thomas Günter Fischer, 2019, Statistical arbitrage in cryptocurrency markets, J. Risk Financial Manag., 12 Gneiting, 2007, Strictly proper scoring rules, prediction, and estimation, J. Amer. Statist. Assoc., 102, 359, 10.1198/016214506000001437 Rothstein, 2005