References

Abdel-Hamid, Ossama, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn, and Dong Yu. 2014. "Convolutional Neural Networks for Speech Recognition." IEEE/ACM Transactions on Audio, Speech, and Language Processing 22 (10): 1533–45. https://doi.org/10.1109/TASLP.2014.2339736.

Alqahtani, Hamed, Manolya Kavakli-Thorne, and Gulshan Kumar. 2019. "Applications of Generative Adversarial Networks (GANs): An Updated Review." Archives of Computational Methods in Engineering, December. https://doi.org/10.1007/s11831-019-09388-y.

Ang, Andrew, Robert J Hodrick, Yuhang Xing, and Xiaoyan Zhang. 2006. "The Cross-Section of Volatility and Expected Returns." The Journal of Finance 61 (1): 259–99.

Ang, Andrew. 2014. Asset Management: A Systematic Approach to Factor Investing. 1 edition. Oxford: Oxford University Press.

Araci, Dogu. 2019. "FinBERT: Financial Sentiment Analysis with Pre-Trained Language Models." ArXiv:1908.10063 [Cs], August. http://arxiv.org/abs/1908.10063.

Arora, Saurabh, and Prashant Doshi. 2019. "A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress." ArXiv:1806.06877 [Cs, Stat], August. http://arxiv.org/abs/1806.06877.

Asness, Clifford S., Tobias J. Moskowitz, and Lasse Heje Pedersen. 2013. "Value and Momentum Everywhere." The Journal of Finance 68 (3): 929–85. https://www.jstor.org/stable/42002613.

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2016. "Neural Machine Translation by Jointly Learning to Align and Translate." ArXiv:1409.0473 [Cs, Stat], May. http://arxiv.org/abs/1409.0473.

Bailey, David H., Jonathan M. Borwein, and Marcos Lopez de Prado. 2016. The Probability of Backtest Overfitting. Journal of Computational Finance, September. https://www.risk.net/node/2471206.

Banz, Rolf W. 1981. "The Relationship between Return and Market Value of Common Stocks." Journal of Financial Economics 9 (1): 3–18.

Barberis, Nicholas, Andrei Shleifer, and Robert Vishny. 1998. "A Model of Investor Sentiment." Journal of Financial Economics 49 (3): 307–43.

Basu, Sanjoy, and others. 1981. "The Relationship between Earnings' Yield, Market Value and Return for NYSE Common Stocks: Further Evidence."

Bengio, Y., A. Courville, and P. Vincent. 2013. "Representation Learning: A Review and New Perspectives." IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1798–1828. https://doi.org/10.1109/TPAMI.2013.50.

Betancourt, Michael. 2018. "A Conceptual Introduction to Hamiltonian Monte Carlo." ArXiv:1701.02434 [Stat], July. http://arxiv.org/abs/1701.02434.

Bishop, Christopher. 2006. Pattern Recognition and Machine Learning. Information Science and Statistics. New York: Springer-Verlag. https://www.springer.com/gp/book/9780387310732.

Blei, David M., Andrew Y. Ng, and Michael I. Jordan. 2003. "Latent Dirichlet Allocation." Journal of Machine Learning Research 3 (Jan): 993–1022. http://jmlr.csail.mit.edu/papers/v3/blei03a.html.

Burges, Chris J. C. 2010. "Dimension Reduction: A Guided Tour." Foundations and Trends in Machine Learning, January. https://www.microsoft.com/en-us/research/publication/dimension-reduction-a-guided-tour-2/.

Byrd, David, Maria Hybinette, and Tucker Hybinette Balch. 2019. "ABIDES: Towards High-Fidelity Market Simulation for AI Research." ArXiv:1904.12066 [Cs], April. http://arxiv.org/abs/1904.12066.

Casella, George, and Edward I. George. 1992. "Explaining the Gibbs Sampler." The American Statistician 46 (3): 167–74. https://doi.org/10.2307/2685208.

Chan, Ernie., 2008. Quantitative Trading: How to Build Your Own Algorithmic Trading Business, 1 edition. ed. Wiley, Hoboken, N.J.

Chan, Ernie. 2013. Algorithmic Trading: Winning Strategies and Their Rationale. 1st ed. Wiley Publishing.

Chen, Tianqi, and Carlos Guestrin. 2016. "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16, 785–94. https://doi.org/10.1145/2939672.2939785.

Chen, Wei, Tie-yan Liu, Yanyan Lan, Zhi-ming Ma, and Hang Li. 2009. "Ranking Measures and Loss Functions in Learning to Rank." In Advances in Neural Information Processing Systems 22, edited by Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta, 315–323. Curran Associates, Inc. http://papers.nips.cc/paper/3708-ranking-measures-and-loss-functions-in-learning-to-rank.pdf.

Cheung, W., 2010. The Black–Litterman model explained. J Asset Manag 11, 229–243. https://doi.org/10.1057/jam.2009.28.

Chib, Siddhartha, and Edward Greenberg. 1995. "Understanding the Metropolis-Hastings Algorithm." The American Statistician 49 (4): 327–35. https://doi.org/10.1080/00031305.1995.10476177.

Cho, Kyunghyun, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. "Learning Phrase Representations Using RNN Encoder–Decoder for Statistical Machine Translation." In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724–1734. Doha, Qatar: Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1179.

Chung, Junyoung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014. "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling." NIPS 2014 Workshop on Deep Learning, December 2014. https://nyuscholars.nyu.edu/en/publications/empirical-evaluation-of-gated-recurrent-neural-networks-on-sequen.

Clarke, R., Silva, H. de, Thorley, S., 2002. Portfolio Constraints and the Fundamental Law of Active Management. Financial Analysts Journal 58, 48–66.

Creswell, Antonia, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A. Bharath. 2018. "Generative Adversarial Networks: An Overview." IEEE Signal Processing Magazine 35 (1): 53–65. https://doi.org/10.1109/MSP.2017.2765202.

Cubuk, Ekin D. 2019. "AutoAugment: Learning Augmentation Strategies From Data." CVFPR, 11.

Cummins, Mark, and Andrea Bucca. 2012. "Quantitative Spread Trading on Crude Oil and Refined Products Markets." Quantitative Finance 12 (12): 1857–75. https://doi.org/10.1080/14697688.2012.715749.

Cybenko, G. 1989. "Approximation by Superpositions of a Sigmoidal Function." Mathematics of Control, Signals and Systems 2 (4): 303–14. https://doi.org/10.1007/BF02551274.

David H. Bailey et al. (2015), Backtest Overfitting: An Interactive Example. http://datagrid.lbl.gov/backtest/.

De Prado, Marcos Lopez. 2018. Advances in Financial Machine Learning. John Wiley & Sons.

DeMiguel, V., Garlappi, L., Uppal, R., 2009. Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy? Rev Financ Stud 22, 1915–1953. https://doi.org/10.1093/rfs/hhm075.

Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. "BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding." In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. Minneapolis, Minnesota: Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423.

Dumais, S. T., G. W. Furnas, T. K. Landauer, S. Deerwester, and R. Harshman. 1988. "Using Latent Semantic Analysis to Improve Access to Textual Information." In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 281–285. CHI '88. Washington, D.C., USA: Association for Computing Machinery. https://doi.org/10.1145/57167.57214.

Elliott, Robert J., John Van Der Hoek, and William P. Malcolm. 2005. "Pairs Trading." Quantitative Finance 5 (3): 271–76. https://doi.org/10.1080/14697680500149370.

Esposito, F., D. Malerba, G. Semeraro, and J. Kay. 1997. "A Comparative Analysis of Methods for Pruning Decision Trees." IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (5): 476–91. https://doi.org/10.1109/34.589207.

Esteban, Cristóbal, Stephanie L. Hyland, and Gunnar Rätsch. 2017. "Real-Valued (Medical) Time Series Generation with Recurrent Conditional GANs." ArXiv:1706.02633 [Cs, Stat], December. http://arxiv.org/abs/1706.02633.

Fabozzi, Frank J, Sergio M Focardi, and Petter N Kolm. 2010. Quantitative Equity Investing: Techniques and Strategies. John Wiley & Sons.

Fama, E.F., French, K.R., 2004. The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives 18, 25–46. https://doi.org/10.1257/0895330042162430.

Fama, Eugene F, and James D MacBeth. 1973. "Risk, Return, and Equilibrium: Empirical Tests." Journal of Political Economy 81 (3): 607–36.

Fama, Eugene F, and Kenneth R French. 1993. "Common Risk Factors in the Returns on Stocks and Bonds." Journal of Financial Economics 33: 3–56.

Fama, Eugene F, and Kenneth R French. 1998. "Value versus Growth: The International Evidence." The Journal of Finance 53 (6): 1975–99.

Fama, Eugene F., and Kenneth R. French. 2015. "A Five-Factor Asset Pricing Model." Journal of Financial Economics 116 (1): 1–22. https://doi.org/10.1016/j.jfineco.2014.10.010.

Fawcett, Tom. 2006. "An Introduction to ROC Analysis." Pattern Recognition Letters, ROC Analysis in Pattern Recognition, 27 (8): 861–74. https://doi.org/10.1016/j.patrec.2005.10.010.

Fefferman, Charles, Sanjoy Mitter, and Hariharan Narayanan. 2016. "Testing the Manifold Hypothesis." Journal of the American Mathematical Society 29 (4): 983–1049. https://doi.org/10.1090/jams/852.

Fei-Fei, Li. 2015. "ImageNet Large Scale Visual Recognition Challenge." International Journal of Computer Vision 115 (3): 211–52. https://doi.org/10.1007/s11263-015-0816-y.

Fisher, Walter D. 1958. "On Grouping for Maximum Homogeneity." Journal of the American Statistical Association 53 (284): 789–98. https://doi.org/10.2307/2281952.

Freund, Yoav, and Robert E Schapire. 1997. "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting." Journal of Computer and System Sciences 55 (1): 119–39. https://doi.org/10.1006/jcss.1997.1504.

Friedman, Jerome H. 2001. "Greedy Function Approximation: A Gradient Boosting Machine." The Annals of Statistics 29 (5): 1189–1232. https://www.jstor.org/stable/2699986.

Fu, Rao, Jie Chen, Shutian Zeng, Yiping Zhuang, and Agus Sudjianto. 2019. "Time Series Simulation by Conditional Generative Adversarial Net." ArXiv:1904.11419 [Cs, Eess, Stat], April. http://arxiv.org/abs/1904.11419.

Gatev, Evan, William N. Goetzmann, and K. Geert Rouwenhorst. 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule." The Review of Financial Studies 19 (3): 797–827. https://doi.org/10.1093/rfs/hhj020.

Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis, Third Edition. CRC Press.

Gómez, David, and Alfonso Rojas. 2015. "An Empirical Overview of the No Free Lunch Theorem and Its Effect on Real-World Machine Learning Classification." Neural Computation 28 (1): 216–28. https://doi.org/10.1162/NECO_a_00793.

Gonzalo, Jesús, and Tae-Hwy Lee. 1998. "Pitfalls in Testing for Long-Run Relationships." Journal of Econometrics 86 (1): 129–54. https://doi.org/10.1016/S0304-4076(97)00111-5.

Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. "Generative Adversarial Nets." In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 2672–2680. Curran Associates, Inc. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf.

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT press.

Goodfellow, Ian. 2014. "Multi-Digit Number Recognition from Street View Imagery Using Deep Convolutional Neural Networks." In ICLR2014.

Goyal, Amit. 2012. "Empirical Cross-Sectional Asset Pricing: A Survey." Financial Markets and Portfolio Management 26 (1): 3–38. https://doi.org/10.1007/s11408-011-0177-7.

Graham, Benjamin, David Dodd, and David Le Fevre Dodd. 1934. Security Analysis: The Classic 1934 Edition. McGraw Hill Professional.

Green, Jeremiah, John R. M. Hand, and X. Frank Zhang. 2017. "The Characteristics That Provide Independent Information about Average U.S. Monthly Stock Returns." The Review of Financial Studies 30 (12): 4389–4436. https://doi.org/10.1093/rfs/hhx019.

Grinold, R.C., 1989. The fundamental law of active management. The Journal of Portfolio Management 15, 30–37. https://doi.org/10.3905/jpm.1989.409211.

Gu, S., Kelly, B., and Xu, D. 2020 "Autoencoder Asset Pricing Models." Journal of Econometrics (forthcoming).

Gu, Shihao, Bryan Kelly, and Dacheng Xiu. 2020. "Empirical Asset Pricing via Machine Learning." The Review of Financial Studies, no. hhaa009 (February). https://doi.org/10.1093/rfs/hhaa009.

Harris, Larry. 2003. Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.

Hasselt, Hado van, Arthur Guez, and David Silver. 2015. "Deep Reinforcement Learning with Double Q-Learning." ArXiv:1509.06461 [Cs], September. http://arxiv.org/abs/1509.06461.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. 2nd ed. Springer Series in Statistics. New York: Springer-Verlag. https://doi.org/10.1007/978-0-387-84858-7.

Hastie, Trevor, Robert Tibshirani, and Martin Wainwright. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. CRC press.

He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. "Deep Residual Learning for Image Recognition." ArXiv:1512.03385 [Cs], December. http://arxiv.org/abs/1512.03385.

Hendricks, Dieter, and Diane Wilcox. 2014. "A Reinforcement Learning Extension to the Almgren-Chriss Framework for Optimal Trade Execution." In 2014 IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr), 457–64. https://doi.org/10.1109/CIFEr.2014.6924109.

Hessel, Matteo, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. 2017. "Rainbow: Combining Improvements in Deep Reinforcement Learning." ArXiv:1710.02298 [Cs], October. http://arxiv.org/abs/1710.02298.

Hihi, Salah El, and Yoshua Bengio. 1996. "Hierarchical Recurrent Neural Networks for Long-Term Dependencies." In Advances in Neural Information Processing Systems 8, edited by D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, 493–499. MIT Press. http://papers.nips.cc/paper/1102-hierarchical-recurrent-neural-networks-for-long-term-dependencies.pdf.

Hochreiter, Sepp, and Jürgen Schmidhuber. 1996. "LSTM Can Solve Hard Long Time Lag Problems." In Proceedings of the 9th International Conference on Neural Information Processing Systems, 473–479. NIPS'96. Denver, Colorado: MIT Press.

Hochreiter, Sepp, Yoshua Bengio, Paolo Frasconi, Jürgen Schmidhuber, and others. 2001. "Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies."

Hoerl, Arthur E, and Robert W Kennard. 1970. "Ridge Regression: Biased Estimation for Nonorthogonal Problems." Technometrics 12 (1): 55–67.

Hoffman, Matthew D., and Andrew Gelman. 2011. "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo." ArXiv:1111.4246 [Cs, Stat], November. http://arxiv.org/abs/1111.4246.

Hofmann, Thomas. 2001. "Unsupervised Learning by Probabilistic Latent Semantic Analysis." Machine Learning 42 (1): 177–96. https://doi.org/10.1023/A:1007617005950.

Hong, Harrison, Terence Lim, and Jeremy C. Stein. 2000. "Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies." The Journal of Finance 55 (1): 265–95. https://doi.org/10.1111/0022-1082.00206.

Hornik, Kurt. 1991. "Approximation Capabilities of Multilayer Feedforward Networks." Neural Networks 4 (2): 251–57. https://doi.org/10.1016/0893-6080(91)90009-T.

Hou, Kewei, Chen Xue, and Lu Zhang. 2015. "Digesting Anomalies: An Investment Approach." The Review of Financial Studies 28 (3): 650–705. https://doi.org/10.1093/rfs/hhu068.

Hou, K., Xue, C., Zhang, L., 2017. Replicating Anomalies (SSRN Scholarly Paper No. ID 2961979). Social Science Research Network, Rochester, NY.

Huang, Gao, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. 2018. "Densely Connected Convolutional Networks." ArXiv:1608.06993 [Cs], January. http://arxiv.org/abs/1608.06993.

Ismail Fawaz, Hassan, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. "Deep Learning for Time Series Classification: A Review." Data Mining and Knowledge Discovery 33 (4): 917–63. https://doi.org/10.1007/s10618-019-00619-1.

Jaeger, Herbert. 2001. "The 'Echo State' Approach to Analysing and Training Recurrent Neural Networks with an Erratum Note." Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148 (34): 13.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Vol. 112. Springer.

Jegadeesh, Narasimhan, and Sheridan Titman. 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." The Journal of Finance 48 (1): 65–91.

Jones, Charles. 2018. "Understanding the Market for Us Equity Market Data." NYSE. https://www0.gsb.columbia.edu/faculty/cjones/papers/2018.08.31%20US%20Equity%20Market%20Data%20Paper.pdf.

JP Morgan, 2012. Improving on risk parity – Hedging Forecast Uncertainty. https://am.jpmorgan.com/blobcontent/1378404528937/83456/11_77%20Risk%20Parity.pdf.

Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." In Advances in Neural Information Processing Systems 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 3146–3154. Curran Associates, Inc. http://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf.

Kearns, Michael, and Yuriy Nevmyvaka. 2013. "Machine Learning for Market Microstructure and High Frequency Trading." High Frequency Trading: New Realities for Traders, Markets, and Regulators.

Kelley, David. 2019. "Which Leading Indicators Have Done Better at Signaling Past Recessions?" Chicago Fed Letter, no. 425: 1.

Kelly, Bryan T., Seth Pruitt, and Yinan Su. 2019. "Characteristics Are Covariances: A Unified Model of Risk and Return." Journal of Financial Economics 134 (3): 501–24. https://doi.org/10.1016/j.jfineco.2019.05.001.

Kelly, J.L., 2011. A New Interpretation of Information Rate, in: The Kelly Capital Growth Investment Criterion, World Scientific Handbook in Financial Economics Series. WORLD SCIENTIFIC, pp. 25–34. https://doi.org/10.1142/9789814293501_0003.

Kingma, Diederik P., and Max Welling. 2014. "Auto-Encoding Variational Bayes." ArXiv:1312.6114 [Cs, Stat], May. http://arxiv.org/abs/1312.6114.

Kingma, Diederik, and Jimmy Ba. 2014. "Adam: A Method for Stochastic Optimization," December. https://arxiv.org/abs/1412.6980v8.

Kingma, Diederik P., and Max Welling. 2019. "An Introduction to Variational Autoencoders." Foundations and Trends® in Machine Learning 12 (4): 307–92. https://doi.org/10.1561/2200000056.

Kolanovic, Marko, and Rajesh Krishnamachari. 2017. "Big Data and AI Strategies - Machine Learning and Alternative Data Approach to Investing." White Paper. JP Morgan. http://www.fullertreacymoney.com/system/data/files/PDFs/2017/October/18th/Big%20Data%20and%20AI%20Strategies%20-%20Machine%20Learning%20and%20Alternative%20Data%20Approach%20to%20Investing.pdf.

Koshiyama, Adriano, Nick Firoozye, and Philip Treleaven. 2019. "Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination." ArXiv:1901.01751 [Cs, q-Fin, Stat], March. http://arxiv.org/abs/1901.01751.

Krauss, Christopher, Xuan Anh Do, and Nicolas Huck. 2017. "Deep Neural Networks, Gradient-Boosted Trees, Random Forests: Statistical Arbitrage on the S&P 500." European Journal of Operational Research 259 (2): 689–702. https://doi.org/10.1016/j.ejor.2016.10.031.

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E Hinton. 2012. "ImageNet Classification with Deep Convolutional Neural Networks." In Advances in Neural Information Processing Systems 25, edited by F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, 1097–1105. Curran Associates, Inc. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.

Lecun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. "Gradient-Based Learning Applied to Document Recognition." Proceedings of the IEEE 86 (11): 2278–2324. https://doi.org/10.1109/5.726791.

LeCun, Yann, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. 1989. "Backpropagation Applied to Handwritten Zip Code Recognition." Neural Computation 1 (4): 541–51.

LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. "Deep Learning." Nature 521 (7553): 436–44. https://doi.org/10.1038/nature14539.

Ledig, Christian, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, et al. 2017. "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network." In , 4681–90. http://openaccess.thecvf.com/content_cvpr_2017/html/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.html.

Ledoit, O., Wolf, M., 2003. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance 10, 603–621. https://doi.org/10.1016/S0927-5398(03)00007-0.

Levy, Omer, and Yoav Goldberg. 2014. "Neural Word Embedding as Implicit Matrix Factorization." In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 2177–2185. Curran Associates, Inc. http://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf.

Lin, Long-Ji, and Tom M Mitchell. 1992. Memory Approaches to Reinforcement Learning in Non-Markovian Domains. Citeseer.

Liu, Yinhan, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. "RoBERTa: A Robustly Optimized BERT Pretraining Approach." ArXiv:1907.11692 [Cs], July. http://arxiv.org/abs/1907.11692.

Lo, A.W., 2002. The Statistics of Sharpe Ratios. https://doi.org/10.2469/faj.v58.n4.2453.

Maaten, Laurens van der, and Geoffrey Hinton. 2008. "Visualizing Data Using T-SNE." Journal of Machine Learning Research 9 (Nov): 2579–2605. http://www.jmlr.org/papers/v9/vandermaaten08a.html.

Madhavan, Ananth. 2002. "Market Microstructure: A Practitioner's Guide." Financial Analysts Journal 58 (5): 28–42. www.jstor.org/stable/4480415.

Madhavan, Ananth. 2000. "Market Microstructure: A Survey." Journal of Financial Markets 3 (3): 205–58. https://doi.org/10.1016/S1386-4181(00)00007-0.

Malkiel, Burton G. 2003. "The Efficient Market Hypothesis and Its Critics." Journal of Economic Perspectives 17 (1): 59–82. https://doi.org/10.1257/089533003321164958.

Man, Xiliu, Tong Luo, and Jianwu Lin. 2019. "Financial Sentiment Analysis(FSA): A Survey." In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), 617–22. https://doi.org/10.1109/ICPHYS.2019.8780312.

Markowitz, H., 1952. Portfolio Selection. The Journal of Finance 7, 77–91. https://doi.org/10.2307/2975974.

Meredith, Mike, and John Kruschke. 2018. "Bayesian Estimation Supersedes the T-Test," 14.

Michaud, Richard O., Esch, D.N., Michaud, Robert O., 2017. The "Fundamental Law of Active Management" is No Law of Anything. https://doi.org/10.2139/ssrn.2834020.

Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. "Distributed Representations of Words and Phrases and Their Compositionality." In Proceedings of the 26th International Conference on Neural Information Processing Systems – Volume 2, 3111–3119. NIPS'13. USA: Curran Associates Inc. http://dl.acm.org/citation.cfm?id=2999792.2999959.

Miller, Rena S. 2016. "High Frequency Trading: Overview of Recent Developments." High Frequency Trading, 19.

Mimno, David, Hanna M. Wallach, Edmund Talley, Miriam Leenders, and Andrew McCallum. 2011. "Optimizing Semantic Coherence in Topic Models." In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 262–272. EMNLP '11. Edinburgh, United Kingdom: Association for Computational Linguistics.

Mitchell, Tom M. 1997. "Machine Learning."

Mnih, Andriy, and Koray Kavukcuoglu. 2013. "Learning Word Embeddings Efficiently with Noise-Contrastive Estimation." In Advances in Neural Information Processing Systems 26, edited by C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, 2265–2273. Curran Associates, Inc. http://papers.nips.cc/paper/5165-learning-word-embeddings-efficiently-with-noise-contrastive-estimation.pdf.

Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. "Playing Atari with Deep Reinforcement Learning." ArXiv:1312.5602 [Cs], December. http://arxiv.org/abs/1312.5602.

Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, et al. 2015. "Human-Level Control through Deep Reinforcement Learning." Nature 518 (7540): 529–33. https://doi.org/10.1038/nature14236.

Morin, Frederic, and Yoshua Bengio. 2005. "Hierarchical Probabilistic Neural Network Language Model." In Aistats, 5:246–52.

Nakamoto, Yukikazu. 2011. "A Short Introduction to Learning to Rank." IEICE Transactions on Information and Systems E94-D (1): 1–2. https://doi.org/10.1587/transinf.E94.D.1.

Nasseri, Alya Al, Allan Tucker, and Sergio de Cesare. 2015. "Quantifying StockTwits Semantic Terms' Trading Behavior in Financial Markets: An Effective Application of Decision Tree Algorithms." Expert Systems with Applications 42 (23): 9192–9210. https://doi.org/10.1016/j.eswa.2015.08.008.

Netzer, Yuval. 2011. "Reading Digits in Natural Images with Unsupervised Feature Learning." In NIPS Workshop on Deep Learning.

Nevmyvaka, Yuriy, Yi Feng, and Michael Kearns. 2006. "Reinforcement Learning for Optimized Trade Execution." In Proceedings of the 23rd International Conference on Machine Learning, 673–680. ICML '06. Pittsburgh, Pennsylvania, USA: Association for Computing Machinery. https://doi.org/10.1145/1143844.1143929.

Ng, Andrew Y., and Michael I. Jordan. 2002. "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes." In Advances in Neural Information Processing Systems 14, edited by T. G. Dietterich, S. Becker, and Z. Ghahramani, 841–848. MIT Press. http://papers.nips.cc/paper/2020-on-discriminative-vs-generative-classifiers-a-comparison-of-logistic-regression-and-naive-bayes.pdf.

Nilsson, Nils J. 2009. The Quest for Artificial Intelligence. Cambridge University Press.

Novy-Marx, R., 2015. Backtesting Strategies Based on Multiple Signals (Working Paper No. 21329). National Bureau of Economic Research. https://doi.org/10.3386/w21329.

Odena, Augustus. 2019. "Open Questions about Generative Adversarial Networks." Distill 4 (4): e18. https://doi.org/10.23915/distill.00018.

Pan, Zhaoqing, Weijie Yu, Xiaokai Yi, Asifullah Khan, Feng Yuan, and Yuhui Zheng. 2019. "Recent Progress on Generative Adversarial Networks (GANs): A Survey." IEEE Access 7: 36322–33. https://doi.org/10.1109/ACCESS.2019.2905015.

Pennington, Jeffrey, Richard Socher, and Christoper Manning. 2014. "Glove: Global Vectors for Word Representation." In EMNLP, 14:1532–43. https://doi.org/10.3115/v1/D14-1162.

Perold, A.F., 2004. The Capital Asset Pricing Model. Journal of Economic Perspectives 18, 3–24. https://doi.org/10.1257/0895330042162340.

Prabhavalkar, Rohit, Kanishka Rao, Tara N. Sainath, Bo Li, Leif Johnson, and Navdeep Jaitly. 2017. "A Comparison of Sequence-to-Sequence Models for Speech Recognition." In Interspeech 2017, 939–43. ISCA. https://doi.org/10.21437/Interspeech.2017-233.

Prado, M.L. de, 2016. "Building Diversified Portfolios that Outperform Out of Sample." The Journal of Portfolio Management 42, 59–69. https://doi.org/10.3905/jpm.2016.42.4.059.

Preis, Tobias, Helen Susannah Moat, and H. Eugene Stanley. 2013. "Quantifying Trading Behavior in Financial Markets Using Google Trends." Scientific Reports 3 (1): 1–6. https://doi.org/10.1038/srep01684.

Prokhorenkova, Liudmila, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2019. "CatBoost: Unbiased Boosting with Categorical Features." ArXiv:1706.09516 [Cs], January. http://arxiv.org/abs/1706.09516.

Radford, Alec, Luke Metz, and Soumith Chintala. 2016. "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks." ArXiv:1511.06434 [Cs], January. http://arxiv.org/abs/1511.06434.

Raffinot, T., 2017. Hierarchical Clustering-Based Asset Allocation. The Journal of Portfolio Management 44, 89–99. https://doi.org/10.3905/jpm.2018.44.2.089.

Rasekhschaffe, Keywan Christian, and Robert C. Jones. 2019. "Machine Learning for Stock Selection." Financial Analysts Journal 75 (3): 70–88. https://doi.org/10.1080/0015198X.2019.1596678.

Redmon, Joseph. 2016. "You Only Look Once: Unified, Real-Time Object Detection." ArXiv:1506.02640 [Cs], May.

Reed, Scott, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, and Honglak Lee. 2016. "Learning What and Where to Draw." ArXiv:1610.02454 [Cs], October. http://arxiv.org/abs/1610.02454.

Reinganum, Marc R. 1981. "Misspecification of Capital Asset Pricing: Empirical Anomalies Based on Earnings' Yields and Market Values." Journal of Financial Economics 9 (1): 19–46.

Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. 2015. "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." In Advances in Neural Information Processing Systems 28, edited by C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, 91–99. Curran Associates, Inc. http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf.

Roa-Vicens, Jacobo, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, and Ricardo Silva. 2019. "Towards Inverse Reinforcement Learning for Limit Order Book Dynamics." ArXiv:1906.04813 [Cs, q-Fin, Stat], June. http://arxiv.org/abs/1906.04813.

Röder, Michael, Andreas Both, and Alexander Hinneburg. 2015. "Exploring the Space of Topic Coherence Measures." In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 399–408. WSDM '15. Shanghai, China: Association for Computing Machinery. https://doi.org/10.1145/2684822.2685324.

Rokach, Lior, and Oded Z. Maimon. 2008. Data Mining with Decision Trees: Theory and Applications. World Scientific.

Roll, Richard, and Stephen A. Ross. 1984. "The Arbitrage Pricing Theory Approach to Strategic Portfolio Planning." Financial Analysts Journal 40 (3): 14–26. https://doi.org/10.2469/faj.v40.n3.14.

Romero, P.J., Balch, T., 2014. What Hedge Funds Really Do: An Introduction to Portfolio Management. Business Expert Press.

Ruder, Sebastian. 2017. "An Overview of Gradient Descent Optimization Algorithms." ArXiv:1609.04747 [Cs], June. http://arxiv.org/abs/1609.04747.

Salimans, Tim, Diederik P. Kingma, and Max Welling. 2015. "Markov Chain Monte Carlo and Variational Inference: Bridging the Gap." ArXiv:1410.6460 [Stat], May. http://arxiv.org/abs/1410.6460.

Samuelson, P., Thorp, E., T. Kassouf, S., 1968. Beat the Market: A Scientific Stock Market System. Journal of the American Statistical Association 63, 1049. https://doi.org/10.2307/2283900.

Saul, Lawrence K, and Sam T Roweis. 2000. "An Introduction to Locally Linear Embedding." Unpublished. Available at: https://cs.nyu.edu/~roweis/lle/papers/lleintro.pdf.

Schapire, Robert E., and Yoav Freund. 2012. Boosting: Foundations and Algorithms. MIT Press.

Schaul, Tom, John Quan, Ioannis Antonoglou, and David Silver. 2015. "Prioritized Experience Replay." ArXiv:1511.05952 [Cs], November. http://arxiv.org/abs/1511.05952.

Schuster, M., and K.K. Paliwal. 1997. "Bidirectional Recurrent Neural Networks." IEEE Transactions on Signal Processing 45 (11): 2673–81. https://doi.org/10.1109/78.650093.

Sezer, Omer Berat, and Ahmet Murat Ozbayoglu. 2018. "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach." Applied Soft Computing 70 (September): 525–38. https://doi.org/10.1016/j.asoc.2018.04.024.

Sievert, Carson, and Kenneth Shirley. 2014. "LDAvis: A Method for Visualizing and Interpreting Topics." In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 63–70.

Sigtia, Siddharth, Emmanouil Benetos, Srikanth Cherla, Tillman Weyde, A. Garcez, and Simon Dixon. 2014. "RNN-Based Music Language Models for Improving Automatic Music Transcription."

Simonyan, Karen. 2015. "Very Deep Convolutional Networks for Large-Scale Image Recognition." ArXiv:1409.1556 [Cs], April.

Srivastava, Nitish, Elman Mansimov, and Ruslan Salakhutdinov. 2016. "Unsupervised Learning of Video Representations Using LSTMs," 10.

Stevens, Keith, Philip Kegelmeyer, David Andrzejewski, and David Buttler. 2012. "Exploring Topic Coherence over Many Models and Many Topics."

Strumeyer, Gary. 2017. The Capital Markets: Evolution of the Financial Ecosystem. John Wiley & Sons.

Sutskever, Ilya, James Martens, George Dahl, and Geoffrey Hinton. 2013. "On the Importance of Initialization and Momentum in Deep Learning." In International Conference on Machine Learning, 1139–47. http://proceedings.mlr.press/v28/sutskever13.html.

Sutton, Richard S, and Andrew G Barto. 2018. Reinforcement Learning: An Introduction. MIT press.

Sutton, Richard S, David A. McAllester, Satinder P. Singh, and Yishay Mansour. 2000. "Policy Gradient Methods for Reinforcement Learning with Function Approximation." In Advances in Neural Information Processing Systems 12, edited by S. A. Solla, T. K. Leen, and K. Müller, 1057–1063. MIT Press. http://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf.

Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. "Going Deeper with Convolutions." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. https://doi.org/10.1109/CVPR.2015.7298594.

Trichilo, D., Braun, J.L., 2005. Extending the Fundamental Law of Investment Management.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. "Attention Is All You Need." In Advances in Neural Information Processing Systems 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 5998–6008. Curran Associates, Inc. http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf.

Vincent, Pascal, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. "Extracting and Composing Robust Features with Denoising Autoencoders." In Proceedings of the 25th International Conference on Machine Learning, 1096–1103. ICML '08. Helsinki, Finland: Association for Computing Machinery. https://doi.org/10.1145/1390156.1390294.

Wang, Alex, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. "SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems." ArXiv:1905.00537 [Cs], May. http://arxiv.org/abs/1905.00537.

Wang, Jia, Tong Sun, Benyuan Liu, Yu Cao, and Hongwei Zhu. 2019. "CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets." In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 3705–11. Macao, China: International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2019/514.

Watkins, Christopher J. C. H., and Peter Dayan. 1992. "Q-Learning." In Machine Learning, 279–292.

Watkins, Christopher John Cornish Hellaby. 1989. "Learning from Delayed Rewards.", http://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf.

Werbos, P.J. 1990. "Backpropagation through Time: What It Does and How to Do It." Proceedings of the IEEE 78 (10): 1550–60. https://doi.org/10.1109/5.58337.

Wiese, Magnus, Robert Knobloch, Ralf Korn, and Peter Kretschmer. 2019. "Quant GANs: Deep Generation of Financial Time Series." ArXiv:1907.06673 [Cs, q-Fin, Stat], December. http://arxiv.org/abs/1907.06673.

Williams, Ronald J, and David Zipser. 1989. "A Learning Algorithm for Continually Running Fully Recurrent Neural Networks." Neural Computation 1 (2): 270–80.

Wooldridge, Jeffrey M. 2002. "Econometric Analysis of Cross Section and Panel Data MIT Press." Cambridge, MA 108.

Wooldridge, Jeffrey M. 2008. Introductory Econometrics: A Modern Approach. Cengage Learning.

Yoon, Jinsung, Daniel Jarrett, and Mihaela van der Schaar. 2019. "Time-Series Generative Adversarial Networks." In Advances in Neural Information Processing Systems 32, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d extquotesingle Alché-Buc, E. Fox, and R. Garnett, 5508–5518. Curran Associates, Inc. http://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf.

Zhang, Han, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris Metaxas. 2017. "StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks." ArXiv:1612.03242 [Cs, Stat], August. http://arxiv.org/abs/1612.03242.

Zhou, Xingyu, Zhisong Pan, Guyu Hu, Siqi Tang, and Cheng Zhao. 2018. "Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets." Research Article: Mathematical Problems in Engineering. Hindawi. 2018. https://doi.org/10.1155/2018/4907423.

Zhu, Jun-Yan, Taesung Park, Phillip Isola, and Alexei A. Efros. 2018. "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks." ArXiv:1703.10593 [Cs], November. http://arxiv.org/abs/1703.10593.

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