Deep Learning for Finance: Deep Portfolios

By J. Heaton, Nicholas G. Polson, J. Witte. 2016

649 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Finance, Management Science and Operations Research, Optimization & Training, Economics & Operations Research

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “Portfolio selection and smart indexing were the main focuses of [130] and [131] using AE and LSTM networks.”
Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? (2018, background): “Over against the limitations of existing models DL can process high frequency big data, analyze the financial market, and predict stock returns [23], [24].”
Deep Learning for Predicting Asset Returns (2018, background): “…economics and ﬁnance, see Gal-lant and White (1988a,b); Hornik et al. (1989); Gallant and White (1992); Kuan and White (1994); Hutchinson et al. (1994); Lo (1994); Qi (1999); Jones (2006); Sirignano et al. (2016) and Heaton et al. (2017), deep learning is new to characteristic-based asset pricing.”
AI in Finance: Challenges, Techniques, and Opportunities (2021, methodology): “, Bayesian networks and Markov networks), machine learning models such as for clustering, classification and semi-supervised learning, deep reinforcement learning (DRL) models, and deep neural networks [13, 21, 38, 50, 73, 133, 156].”
Restricted Boltzmann machine based stock market trend prediction (2017, methodology): “[33] provide a smart indexing example by auto-encoding the IBB biotechnology index.”
A Multi-objective Deep Reinforcement Learning Approach for Stock Index Future’s Intraday Trading (2017, background): “Many researches have been done on machine learning approaches to financial market trading, but many of them try to predict price movement or trends [2], [3].”
Cryptocurrency portfolio management with deep reinforcement learning (2016, background): “Many of the work applying deep machine-learning to financial market trading, tries to predict the price movements or trends [9], [10] using historic market data.”
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (2017, background): “However, many of them try to predict price movements or trends (Heaton et al., 2016; Niaki and Hoseinzade, 2013; Freitas et al., 2009).”
Sequence classification of the limit order book using recurrent neural networks (2017, background): “The overall objective function is closely related to ridge regression with a g-prior (Heaton et al., 2017).”
Deep Reinforcement Learning: An Overview (2017, background): “It would be interesting to investigate the reward model with the approach in Su et al. (2016b) below or with inverse RL and imitation learning as discussed in Section 6, although Su et al. (2016b) mentioned that such methods are costly, and humans may not act optimally.”
Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications (2018, methodology): “Witte [13] where they present an automated portfolio selection procedure based on, first encoding a large dataset of historical returns by means of an Autoencoder (AE) and then decoding it by solving an optimization problem.”
Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics (2020, positive): “By applying DL, financial modeling can be done with far more precision than standard applications (Heaton et al. 2017).”