Multi-Task Learning for Stock Selection

By Joumana Ghosn, Yoshua Bengio. 1996

Subfields: Economics & Operations Research

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In Economics & Operations Research

1. Eric P. Xing
2. R. Caruana
3. Qiang Yang
4. Tom M. Heskes
5. Yu Zhang

Citations in the corpus

• An Overview of Multi-task Learning (2018, background): “Applications of MTL in ubiquitous computing include stock prediction [126], multi-device localization [127], the inverse dynamics problem for robotics [128,129], estimation of travel costs on road networks [130], travel-time prediction on road networks [131], and traffic-sign recognition [132].”
• A Survey on Multi-Task Learning (2017, neutral): “In [207], a multi-task neural network, which shares hidden layers for different tasks, is used to predict yearly returns of stocks.”
• Multi-target regression via input space expansion: treating targets as inputs (2012, background): “The question of how much sharing is better when multi-task ANNs are applied for stock return prediction was explored by (Ghosn and Bengio 1996) who concluded that a partial sharing of network parameters is preferable compared to full or no sharing.”
• Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach (1998, background): “An exception is [7], where di erent tasks concerning stock selection and portfolio management are combined in various ways.”
• Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso (2010, background): “A similar problem arises in stock prediction where some of the stock prices are more highly correlated than others [6].”
• Multitask Learning (1997, background): “(Ghosn & Bengio 1997) experiment with several different architectures for MTL in backprop nets.”
• Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks (2015, background): “in many applications, such as disease prediction [44], [45] and financial stock selection [46].”