Portfolio Selection : A Survey

By Liang Bin, Steven C. H. Hoi. 2014

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Frank Xing
2. Steven C. H. Hoi
3. Doyen Sahoo
4. Zhengyao Jiang
5. Jing Lu
6. Peilin Zhao
7. Roy E. Welsch
8. Dixing Xu

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, neutral): “[125] reviewed the online portfolio selection studies using various rule-based or ML models.”
• Natural language based financial forecasting: a survey (2017, methodology): “numerical data, many studies have discussed online methods to reduce algorithm complexity, such as motif matching [80] and online portfolio selection [62].”
• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (2017, methodology): “Most of the strategies to be compared in this work were surveyed by Li and Hoi (2014), including Aniticor (Borodin et al., 2004), Online Moving Average Reversion (OLMAR) (Li et al., 2015b), Passive Aggressive Mean Reversion (PAMR) (Li et al., 2012), Confi-dence Weighted Mean Reversion (CWMR) (Li et…”
• Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach (2020, background): “Machine learning has been applied in computational finance (Li and Hoi, 2014), among which one of the most interesting and challenging ones is the detection of accounting frauds in publicly traded firms (Ravisankar et al.”
• Understand Dynamic Regret with Switching Cost for Online Decision Making (2019, background): “Some problems such as dynamic control of data centers [32], stock portfolio management [27], require to be sensitive to the small change between successive decisions, and the switching cost in these problems is usually bounded by ‖xt+1 − xt ‖.”
• Online Learning: A Comprehensive Survey (2018, methodology): “Besides the traditional benchmarking approaches, the approaches for online portoflio selection can be categorized into Follow-the-winner, Follow-the-loser, Pattern Matching, and Meta-Learning approaches [217].”
• Adversarial Deep Reinforcement Learning in Portfolio Management (2018, background): “Several major categories of portfolio management approaches including ”Follow-the-Winner”, ”Follow-the-Loser”, ”PatternMatching” and ”Meta-Learning Algorithms” have been pro- 1https://github.com/qq303067814/Reinforcement-learning-in-portfoliomanagement- posed.”
• Cryptocurrency portfolio management with deep reinforcement learning (2016, methodology): “Traditional portfolio management methods can be classified into four classes: “Follow-the-Winner”, “Follow-theLoser”, “Pattern-Matching” and “Meta-Learning” [3].”