The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Online portfolio selection strategy based on combining experts' advice
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Taking many complex financial factors into account, DRL trading agents build a multi-factor model and provide algorithmic trading strategies, which are difficult for human traders [3, 47, 24, 22].”
From FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance (2020) · cites this paper as [47]
In the citing paper
“Incorporating many financial factors, as shown in Fig. 1, a DRL trading agent builds a multi-factor model to trade automatically, which is difficult for human traders to accomplish [4, 53].”
From FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance (2021) · cites this paper as [53]
Citations
Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
Prediction, Learning, and Games
Cesa-Bianchi, N., & Lugosi, G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546921 · Cited by 3,162
Exponentiated Gradient versus Gradient Descent for Linear Predictors
Kivinen, J., & Warmuth, M. K. (1997) · Information and Computation · doi:10.1006/inco.1996.2612 · Cited by 864
Cover, T. M. (1991) · Mathematical Finance · doi:10.1111/j.1467-9965.1991.tb00002.x · Cited by 587
On‐Line Portfolio Selection Using Multiplicative Updates
Helmbold, D. P., Schapire, R. E., Singer, Y., et al. (1998) · Mathematical Finance · doi:10.1111/1467-9965.00058 · Cited by 392
Universal portfolios with side information
Cover, T. M., & Ordentlich, E. (1996) · IEEE Transactions on Information Theory · doi:10.1109/18.485708 · Cited by 347
Li, B., & Hoi, S. C. H. (2014) · ACM Computing Surveys · doi:10.1145/2512962 · Cited by 245
Universal portfolios with and without transaction costs
Blum, A. L., & Kalai, A. T. (1997) · Annual Conference on Computational Learning Theory (COLT) · doi:10.1145/267460.267518 · Cited by 153
A Nonstandard Approach to Option Pricing
Cutland, N. J., Kopp, E., & Willinger, W. (1991) · Mathematical Finance · doi:10.1111/j.1467-9965.1991.tb00017.x · Cited by 96
The Cost of Achieving the Best Portfolio in Hindsight
Ordentlich, E., & Cover, T. M. (1998) · Mathematics of Operations Research · doi:10.1287/moor.23.4.960 · Cited by 94
Mean–variance models for portfolio selection subject to experts’ estimations
Huang, X. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.11.119 · Cited by 87
Stochastic Nonstationary Optimization for Finding Universal Portfolios
Gaivoronski, A. A., & Stella, F. A. (2000) · Annals of Operations Research · doi:10.1023/a:1019271201970 · Cited by 67
Possibilistic mean–standard deviation models to portfolio selection for bounded assets
Zhang, W.-G. (2007) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.12.080 · Cited by 53
Singer, Y. (1997) · International Journal of Neural Systems · doi:10.1142/s0129065797000434 · Cited by 53
The weak aggregating algorithm and weak mixability
Kalnishkan, Y., & Vyugin, M. V. (2007) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2007.08.003 · Cited by 44
Ordentlich, E., & Cover, T. M. (1996) · Annual Conference on Computational Learning Theory (COLT) · doi:10.1145/238061.238161 · Cited by 42
On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract)
Borodin, A., El‐Yaniv, R., & Gogan, V. (2000) · Lecture notes in computer science · doi:10.1007/10719839_19 · Cited by 38
On-line portfolio selection using stochastic programming
Gaivoronski, A. A., & Stella, F. A. (2003) · Journal of Economic Dynamics and Control · doi:10.1016/s0165-1889(02)00053-2 · Cited by 37
Universal data compression and portfolio selection
Cover, T. M. (2002) · doi:10.1109/sfcs.1996.548512 · Cited by 35
Weak aggregating algorithm for the distribution-free perishable inventory problem
Levina, T., Levin, Y., McGill, J., et al. (2010) · Operations Research Letters · doi:10.1016/j.orl.2010.09.006 · Cited by 33
UNIVERSAL SEMICONSTANT REBALANCED PORTFOLIOS
Kozat, S. S., & Singer, A. C. (2010) · Mathematical Finance · doi:10.1111/j.1467-9965.2010.00430.x · Cited by 30
On-line portfolio selection strategy with prediction in the presence of transaction costs
Albeverio, S. A., Lao, L., & Zhao, X. (2001) · Mathematical Methods of Operations Research · doi:10.1007/s001860100142 · Cited by 24
A class of on-line portfolio selection algorithms based on linear learning
Zhang, W., Zhang, Y., Yang, X., et al. (2012) · Applied Mathematics and Computation · doi:10.1016/j.amc.2012.05.041 · Cited by 12
Self-citationUniversal portfolios with memory
Cover, T. M. (2002) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.1994.394937 · Cited by 3
Research works printed but not linked: 2
- Li, B., & Hoi, S. C. H. (2012). On-line portfolio selection: A survey. In Proceedings of CoRR.
- Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 8, 77–91.
Cited by
Deep reinforcement learning for automated stock trading: an ensemble strategy
Yang, H., Liu, X.-Y., Zhong, S., & Walid, A. (2020) · Proceedings of the First ACM International Conference on AI in Finance · doi:10.1145/3383455.3422540 · Cited by 196
“However, it is challenging for analysts to consider all relevant factors in a complex and dynamic stock market [3, 23, 47].”
BackgroundZou, J., Lou, J., Wang, B., & Liu, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122801 · Cited by 89
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
Liu, X.-Y. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3737257 · Cited by 90
Liu, X.-Y., Yang, H., Gao, J., & Wang, C. D. (2021) · Proceedings of the Second ACM International Conference on AI in Finance · doi:10.1145/3490354.3494366 · Cited by 87
(2018) · arXiv (Cornell University) · Cited by 74
Metadata
- Authors
- Zhang, Y., & Yang, X.
- Affiliations
- Guangdong University of Technology (CN)
- Corresponding author
- Yang, Xingyu
- Venue
- Computational Economics
- Published
- 2016-05-25
- Volume
- 50
- Issue
- 1
- Pages
- 141-159
- ISSN
- 0927-7099 (ISSN-L)
- 1572-9974
- OpenAlex
- W2398342468
- Cited by
- 38
- References
- 24
- Citations by year
- 2026: 5 · 2024: 8 · 2023: 6 · 2022: 6 · 2021: 3 · 2020: 6 · 2019: 3 · 2018: 1
- FWCI
- 1.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Computational Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- 125 planning for the development of Philosophy and Social Sciences in Guangzhou (15G29)
- National Nature Science Foundation of China (71501049)
- Humanities and Social Science Foundation of the Ministry of Education of China (13YJC630234, 11YJC630255)
- National Natural Science Foundation of China (71301029)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Risk and Portfolio Optimization
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Keywords
- online portfolio selection, expert advice, stock selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
