An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms
Integrity signals
Registry
Cites
Statistics
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?
The twelve selected accuracy and closeness evaluation metrics are appropriate and effective for classification and regression ML tasks in stock market prediction.
“These metrics were selected due to their appropriateness and effectiveness for classification and regression ML tasks in stock market prediction [1,27,62].”
From A comprehensive evaluation of ensemble learning for stock-market prediction (2020) · cites this paper as [27]
Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951 · see citing works
A systematic analysis of performance measures for classification tasks
Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515 · see citing works
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Lee, M.-C. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.02.038 · Cited by 302 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A comprehensive evaluation of ensemble learning for stock-market prediction
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00299-5 · Cited by 329 · see citing works
- Authors
- Jiang, M., Liu, J., Zhang, L., & Liu, C.
- Affiliations
- China Jiliang University (CN)
- Corresponding author
- Liu, Jiapeng
- Venue
- Physica A: Statistical Mechanics and its Applications
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 541
- Pages
- 122272
- ISSN
- 0378-4371 (ISSN-L)
- OpenAlex
- W2993958139
- Cited by
- 155
- References
- 7
- Citations by year
- 2026: 15 · 2025: 24 · 2024: 31 · 2023: 22 · 2022: 31 · 2021: 16 · 2020: 16
- FWCI
- 11.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physica A Statistical Mechanics and its Applications (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Social Science Foundation of China (18BGL224)
- soft science project of Zhejiang Province (2017C35027)
- Natural Science Foundation of Zhejiang Province (LQ17G030008)
- key humanities and social science projects in Zhejiang Province university (2016GH019)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Keywords
- stock index forecasting, stacking ensemble, tree-based ensemble models, random forest, XGBoost, LightGBM, recurrent neural networks, LSTM, GRU, logistic regression meta-classifier, macroeconomic indicators, technical indicators, base learner 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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
