The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Training feedforward networks with the Marquardt algorithm
Hagan, M. T., & Menhaj, M. B. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329697 · Cited by 7,701
An improved harmony search algorithm for solving optimization problems
Mahdavi, M., Fesanghary, M., & Damangir, E. (2007) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.11.033 · Cited by 1,863
A GA-based feature selection and parameters optimizationfor support vector machines
Huang, C.-L., & Wang, C.-J. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.09.024 · Cited by 1,363
Kara, Y., Acar Boyacioglu, M., & Baykan, Ö. K. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.10.027 · Cited by 877
Using artificial neural network models in stock market index prediction
Guresen, E., Kayakutlu, G., & Daim, T. U. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.068 · Cited by 786
Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction
Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/614342 · Cited by 577
Classification of EEG signals using neural network and logistic regression
Subasi, A., & Erçelebi, E. (2005) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2004.10.009 · Cited by 573
Optimal cost design of water distribution networks using harmony search
Geem, Z. W. (2006) · Engineering Optimization · doi:10.1080/03052150500467430 · Cited by 529
Optimal Design of Water Distribution Networks Using Harmony Search
Geem, Z. W. (2009) · Cited by 500
Research works printed but not linked: 13
- Ahmed, 2007, Design and implementation of a neural network for real-time object tracking, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 1, 1816
- Burney, 2005, Levenberg–Marquardt algorithm for Karachi Stock Exchange share rates forecasting, International Journal of Computational Intelligence, 1, 144
- Chiu, 2003, Applying artificial neural network and Chinese news classification techniques to Taiwan Stock Market, Tamsui Oxford Journal of Mathematical Sciences, 19, 201
- Cinko, 2009, Examining the day of the week effect in Istanbul Stock Exchange (ISE), International Business & Economics Research Journal, 8, 1
- Dastgir, 2012, Short-term prediction of Tehran Stock Exchange Price Index (TEPIX): using artificial neural network (ANN), Quarterly Journal of Securities Exchange, 4, 237
- Graupe, 2007, Principles of artificial neural networks, 10.1142/6429 · doi:10.1142/6429
- Haider, 2009, Inflation forecasting in Pakistan using artificial neural networks, Pakistan Economic and Social Review, 47, 123
- Karymshakov, 2012, Forecasting stock index movement with artificial neural networks: the case of Istanbul Stock Exchange, Trakya University Journal of Social Science, 14, 231
- Ravichandran, 2005, Estimation of return on investment in share market through ANN, Journal of Theoretical and Applied Information Technology, 44
- Sureshkumar, 2012, Performance analysis of stock price prediction using artificial neural network, Global Journal of Computer Science and Technology, 12, 18
- Vaisla, 2010, An analysis of the performance of artificial neural network technique for stock market forecasting, International Journal on Computer Science and Engineering, 2, 2104
- Wei, 2012, A hybrid recurrent neural networks model based on synthesis features to forecast the Taiwan Stock Market, International Journal of Innovative Computing Information and Control, 8, 5559
- Yildiz, 2008, Forecasting the Istanbul Stock Exchange national 100 index using an artificial neural network, World Academy of Science, Engineering and Technology, 22, 36
Cited by
A systematic review of fundamental and technical analysis of stock market predictions
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09754-z · Cited by 547
Chen, Y., & Hao, Y. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.044 · Cited by 382
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
Applying artificial intelligence technique to predict knowledge hiding behavior
Abubakar, A. M., Behravesh, E., Rezapouraghdam, H., & Yildiz, S. B. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.02.006 · Cited by 221
Chen, C., Zhao, L., Bian, J., Xing, C., & Liu, T.-Y. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330663 · Cited by 73
Metadata
- Authors
- Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 44
- Pages
- 320-331
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1815264562
- Cited by
- 324
- References
- 36
- FWCI
- 17.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction. https://app.cochat.ai/papers/10.1016/j.eswa.2015.09.029
- The paper
- Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2015.09.029
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.
