David Quintana

836 total citations
41 papers, 558 citations indexed

About

David Quintana is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, David Quintana has authored 41 papers receiving a total of 558 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Management Science and Operations Research, 18 papers in Artificial Intelligence and 8 papers in Computational Theory and Mathematics. Recurrent topics in David Quintana's work include Stock Market Forecasting Methods (15 papers), Metaheuristic Optimization Algorithms Research (10 papers) and Evolutionary Algorithms and Applications (9 papers). David Quintana is often cited by papers focused on Stock Market Forecasting Methods (15 papers), Metaheuristic Optimization Algorithms Research (10 papers) and Evolutionary Algorithms and Applications (9 papers). David Quintana collaborates with scholars based in Spain, Ireland and France. David Quintana's co-authors include Alejandro Cervantes, Pedro Isasi, Andrés L. Suárez‐Cetrulo, Yago Sáez, S. Alonso Monsalve, Francisco Luna, Inés M. Galván, Edward Rolando Núñez‐Valdéz, Rubén González Crespo and Enrique Herrera‐Viedma and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and International Journal of Environmental Research and Public Health.

In The Last Decade

David Quintana

38 papers receiving 537 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
David Quintana 281 160 130 103 97 41 558
Matthew Dixon 156 0.6× 91 0.6× 127 1.0× 43 0.4× 108 1.1× 42 426
Tiejun Ma 206 0.7× 106 0.7× 138 1.1× 63 0.6× 99 1.0× 37 495
Shian-Chang Huang 184 0.7× 188 1.2× 191 1.5× 66 0.6× 102 1.1× 42 614
Bruce Vanstone 358 1.3× 123 0.8× 273 2.1× 29 0.3× 186 1.9× 44 638
Haruna Isah 295 1.0× 142 0.9× 92 0.7× 116 1.1× 83 0.9× 14 541
Wai-Ki Ching 156 0.6× 56 0.3× 127 1.0× 34 0.3× 198 2.0× 80 838
Woojin Chang 143 0.5× 68 0.4× 399 3.1× 50 0.5× 184 1.9× 55 686
Arash Bahrammirzaee 167 0.6× 140 0.9× 89 0.7× 43 0.4× 67 0.7× 6 394
Jianfeng Zhang 102 0.4× 243 1.5× 90 0.7× 50 0.5× 102 1.1× 45 648
Francesca Perla 78 0.3× 315 2.0× 67 0.5× 63 0.6× 33 0.3× 23 601

Countries citing papers authored by David Quintana

Since Specialization
Citations

This map shows the geographic impact of David Quintana's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Quintana with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Quintana more than expected).

Fields of papers citing papers by David Quintana

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Quintana. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Quintana. The network helps show where David Quintana may publish in the future.

Co-authorship network of co-authors of David Quintana

This figure shows the co-authorship network connecting the top 25 collaborators of David Quintana. A scholar is included among the top collaborators of David Quintana based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Quintana. David Quintana is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Suárez‐Cetrulo, Andrés L., David Quintana, & Alejandro Cervantes. (2023). Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review.. International Journal of Interactive Multimedia and Artificial Intelligence. 9(1). 137–148. 2 indexed citations
2.
Suárez‐Cetrulo, Andrés L., David Quintana, & Alejandro Cervantes. (2022). A survey on machine learning for recurring concept drifting data streams. Expert Systems with Applications. 213. 118934–118934. 60 indexed citations
3.
Cervantes, Alejandro, David Quintana, Yago Sáez, & Pedro Isasi. (2022). Longitudinal Segmented Analysis of Internet Usage and Well-Being Among Older Adults.. International Journal of Interactive Multimedia and Artificial Intelligence. 8(6). 168–176.
4.
Quintana, David & David Moreno. (2021). Resampled Efficient Frontier Integration for MOEAs. Entropy. 23(4). 422–422. 1 indexed citations
5.
Monsalve, S. Alonso, Andrés L. Suárez‐Cetrulo, Alejandro Cervantes, & David Quintana. (2020). Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications. 149. 113250–113250. 136 indexed citations
6.
Suárez‐Cetrulo, Andrés L., Alejandro Cervantes, & David Quintana. (2019). Incremental Market Behavior Classification in Presence of Recurring Concepts. Entropy. 21(1). 25–25. 16 indexed citations
7.
Quintana, David, Alejandro Cervantes, Yago Sáez, & Pedro Isasi. (2018). Internet Use and Psychological Well-Being at Advanced Age: Evidence from the English Longitudinal Study of Aging. International Journal of Environmental Research and Public Health. 15(3). 480–480. 52 indexed citations
8.
Duru, Okan, et al.. (2018). Computational Intelligence in Finance and Economics [Guest Editorial]. IEEE Computational Intelligence Magazine. 13(4). 13–13. 1 indexed citations
9.
Quintana, David, Francisco Chávez, Rafael Marcos Luque‐Baena, & Francisco Luna. (2018). Fuzzy techniques for IPO underpricing prediction. Journal of Intelligent & Fuzzy Systems. 35(1). 367–381. 3 indexed citations
10.
Quintana, David, et al.. (2018). Evolution of trading strategies with flexible structures: A configuration comparison. Neurocomputing. 331. 242–262. 3 indexed citations
11.
Quintana, David, et al.. (2014). Multiobjective Algorithms with Resampling for Portfolio Optimization. Computing and Informatics / Computers and Artificial Intelligence. 32(4). 777–796. 8 indexed citations
12.
Quintana, David, et al.. (2012). Time-stamped resampling for robust evolutionary portfolio optimization. Expert Systems with Applications. 39(12). 10722–10730. 12 indexed citations
13.
Quintana, David, et al.. (2009). Two-layered evolutionary forecasting for IPO underpricing. 2310. 2374–2378. 2 indexed citations
14.
Quintana, David & Pedro Isasi. (2008). Rendimiento en salidas a Bolsa:Un estudio mediante perceptronesmulticapa. Actualidad Contable FACES. 11(16). 78–88.
15.
Isasi, Pedro, et al.. (2007). APPLIED COMPUTATIONAL INTELLIGENCE FOR FINANCE AND ECONOMICS. Computational Intelligence. 23(2). 111–116. 4 indexed citations
16.
Quintana, David, et al.. (2007). Soft computing techniques applied to finance. Applied Intelligence. 29(2). 111–115. 32 indexed citations
17.
Sáez, Yago, et al.. (2007). Bidding with memory in the presence of synergies: a genetic algorithm implementation. e-Archivo (Carlos III University of Madrid). 23. 228–235. 1 indexed citations
18.
Quintana, David & Pedro Isasi. (2005). Estructura de Colocación y Rendimiento Inicial de Salidas a Bolsa: Tecnológicas Frente a No Tecnológicas*. Actualidad Contable FACES. 8(11). 80–86. 1 indexed citations
19.
Quintana, David, Pedro Isasi, & Ricardo Gimeno. (2005). Detección de inercia sectorial en salidas a bolsa mediante modelos arima y redes neuronales. e-Archivo (Carlos III University of Madrid). 42(65). 29–53.
20.
Quintana, David, et al.. (2005). Analysis of Ausubel Auctions by Means of Evolutionary Computation. e-Archivo (Carlos III University of Madrid). 3. 2645–2652. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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