Christian Gonzalez-Martel

Personal Details

First Name:	Christian
Middle Name:
Last Name:	Gonzalez-Martel
Suffix:
RePEc Short-ID:	pgo66
	Universidad de Las Palmas de Gran Canaria Edif. Dep. de Ciencias Económicas y Empresariales Campus se Tafira Modulo D Despacho 4.07 Las Palmas de Gran Canaria 35015 Las Palmas
	+34 928 458220

Affiliation

Departamento de Métodos Cuantitativos en la Economía y la Gestión
Facultad de Economía, Empresa y Turismo
Universidad de las Palmas de Gran Canaria

Las Palmas, Spain
http://www.ulpgc.es/index.php?pagina=dmc&ver=inicio
RePEc:edi:dmlpges (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Fernando Fernández-Rodríguez & Christian González-Martel* & Simón Sosvilla-Rivero, "undated". "On the profitability of technical trading rules based on arifitial neural networks : evidence from the Madrid stock market," Working Papers 99-07, FEDEA.
   • Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.

   Cited by:

   1. James J. Kung & Wing‐Keung Wong, 2009. "Efficiency Of The Taiwan Stock Market," The Japanese Economic Review, Japanese Economic Association, vol. 60(3), pages 389-394, September.
   2. Mariano Matilla-Garcia, 2006. "Are trading rules based on genetic algorithms profitable?," Applied Economics Letters, Taylor & Francis Journals, vol. 13(2), pages 123-126.
   3. Jacinta Chan Phooi M’ng & Ham Yi Jer, 2021. "Do economic statistics contain information to predict stock indexes futures prices and returns? Evidence from Asian equity futures markets," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1033-1060, October.
   4. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
   5. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 14645, University Library of Munich, Germany.
   6. Carl Chiarella & Xue-Zhong He & Cars Hommes, 2004. "A Dynamic Analysis of Moving Average Rules," Research Paper Series 133, Quantitative Finance Research Centre, University of Technology, Sydney.
      • Chiarella, Carl & He, Xue-Zhong & Hommes, Cars, 2006. "A dynamic analysis of moving average rules," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1729-1753.
      • Cars Hommes & Carl Chiarella & Xue-Zhong He, 2004. "A Dynamical Analysis of Moving Average Rules," Computing in Economics and Finance 2004 238, Society for Computational Economics.
      • Chiarella, C. & He, X.-Z. & Hommes, C.H., 2004. "A Dynamic Analysis of Moving Average Rules," CeNDEF Working Papers 04-14, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
      • Carl Chiarella & Tony He & Cars H. Hommes, 2005. "A Dynamic Analysis of Moving Average Rules," Tinbergen Institute Discussion Papers 05-057/1, Tinbergen Institute.
   7. Stephan Schulmeister, 2000. "Technical Analysis and Exchange Rate Dynamics," WIFO Studies, WIFO, number 25857, March.
   8. Mansoor Ahmed & Anirudh Sriram & Sanjay Singh, 2020. "Short Term Firm-Specific Stock Forecasting with BDI Framework," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 745-778, March.
   9. Schulmeister, Stephan, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, Elsevier, vol. 18(4), pages 190-201, October.
      • Stephan Schulmeister, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, John Wiley & Sons, vol. 18(4), pages 190-201, October.
      • Stephan Schulmeister, 2007. "The Profitability of Technical Stock Trading has Moved from Daily to Intraday Data," WIFO Working Papers 289, WIFO.
      • Stephan Schulmeister, 2008. "Profitability of Technical Stock Trading: Has it Moved from Daily to Intraday Data?," WIFO Working Papers 323, WIFO.
   10. Julián Andrada-Félix & Fernando Fernández-Rodríguez & María Dolores García-Artiles & Simón Sosvilla-Rivero, "undated". "An Empirical Evaluation of Non-Linear Trading Rules," Working Papers 2001-16, FEDEA.
       • Andrada-Félix Julián & Fernadez-Rodriguez Fernando & Garcia-Artiles Maria-Dolores & Sosvilla-Rivero Simon, 2003. "An Empirical Evaluation of Non-Linear Trading Rules," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(3), pages 1-32, October.
   11. He, Xue-Zhong & Zheng, Min, 2010. "Dynamics of moving average rules in a continuous-time financial market model," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 615-634, December.
       • Xue-Zhong He & Min Zheng, 2010. "Dynamics of Moving Average Rules in a Continuous-time Financial Market Model," Research Paper Series 268, Quantitative Finance Research Centre, University of Technology, Sydney.
   12. Phooi M’ng, Jacinta Chan, 2018. "Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 336-345.
   13. Po-Hsuan Hsu & Chung-Ming Kuan, 2004. "Re-Examining the Profitability of Technical Analysis with White’s Reality Check," IEAS Working Paper : academic research 04-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.
   14. Ka Po Kung, 2022. "Efficiency of the Stock Markets after the 2008 Financial Crisis: Evidence from the Four Asian Dragons," Eurasian Journal of Business and Management, Eurasian Publications, vol. 10(2), pages 101-115.
   15. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
   16. Bill Cai & Charlie Cai & Kevin Keasey, 2005. "Market Efficiency and Returns to Simple Technical Trading Rules: Further Evidence from U.S., U.K., Asian and Chinese Stock Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 12(1), pages 45-60, March.
   17. Erdinc Akyildirim & Aurelio F. Bariviera & Duc Khuong Nguyen & Ahmet Sensoy, 2022. "Forecasting high-frequency stock returns: a comparison of alternative methods," Annals of Operations Research, Springer, vol. 313(2), pages 639-690, June.
   18. Chang, Eui Jung & Lima, Eduardo Jose Araujo & Tabak, Benjamin Miranda, 2004. "Testing for predictability in emerging equity markets," Emerging Markets Review, Elsevier, vol. 5(3), pages 295-316, September.
   19. Julián Andrada Félix & Fernando Fernández Rodríguez & María Dolores García Artiles, 2004. "Non-linear trading rules in the New York Stock Exchange," Documentos de trabajo conjunto ULL-ULPGC 2004-05, Facultad de Ciencias Económicas de la ULPGC.
   20. Kang, Haijun & Zong, Xiangyu & Wang, Jianyong & Chen, Haonan, 2023. "Binary gravity search algorithm and support vector machine for forecasting and trading stock indices," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 507-526.
   21. Hsu, Pao-Peng & Liao, Szu-Lang, 2012. "The portfolio strategy and hedging: A spectrum perspective on mean–variance theory," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 129-140.
   22. S. D. Bekiros & D. A. Georgoutsos, 2008. "Direction-of-change forecasting using a volatility-based recurrent neural network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 407-417.
       • Bekiros, S. & Georgoutsos, D., 2006. "Direction-of-Change Forecasting using a Volatility- Based Recurrent Neural Network," CeNDEF Working Papers 06-16, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
   23. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
   24. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
   25. Metghalchi, Massoud & Chen, Chien-Ping & Hayes, Linda A., 2015. "History of share prices and market efficiency of the Madrid general stock index," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 178-184.
   26. Kwang-il Choe & Joshua Krausz & Kiseok Nam, 2011. "Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 36(3), pages 323-353, April.
   27. Song, Yu & Akagi, Fumio, 2016. "Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock marketAuthor-Name: Qiu, Mingyue," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 1-7.
   28. Jacinta Chan Phooi M'ng & Azmin Azliza Aziz, 2016. "Using Neural Networks to Enhance Technical Trading Rule Returns: A Case with KLCI," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 2(1), pages 63-70, January.
   29. Frey, Ulrich J. & Rusch, Hannes, 2014. "Modeling Ecological Success of Common Pool Resource Systems Using Large Datasets," World Development, Elsevier, vol. 59(C), pages 93-103.
   30. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
   31. Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Papers 0901.0447, arXiv.org.
   32. Ozgur Ican & Taha Bugra Celik, 2017. "Stock Market Prediction Performance of Neural Networks: A Literature Review," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(11), pages 100-108, November.
   33. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
   34. Bekiros, Stelios D., 2010. "Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1153-1170, June.
   35. M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
       • Ali Choudhary & Adnan Haider, 2008. "Neural Network Models for Inflation Forecasting: An Appraisal," School of Economics Discussion Papers 0808, School of Economics, University of Surrey.
       • M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
       • M. Ali Choudhary, 2011. "Neural Network Models for Inflation Forecasting: An Appraisal," Post-Print hal-00704670, HAL.
   36. Roy Hayes & Jingwei Wu & Ruijra Chaysiri & Jean Bae & Peter Beling & William Scherer, 2016. "Effects of time horizon and asset condition on the profitability of technical trading rules," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(1), pages 41-59, January.
   37. Shigeo Kamitsuji & Ritei Shibata, 2003. "Effectiveness of Stochastic Neural Network for Prediction of Fall or Rise of TOPIX," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 10(2), pages 187-204, September.
   38. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
   39. Shambora, William E. & Rossiter, Rosemary, 2007. "Are there exploitable inefficiencies in the futures market for oil?," Energy Economics, Elsevier, vol. 29(1), pages 18-27, January.
   40. Bekiros, Stelios D., 2010. "Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets," European Journal of Operational Research, Elsevier, vol. 202(1), pages 285-293, April.
   41. Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
   42. Yu-Lieh Huang, 2009. "Identifying turbulent and calm regimes in stock prices: evidence from the Taiwan stock market," Applied Economics Letters, Taylor & Francis Journals, vol. 16(14), pages 1477-1481.
   43. Jacinta Chan Phooi M’ng & Mohammadali Mehralizadeh, 2016. "Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-29, June.
   44. Bokhari, Jawaad & Cai, Charlie & Hudson, Robert & Keasey, Kevin, 2005. "The predictive ability and profitability of technical trading rules: does company size matter?," Economics Letters, Elsevier, vol. 86(1), pages 21-27, January.
   45. Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.
   46. Chen, Rui & Ren, Jinjuan, 2022. "Do AI-powered mutual funds perform better?," Finance Research Letters, Elsevier, vol. 47(PA).
   47. Michael D. McKenzie, 2007. "Technical Trading Rules in Emerging Markets and the 1997 Asian Currency Crises," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 43(4), pages 46-73, August.
   48. Jiali Fang & Ben Jacobsen & Yafeng Qin, 2014. "Predictability of the simple technical trading rules: An out‐of‐sample test," Review of Financial Economics, John Wiley & Sons, vol. 23(1), pages 30-45, January.
       • Fang, Jiali & Jacobsen, Ben & Qin, Yafeng, 2014. "Predictability of the simple technical trading rules: An out-of-sample test," Review of Financial Economics, Elsevier, vol. 23(1), pages 30-45.
   49. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.
   50. Kerda Varaku, 2019. "Stock Price Forecasting and Hypothesis Testing Using Neural Networks," Papers 1908.11212, arXiv.org.
   51. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
   52. Bekiros, Stelios D., 2013. "Irrational fads, short-term memory emulation, and asset predictability," Review of Financial Economics, Elsevier, vol. 22(4), pages 213-219.
       • Stelios D. Bekiros, 2013. "Irrational fads, short‐term memory emulation, and asset predictability," Review of Financial Economics, John Wiley & Sons, vol. 22(4), pages 213-219, November.
   53. Mariano Matilla-Garcia & Carlos Arguello, 2005. "A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market," Applied Economics Letters, Taylor & Francis Journals, vol. 12(5), pages 303-308.
   54. Paulo Vitor Campos Souza & Luiz Carlos Bambirra Torres, 2021. "Extreme Wavelet Fast Learning Machine for Evaluation of the Default Profile on Financial Transactions," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1263-1285, April.
   55. Marcos Alvarez Díaz & Lucy Amigo Dobano & Francisco Rodríguez de Prado, "undated". "Taxing on Housing: A Welfare Evaluation of the Spanish Personal Income Tax," Studies on the Spanish Economy 142, FEDEA.
   56. Jorge Perez-Rodriguez & Salvador Torra & Julian Andrada-Felix, 2005. "Are Spanish Ibex35 stock future index returns forecasted with non-linear models?," Applied Financial Economics, Taylor & Francis Journals, vol. 15(14), pages 963-975.
   57. Nam, Kiseok & Washer, Kenneth M. & Chu, Quentin C., 2005. "Asymmetric return dynamics and technical trading strategies," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 391-418, February.
   58. Yasemin Deniz Akarım, 2013. "A Comparison of Linear and Nonlinear Models in Forecasting Market Risk: The Evidence from Turkish Derivative Exchange," Journal of Economics and Behavioral Studies, AMH International, vol. 5(3), pages 164-172.
   59. Fabin Shi & Xiao-Qian Sun & Jinhua Gao & Zidong Wang & Hua-Wei Shen & Xue-Qi Cheng, 2021. "The prediction of fluctuation in the order-driven financial market," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-15, November.
   60. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
   61. Marcos Álvarez-Díaz & Lucy Amigo Dobaño, 2003. "Métodos No-Lineales De Predicción En El Mercado De Valores Tecnológicos En España. Una Verificación De La Hipótesis Débil De Eficiencia," Working Papers 0303, Universidade de Vigo, Departamento de Economía Aplicada.
   62. Alireza Namdari & Tariq S. Durrani, 2021. "A Multilayer Feedforward Perceptron Model in Neural Networks for Predicting Stock Market Short-term Trends," SN Operations Research Forum, Springer, vol. 2(3), pages 1-30, September.
   63. Stelios Bekiros, 2007. "A neurofuzzy model for stock market trading," Applied Economics Letters, Taylor & Francis Journals, vol. 14(1), pages 53-57.

2. Fernando Fernández-Rodríguez & Christian González-Martel & Simón Sosvilla-Rivero, "undated". "Optimisation of Technical Rules by Genetic Algorithms: Evidence from the Madrid Stock Market," Working Papers 2001-14, FEDEA.
   • Fernando Fernandez-Rodriguez & Christian Gonzalez-Martel & Simon Sosvilla-Rivero, 2005. "Optimization of technical rules by genetic algorithms: evidence from the Madrid stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 15(11), pages 773-775.

   Cited by:

   1. Kaucic, Massimiliano, 2010. "Investment using evolutionary learning methods and technical rules," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1717-1727, December.
   2. Schulmeister, Stephan, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, Elsevier, vol. 18(4), pages 190-201, October.
      • Stephan Schulmeister, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, John Wiley & Sons, vol. 18(4), pages 190-201, October.
      • Stephan Schulmeister, 2007. "The Profitability of Technical Stock Trading has Moved from Daily to Intraday Data," WIFO Working Papers 289, WIFO.
      • Stephan Schulmeister, 2008. "Profitability of Technical Stock Trading: Has it Moved from Daily to Intraday Data?," WIFO Working Papers 323, WIFO.
   3. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.

Articles

1. Fernando Fernandez-Rodriguez & Christian Gonzalez-Martel & Simon Sosvilla-Rivero, 2005. "Optimization of technical rules by genetic algorithms: evidence from the Madrid stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 15(11), pages 773-775.
   • Fernando Fernández-Rodríguez & Christian González-Martel & Simón Sosvilla-Rivero, "undated". "Optimisation of Technical Rules by Genetic Algorithms: Evidence from the Madrid Stock Market," Working Papers 2001-14, FEDEA.
   See citations under working paper version above.
2. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
   • Fernando Fernández-Rodríguez & Christian González-Martel* & Simón Sosvilla-Rivero, "undated". "On the profitability of technical trading rules based on arifitial neural networks : evidence from the Madrid stock market," Working Papers 99-07, FEDEA.
   See citations under working paper version above.Sorry, no citations of articles recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-CMP: Computational Economics (1) 2001-10-16
2. NEP-ETS: Econometric Time Series (1) 1999-12-14
3. NEP-EVO: Evolutionary Economics (1) 2001-10-16
4. NEP-FIN: Finance (1) 1999-12-14
5. NEP-IND: Industrial Organization (1) 1999-12-14

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