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Stock index forecasting based on a hybrid model

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Cited by:

  1. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
  2. Saeed Shavvalpour & Hossein Khanjarpanah & Farhad Zamani & Armin Jabbarzadeh, 2017. "Petrochemical Products Market and Stock Market Returns: Empirical Evidence from Tehran Stock Exchange," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 21(2), pages 383-403, Spring.
  3. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
  4. Mahla Nikou & Gholamreza Mansourfar & Jamshid Bagherzadeh, 2019. "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 164-174, October.
  5. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
  6. Mohammad Almasarweh & S. AL Wadi, 2018. "ARIMA Model in Predicting Banking Stock Market Data," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 309-309, November.
  7. Martha Cecilia García & Aura María Jalal & Luis Alfonso Garzón & Jorge Mario López, 2013. "Métodos para predecir índices Bursátiles," Revista Ecos de Economía, Universidad EAFIT, December.
  8. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
  9. Lihki Rubio & Keyla Alba, 2022. "Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
  10. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Creating Investment Scheme with State Space Modeling," CARF F-Series cf406, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  11. Longsheng Cheng & Mahboubeh Shadabfar & Arash Sioofy Khoojine, 2023. "A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets," Mathematics, MDPI, vol. 11(5), pages 1-34, February.
  12. Maya Malinda & Jo-Hui Chen, 2022. "The forecasting of consumer exchange-traded funds (ETFs) via grey relational analysis (GRA) and artificial neural network (ANN)," Empirical Economics, Springer, vol. 62(2), pages 779-823, February.
  13. Akhter Mohiuddin Rather & V. N. Sastry & Arun Agarwal, 2017. "Stock market prediction and Portfolio selection models: a survey," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 558-579, September.
  14. Mohammad Arashi & Mohammad Mahdi Rounaghi, 2022. "Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.
  15. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
  16. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
  17. Gu Pang & Bartosz Gebka, 2017. "Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2454-2469, May.
  18. 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.
  19. Gourav Kumar & Uday Pratap Singh & Sanjeev Jain, 2022. "Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 991-1039, October.
  20. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
  21. Hajirahimi, Zahra & Khashei, Mehdi, 2022. "Series Hybridization of Parallel (SHOP) models for time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
  22. Liu, Longlong & Zhou, Suyu & Jie, Qian & Du, Pei & Xu, Yan & Wang, Jianzhou, 2024. "A robust time-varying weight combined model for crude oil price forecasting," Energy, Elsevier, vol. 299(C).
  23. Chen, Wei & Zhang, Haoyu & Jia, Lifen, 2022. "A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
  24. Dushmanta Kumar Padhi & Neelamadhab Padhy & Akash Kumar Bhoi & Jana Shafi & Muhammad Fazal Ijaz, 2021. "A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators," Mathematics, MDPI, vol. 9(21), pages 1-31, October.
  25. Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.
  26. Li Xiangfei & Zhang Zaisheng & Huang Chao, 2014. "An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm," Journal of Systems Science and Information, De Gruyter, vol. 2(6), pages 481-504, December.
  27. Nazarian, Rafik & Gandali Alikhani, Nadiya & Naderi, Esmaeil & Amiri, Ashkan, 2013. "Forecasting Stock Market Volatility: A Forecast Combination Approach," MPRA Paper 46786, University Library of Munich, Germany.
  28. Ruiz-Aguilar, J.J. & Turias, I.J. & Jiménez-Come, M.J., 2014. "Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 1-13.
  29. Huseyin INCE & Theodore B. TRAFALİS, 2017. "A Hybrid Forecasting Model for Stock Market Prediction," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(3), pages 263-280.
  30. Huang, Dong & Grifoll, Manel & Sanchez-Espigares, Jose A. & Zheng, Pengjun & Feng, Hongxiang, 2022. "Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic," Transport Policy, Elsevier, vol. 128(C), pages 1-12.
  31. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
  32. Lamichhane, Sabhyata & Mei, Bin & Siry, Jacek, 2023. "Forecasting pine sawtimber stumpage prices: A comparison between a time series hybrid model and an artificial neural network," Forest Policy and Economics, Elsevier, vol. 154(C).
  33. Mahdi Moradi & Mehdi Jabbari Nooghabi & Mohammad Mahdi Rounaghi, 2021. "Investigation of fractal market hypothesis and forecasting time series stock returns for Tehran Stock Exchange and London Stock Exchange," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 662-678, January.
  34. Doyle, John R. & Chen, Catherine H., 2013. "Patterns in stock market movements tested as random number generators," European Journal of Operational Research, Elsevier, vol. 227(1), pages 122-132.
  35. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Creating Investment Scheme with State Space Modeling ," CIRJE F-Series CIRJE-F-1038, CIRJE, Faculty of Economics, University of Tokyo.
  36. Huijian Dong & Xiaomin Guo & Han Reichgelt & Ruizhi Hu, 2020. "Predictive power of ARIMA models in forecasting equity returns: a sliding window method," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 549-566, October.
  37. Hüseyin İlker Erçen & Hüseyin Özdeşer & Turgut Türsoy, 2022. "The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
  38. Daniel Musafiri Balungu & Avinash Kumar, 2024. "Forecasting The Economic Growth of Sverdlovsk Region: A Comparative Analysis of Machine Learning, Linear Regression and Autoregressive Models," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(3), pages 674-695.
  39. Hajirahimi, Zahra & Khashei, Mehdi & Etemadi, Sepideh, 2022. "A novel class of reliability-based parallel hybridization (RPH) models for time series forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
  40. Mehdi Khashei & Zahra Hajirahimi, 2017. "Performance evaluation of series and parallel strategies for financial time series forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-24, December.
  41. Svetunkov, Ivan & Kourentzes, Nikolaos, 2015. "Complex Exponential Smoothing," MPRA Paper 69394, University Library of Munich, Germany.
  42. Heng-Li Yang & Han-Chou Lin, 2017. "Applying the Hybrid Model of EMD, PSR, and ELM to Exchange Rates Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 99-116, January.
  43. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
  44. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
  45. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
  46. Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.
  47. S. AL Wadi & Mohammad Almasarweh & Ahmed Atallah Alsaraireh, 2018. "Predicting Closed Price Time Series Data Using ARIMA Model," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 181-181, November.
  48. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
  49. Chen, Wei & Xu, Huilin & Jia, Lifen & Gao, Ying, 2021. "Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants," International Journal of Forecasting, Elsevier, vol. 37(1), pages 28-43.
  50. Uddin, Ajim & Yu, Dantong, 2020. "Latent factor model for asset pricing," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
  51. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
  52. Hakan Er & Adnan Hushmat, 2017. "The application of technical trading rules developed from spot market prices on futures market prices using CAPM," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 7(3), pages 313-353, December.
  53. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
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