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Neural network forecasts of Canadian stock returns using accounting ratios

Citations

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Cited by:

  1. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
  2. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
  3. I-Cheng Yeh, 2023. "Synergy frontier of multi-factor stock selection model," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 445-480, March.
  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. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.
  6. Sergey SVESHNIKOV & Victor BOCHARNIKOV, 2009. "Eforecasting Financial Indexes With Model Of Composite Events Influence," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 4(3(9)_Fall).
  7. Ayşegül PEKER & Duygu TUNALI, 2025. "The Comparison of Artificial Neural Networks and Panel Data Analysis on Profitability Prediction: The Case of Real Estate Investment Trusts," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 10(1), pages 160-183.
  8. Hakan Pabuccu & Adrian Barbu, 2024. "Feature selection with annealing for forecasting financial time series," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.
  9. Huang, Lili & Wang, Jun, 2018. "Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network," Energy, Elsevier, vol. 151(C), pages 875-888.
  10. Wang, Jie & Wang, Jun, 2016. "Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations," Energy, Elsevier, vol. 102(C), pages 365-374.
  11. Yu-Min Lian & Jia-Ling Chen & Hsueh-Chien Cheng, 2022. "Predicting Bitcoin Prices via Machine Learning and Time Series Models," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(5), pages 1-2.
  12. Chia-Cheng Chen & Chun-Hung Chen & Ting-Yin Liu, 2020. "Investment Performance of Machine Learning: Analysis of S&P 500 Index," International Journal of Economics and Financial Issues, Econjournals, vol. 10(1), pages 59-66.
  13. Onur Enginar & Kazim Baris Atici, 2022. "Optimal forecast error as an unbiased estimator of abnormal return: A proposition," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 158-166, January.
  14. ?enol Emir & Hasan Din?er & Mehpare Timor, 2012. "A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 106-122, August.
  15. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
  16. Joerg Osterrieder & Daniel Kucharczyk & Silas Rudolf & Daniel Wittwer, 2020. "Neural networks and arbitrage in the VIX," Digital Finance, Springer, vol. 2(1), pages 97-115, September.
  17. Shuyun Ren & Hau-Ling Chan & Tana Siqin, 2020. "Demand forecasting in retail operations for fashionable products: methods, practices, and real case study," Annals of Operations Research, Springer, vol. 291(1), pages 761-777, August.
  18. Faizal Hafiz & Jan Broekaert & Davide Torre & Akshya Swain, 2024. "A multi-criteria approach to evolve sparse neural architectures for stock market forecasting," Annals of Operations Research, Springer, vol. 336(1), pages 1219-1263, May.
  19. Giovanni Ballarin & Jacopo Capra & Petros Dellaportas, 2025. "Multi-Horizon Echo State Network Prediction of Intraday Stock Returns," Papers 2504.19623, arXiv.org.
  20. Hakob GRIGORYAN, 2015. "Stock Market Prediction using Artificial Neural Networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(2), pages 14-23, October.
  21. 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.
  22. Jordan French, 2016. "Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets," IJFS, MDPI, vol. 4(3), pages 1-13, July.
  23. Chaima Kooli & Raoudha Trabelsi & Fethi Tlili, 2018. "The Impact of Accounting Disclosure On Emerging Stock Market Prediction in an Unstable Socio-Political Context," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 17(3), pages 313-329, September.
  24. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
  25. Yoshiyuki Suimon & Hiroki Sakaji & Kiyoshi Izumi & Hiroyasu Matsushima, 2020. "Autoencoder-Based Three-Factor Model for the Yield Curve of Japanese Government Bonds and a Trading Strategy," JRFM, MDPI, vol. 13(4), pages 1-21, April.
  26. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
  27. Zhengxin Joseph Ye & Bjorn W. Schuller, 2020. "Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning," Papers 2009.03094, arXiv.org.
  28. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
  29. Rounaghi, Mohammad Mahdi & Abbaszadeh, Mohammad Reza & Arashi, Mohammad, 2015. "Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 625-633.
  30. Alexandre Silva Oliveira & Paulo Sergio Ceretta & Daniel Pastorek, 2025. "Correction to: An experiment with ANNs and Long‑Tail Probability Ranking to Obtain Portfolios with Superior Returns," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 1855-1855, April.
  31. Chopra, Ritika & Sharma, Gagan Deep & Pereira, Vijay, 2024. "Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction," Technovation, Elsevier, vol. 135(C).
  32. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
  33. Chin-Sheng Huang & Yi-Sheng Liu, 2019. "Machine Learning on Stock Price Movement Forecast: The Sample of the Taiwan Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 189-201.
  34. Dhaoui, Abderrazak & Audi, Mohamed & Ouled Ahmed Ben Ali, Raja, 2015. "Revising empirical linkages between direction of Canadian stock price index movement and Oil supply and demand shocks: Artificial neural network and support vector machines approaches," MPRA Paper 66029, University Library of Munich, Germany.
  35. Tseng-Chung Tang, 2010. "Effects of announcements of reorganization outcome," Applied Economics, Taylor & Francis Journals, vol. 42(9), pages 1113-1124.
  36. Tania Morris & Jules Comeau, 2020. "Portfolio creation using artificial neural networks and classification probabilities: a Canadian study," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 133-163, June.
  37. Vinci Chow, 2017. "Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network," Papers 1701.08711, arXiv.org, revised Oct 2019.
  38. Masaya Abe & Hideki Nakayama, 2018. "Deep Learning for Forecasting Stock Returns in the Cross-Section," Papers 1801.01777, arXiv.org, revised Jun 2018.
  39. Brad S. Trinkle, 2005. "Forecasting annual excess stock returns via an adaptive network‐based fuzzy inference system," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(3), pages 165-177, July.
  40. Erol Eğrioğlu & Robert Fildes, 2022. "A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1355-1383, April.
  41. Chia-Cheng Chen & Yisheng Liu & Ting-Hsin Hsu, 2019. "An Analysis on Investment Performance of Machine Learning: An Empirical Examination on Taiwan Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 1-10.
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