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Neural network forecasts of Canadian stock returns using accounting ratios

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  • Olson, Dennis
  • Mossman, Charles

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  • Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
  • Handle: RePEc:eee:intfor:v:19:y:2003:i:3:p:453-465
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    References listed on IDEAS

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    1. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    2. Holthausen, Robert W. & Larcker, David F., 1992. "The prediction of stock returns using financial statement information," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 373-411, August.
    3. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    4. Ou, Jane A. & Penman, Stephen H., 1989. "Financial statement analysis and the prediction of stock returns," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 295-329, November.
    5. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
    6. Gorr, Wilpen L., 1994. "Editorial: Research prospective on neural network forecasting," International Journal of Forecasting, Elsevier, vol. 10(1), pages 1-4, June.
    7. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    8. Chatfield, Chris, 1993. "Neural networks: Forecasting breakthrough or passing fad?," International Journal of Forecasting, Elsevier, vol. 9(1), pages 1-3, April.
    9. Haefke, Christian & Helmenstein, Christian, 1995. "Forecasting Austrian IPOs: An Application of Linear and Neural Network Error-Correction Models," Economics Series 18, Institute for Advanced Studies.
    10. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    11. Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. Jordan French, 2016. "Back to the Future Betas: Empirical Asset Pricing of US and Southeast Asian Markets," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 4(3), pages 1-13, July.
    7. Leoni Eleni Oikonomikou, 2016. "Forecasting the Market Risk Premium with Artificial Neural Networks," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 202, Courant Research Centre PEG.
    8. 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.
    9. Leoni Eleni Oikonomikou, 2016. "Comparing the market risk premia forecasts in JSE and NYSE equity markets," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 203, Courant Research Centre PEG.
    10. 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.
    11. repec:eee:energy:v:151:y:2018:i:c:p:875-888 is not listed on IDEAS
    12. 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.
    13. ?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.
    14. 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.
    15. 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.
    16. Vinci Chow, 2017. "Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network," Papers 1701.08711, arXiv.org, revised Feb 2017.
    17. Masaya Abe & Hideki Nakayama, 2018. "Deep Learning for Forecasting Stock Returns in the Cross-Section," Papers 1801.01777, arXiv.org, revised Jun 2018.

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