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Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks

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  • Eakins, Stanley G.
  • Stansell, Stanley R.

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  • Eakins, Stanley G. & Stansell, Stanley R., 2003. "Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks," International Review of Financial Analysis, Elsevier, vol. 12(1), pages 83-97.
    • Handle: RePEc:eee:finana:v:12:y:2003:i:1:p:83-97
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    1. Lakonishok, Josef & Shleifer, Andrei & Vishny, Robert W, 1994. "Contrarian Investment, Extrapolation, and Risk," Journal of Finance, American Finance Association, vol. 49(5), pages 1541-1578, December.
    2. Michael G. Ferri & H. Dennis Oberhelman & Rodney L. Roenfeldt, 1984. "Market Timing And Mutual Fund Portfolio Composition," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 7(2), pages 143-150, June.
    3. Walker, M. Mark & Hatfield, Gay B., 1996. "Professional stock analysts' recommendations: Implications for individual investors," Financial Services Review, Elsevier, vol. 5(1), pages 13-29.
    4. Dellva, Wilfred L & Olson, Gerard T, 1998. "The Relationship between Mutual Fund Fees and Expenses and Their Effects on Performance," The Financial Review, Eastern Finance Association, vol. 33(1), pages 85-103, February.
    5. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    6. repec:bla:jfinan:v:44:y:1989:i:1:p:135-48 is not listed on IDEAS
    7. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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    Cited by:

    1. Pei En Lee, 2019. "The Empirical Study of Investor Sentiment on Stock Return Prediction," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 119-124.
    2. Xinyue Cui & Zhaoyu Xu & Yue Zhou, 2020. "Using Machine Learning to Forecast Future Earnings," Papers 2005.13995, arXiv.org.
    3. Craig Ellis & Patrick J. Wilson & Ralf Zurbruegg, 2007. "Real Estate ‘Value’ Stocks and International Diversification," Journal of Property Research, Taylor & Francis Journals, vol. 24(3), pages 265-287, September.
    4. Panagiotis Papaioannou & Thomas Dionysopoulos & Dietmar Janetzko & Constantinos Siettos, 2016. "S&P500 Forecasting and Trading using Convolution Analysis of Major Asset Classes," Papers 1612.04370, arXiv.org.
    5. Vukovic, Darko & Vyklyuk, Yaroslav & Matsiuk, Natalia & Maiti, Moinak, 2020. "Neural network forecasting in prediction Sharpe ratio: Evidence from EU debt market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    6. Pei-Fen Tsai & Cheng-Han Gao & Shyan-Ming Yuan, 2023. "Stock Selection Using Machine Learning Based on Financial Ratios," Mathematics, MDPI, vol. 11(23), pages 1-18, November.
    7. Amit Hedau, 2020. "Value Investing: Evidence From Listed Construction And Infrastucture Sector Companies In India," Romanian Economic Business Review, Romanian-American University, vol. 15(4), pages 104-114, december.
    8. 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.
    9. Smimou, K. & Bector, C.R. & Jacoby, G., 2007. "A subjective assessment of approximate probabilities with a portfolio application," Research in International Business and Finance, Elsevier, vol. 21(2), pages 134-160, June.

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