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A variable impact neural network analysis of dividend policies and share prices of transportation and related companies

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  • Abdou, Hussein A.
  • Pointon, John
  • El-Masry, Ahmed
  • Olugbode, Moji
  • Lister, Roger J.

Abstract

The purpose of this research is to investigate dividend policy, including its impact on share prices of transportation providers and related service companies, by comparing generalized regression neural networks with conventional regressions. Our results using regressions reveal that for Europe and for the US and Canada the market-to-book-value, as a surrogate for growth opportunities, fulfils expectations of pressures on dividends leading to a negative association with dividend yields in accordance with the pecking order theory. Neural network analysis indicates a clear role for growth opportunities for the US and Canada pointing to an underlying confidence on the part of transportation companies in their own internal policies. Finally, risk is rewarded especially in Europe.

Suggested Citation

  • Abdou, Hussein A. & Pointon, John & El-Masry, Ahmed & Olugbode, Moji & Lister, Roger J., 2012. "A variable impact neural network analysis of dividend policies and share prices of transportation and related companies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 796-813.
    • Handle: RePEc:eee:intfin:v:22:y:2012:i:4:p:796-813
    DOI: 10.1016/j.intfin.2012.04.008
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    References listed on IDEAS

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    Cited by:

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    2. Hussein A. Abdou & Nouran N. Ellelly & Ahmed A. Elamer & Khaled Hussainey & Hassan Yazdifar, 2021. "Corporate governance and earnings management nexus: Evidence from the UK and Egypt using neural networks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6281-6311, October.
    3. Fatih Konak & Mehmet Akif Bülbül & Diler Türkoǧlu, 2024. "Feature Selection and Hyperparameters Optimization Employing a Hybrid Model Based on Genetic Algorithm and Artificial Neural Network: Forecasting Dividend Payout Ratio," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1673-1693, April.
    4. Şirin Özlem & Omer Faruk Tan, 2022. "Predicting cash holdings using supervised machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
    5. Abdou, Hussein A. & Elamer, Ahmed A. & Abedin, Mohammad Zoynul & Ibrahim, Bassam A., 2024. "The impact of oil and global markets on Saudi stock market predictability: A machine learning approach," Energy Economics, Elsevier, vol. 132(C).

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    More about this item

    Keywords

    Dividend yield; Retention; Market-to-book value; Neural networks; Transportation;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • F23 - International Economics - - International Factor Movements and International Business - - - Multinational Firms; International Business
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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