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Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC)

Author

Listed:
  • Yushen Kong

    (Jiangsu University)

  • Micheal Owusu-Akomeah

    (Jiangsu University)

  • Henry Asante Antwi

    (Jiangsu University)

  • Xuhua Hu

    (Jiangsu University)

  • Patrick Acheampong

    (Jiangsu University)

Abstract

Mutual fund investment continues to play a very important role in the world financial markets especially in developing economies where the capital market is not very matured and tolerant of small scale investors. The total mutual fund asset globally as at the end of 2016 was in excess of $40.4 trillion. Despite its success there are uncertainties as to whether mutual funds in Ghana obtain optimal performance relative to their counterparts in United States, Luxembourg, Ireland, France, Australia, United Kingdom, Japan, China and Brazil. We contribute to the extant literature on mutual fund performance evaluation using a collection of more sophisticated econometric models. We selected six continuous historical years that is 2010–2011, 2012–2013 and 2014–2015 to construct a mutual fund performance evaluation model utilizing the fast adaptive neural network classifier (FANNC), and to compare our results with those from an enhanced resilient back propagation neural networks (ERBPNN) model. Our FANNC model outperformed the existing models in terms of processing time and error rate. This makes it ideal for financial application that involves large volume of data and routine updates.

Suggested Citation

  • Yushen Kong & Micheal Owusu-Akomeah & Henry Asante Antwi & Xuhua Hu & Patrick Acheampong, 2019. "Evaluation of the robusticity of mutual fund performance in Ghana using Enhanced Resilient Backpropagation Neural Network (ERBPNN) and Fast Adaptive Neural Network Classifier (FANNC)," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-12, December.
    • Handle: RePEc:spr:fininn:v:5:y:2019:i:1:d:10.1186_s40854-019-0125-5
    DOI: 10.1186/s40854-019-0125-5
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