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Adaptive Market Hypothesis and Artificial Neural Networks: Evidence from Pakistan

Author

Listed:
  • Sehrish Kayani

    (PhD Scholar,Department of Management Sciences, NUML University, Islamabad, Pakistan.)

  • Usman Ayub

    (Assistant Professor,Department of Management Sciences,COMSATS University, Islamabad, Pakistan.)

  • Imran Abbas Jadoon

    (Assistant Professor,Department of Management Sciences,COMSATS University, Islamabad, Pakistan.)

Abstract

The debate covering stock return predictability is now shifted towards the investigation of changing patterns of return predictability as suggested by the adaptive market hypothesis (AMH). The present article inspects the varying return predictability pertaining to the equity market in Pakistan under AMH framework. A nonlinear autoregressive neural network (NARNN) model is employed to investigate the nonlinear dependency of returns over a period of eighteen years. NARNN is a robust and flexible technique that is free from any restrictive assumptions. Under a rolling window framework, the repeating patterns of predictability and unpredictability are observed. This finding confirms the idea of AMH.

Suggested Citation

  • Sehrish Kayani & Usman Ayub & Imran Abbas Jadoon, 2019. "Adaptive Market Hypothesis and Artificial Neural Networks: Evidence from Pakistan," Global Regional Review, Humanity Only, vol. 4(2), pages 190-203, June.
    • Handle: RePEc:aaw:grrjrn:v:4:y:2019:i:2:p:190-203
    DOI: 10.31703/grr.2019(IV-II).21
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    More about this item

    Keywords

    Adaptive Market Hypothesis; Efficient Market Hypothesis; Artificial Neural Network; Rolling Window Analysis;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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