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Bankruptcy Modelling of Indian Public Sector Banks: Evidence from Neural Trace

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  • Bikramaditya Ghosh

    (Institute of Management, Christ University, Bangalore, India)

Abstract

The paper estimates earnings per share (EPS) of top three Indian public sector banks on the basis of Ohlson O score, Zmijewski score and Graham Number, for a period of 12 years (2004-2015), with the help of the generalized method of moments (GMM), along with the use of an artificial neural network (ANN) algorithm. The time period has been carefully selected so that it could capture crash and consolidation phase, along with unprecedented bull rally too. It has been found that the fitment of ANN based model is accurate. Thus, using this model, their future EPS during distress could be predicted with a higher degree of precision. The authors believe this to illustrate a clear trace of the availability heuristic, timid choice, bold forecast and herding, as bulk deals by institutional investors decide the feat of a stock even on the futuristic possibility of bankruptcy.

Suggested Citation

  • Bikramaditya Ghosh, 2017. "Bankruptcy Modelling of Indian Public Sector Banks: Evidence from Neural Trace," International Journal of Applied Behavioral Economics (IJABE), IGI Global, vol. 6(2), pages 52-65, April.
    • Handle: RePEc:igg:jabe00:v:6:y:2017:i:2:p:52-65
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