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Construction of an EPS Predictor Model with 360 Degree Approach for the Pharmaceutical Industry


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1 Research Scholar, Institute of Management, Christ University, Bengaluru - 560 029, India

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With globalization, foreign countries are investing huge capital in India, making India as one of the largest producers of generic drugs in the world. The main objective of the study was to construct a multiple regression model which could predict the EPS of pharmaceutical companies in India. The model was constructed by selecting four pharma companies, namely Sun Pharmaceutical Ltd., Lupin Limited, Cipla, and Dr. Reddy's Laboratories. The study used generalized moment of methods (econometric technique) to predict the EPS of these companies with a 360 degree view by considering various variables like Altman Z score, Ohlson O score, Zmijewski's score, Graham's number, market price per share, profit after tax, retained earnings, and dividend yield. The model predicted EPS with an accuracy of 96.62%. A very high positive correlation was found between EPS and Zmikjewski score and a mild positive correlation was found between EPS and Graham's number, depicting that higher value investing yields higher EPS. Thus, Zmijewski model was the most suitable for prediction of bankruptcy of pharmaceutical companies. The study also showed that pharma companies issued more shares when they registered higher profits, thus decreasing the EPS value with increasing PAT. The study asserted that the suggested model will give an overall idea about the earnings an investor would yield when buying pharma stocks in India. The pharmaceutical sector was thus inferred to be stable to make investments and generate profits by speculating or holding securities.

Keywords

Bankruptcy, Altman Z Score, Ohlson O Score, Zmijewski Y Score, Graham's Number, EPS, MPS, DY, PAT, RE, Value Investing

G10, G11, G14

Paper Submission Date : May 16, 2017 ; Paper sent back for Revision : December 8, 2017 ; Paper Acceptance Date : December 28, 2017.

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  • Construction of an EPS Predictor Model with 360 Degree Approach for the Pharmaceutical Industry

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Authors

Uddepti Mandhyani
Research Scholar, Institute of Management, Christ University, Bengaluru - 560 029, India

Abstract


With globalization, foreign countries are investing huge capital in India, making India as one of the largest producers of generic drugs in the world. The main objective of the study was to construct a multiple regression model which could predict the EPS of pharmaceutical companies in India. The model was constructed by selecting four pharma companies, namely Sun Pharmaceutical Ltd., Lupin Limited, Cipla, and Dr. Reddy's Laboratories. The study used generalized moment of methods (econometric technique) to predict the EPS of these companies with a 360 degree view by considering various variables like Altman Z score, Ohlson O score, Zmijewski's score, Graham's number, market price per share, profit after tax, retained earnings, and dividend yield. The model predicted EPS with an accuracy of 96.62%. A very high positive correlation was found between EPS and Zmikjewski score and a mild positive correlation was found between EPS and Graham's number, depicting that higher value investing yields higher EPS. Thus, Zmijewski model was the most suitable for prediction of bankruptcy of pharmaceutical companies. The study also showed that pharma companies issued more shares when they registered higher profits, thus decreasing the EPS value with increasing PAT. The study asserted that the suggested model will give an overall idea about the earnings an investor would yield when buying pharma stocks in India. The pharmaceutical sector was thus inferred to be stable to make investments and generate profits by speculating or holding securities.

Keywords


Bankruptcy, Altman Z Score, Ohlson O Score, Zmijewski Y Score, Graham's Number, EPS, MPS, DY, PAT, RE, Value Investing

G10, G11, G14

Paper Submission Date : May 16, 2017 ; Paper sent back for Revision : December 8, 2017 ; Paper Acceptance Date : December 28, 2017.




DOI: https://doi.org/10.17010/ijrcm%2F2017%2Fv4%2Fi4%2F120920