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Interrelationship Between Macroeconomic Fundamentals and Sector-Specific Stock Indices of the Indian Manufacturing Sector

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Indian Manufacturing Sector in Post-Reform Period

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Abstract

This chapter focuses on the dynamic relationship between the select manufacturing sector-specific stock market indices and the various macroeconomic factors. The long-run and short-run interrelation between the indices and the macroeconomic variables was accomplished using Auto Regressive Distributed Lag framework. The outcome of the study showed the existence of both short-run and long-run relationships. The effect of all the macroeconomic variables was not found to be statistically significant for all the sector-specific indices. However, economic policy uncertainty, foreign portfolio investment ratio and price factor emerged as the most sensitive macroeconomic variables affecting the selected sector-specific indices during the period under study.

This chapter was originally published in Management and Labour Studies, Vol. 45, Issue 3 Copyright 2020 © XLRI Jamshedpur, School of Business Management & Human Resources. All rights reserved. Reproduced with the permission of the copyright holders and the publishers, SAGE Publications India Pvt. Ltd., New Delhi.

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Notes

  1. 1.

    The model can be expressed in the following form:

    $${P}_{0}=\frac{{D}_{1}}{{\left(1+{\mathrm{K}}_{\mathrm{e}}\right)}^{1}}+\frac{{D}_{2}}{{\left(1+{{K}}_{{e}}\right)}^{2}}+\dots +\frac{{D}_{n}+{P}_{n}}{{\left(1+{{K}}_{{e}}\right)}^{\mathrm{n}}}$$

    .

    where, P0 is the price of the share at time t0, D represents the expected dividends at different time period ranging from 1 to n, Pn is the terminal price at nth period and Ke is the appropriate discounting rate.

  2. 2.

    \(\text{FPIR}= \frac{\text{Foreign} \,\text{Portfolio}\, \text{Purchase}}{\text{Foreign}\, \text{Portfolio}\, \text{Sales}}\left(\mathrm{Vardhan } \, \& \, \text{Sinha}, 2014\right)\).

    \(\text{FPIR}>1\), it indicates the inflow and \(\text{FPIR}<1\), it implies outflow.

  3. 3.

    A series is said to be stationary if the mean and covariance are constant overtime and the auto-covariance of the series depends only on the lag between two time periods-not the actual time at which the covariance is computed.

  4. 4.

    The trial and error process was used in selection of maximum lag for the ARDL model. For example initially for both the dependent and independent variables the lag was specified as (1,1), then in the next instance lag of (1,2) then (1,3). In this way all the possible combinations up to (5,5) are considered. Optimum lag guided by the lowest possible of value of AIC. Thus different sector-specific ARDL model had different optimal lag length.

  5. 5.

    http://pharmaceuticals.gov.in/industry-news/archieve/2323; http://pharmaceuticals.gov.in/sites/default/files/pg204.pdf.

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Authors and Affiliations

  1. Department of Commerce, The University of Burdwan, Burdwan, West Bengal, India

    Sumit Kumar Maji

  2. Department of Commerce, The University of Burdwan, Burdwan, West Bengal, India

    Arindam Laha

  3. Department of Commerce, The University of Burdwan, Burdwan, West Bengal, India

    Debasish Sur

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Maji, S.K., Laha, A., Sur, D. (2022). Interrelationship Between Macroeconomic Fundamentals and Sector-Specific Stock Indices of the Indian Manufacturing Sector. In: Indian Manufacturing Sector in Post-Reform Period. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-2666-2_6

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