GLS - An Application to ESOP Disbursement: A Study on Blue-chip IT Stocks of India
Year: 2025
Type: article
Abstract: : ESOPs are a mechanism to foster employee loyalty and retention. There is a need for an appropriate model for distributing shares under ESOP schemes specifically tailored to the unique context of IT companies. Generalized least squares (GLS) is a statistical technique that can be used to estimate the coefficients of explanatory variables in order to predict the outcomes of a dependent random variable. In the context of employee stock ownership plans (ESOPs), GLS has been used to examine the distribution of shares to employees in blue-chip IT companies. Panel data models were used to investigate the effects of time-series (time) and cross-sectional (group) variations. These effects can be either random or fixed. Random effects models assume that each individual group and time period has distinct disturbances, while fixed effects models assume that each group and time period has a unique intercept in the regression equation. Data for this study were collected from annual reports of six blue-chip companies in the information technology industry, spanning from 2012 to 2021. Variables such as revenue from operations, net profit, ESOPs exercised, shares allotted, and pro-rata utilization were analysed to assess the relationship. The findings indicate that the fixed effects model is appropriate for designing the shares disbursement model to employees. All factors, including revenue from operations, net profit, and ESOPs exercised, have a significant impact on the allocation of shares. The model exhibits a strong predictive power, with a projection accuracy of 63%. Keywords: GLS, ESOP, Fixed and Random Effect Model, Exercise, Shares Allotted JEL Classification Number: C13, C23, J33, G13, G32
Authors: Manisha Singh, Kavita Srivastava, Muddu Vinay, Pundareeka Vittala K.R.
Institution: ICFAI Foundation for Higher Education
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6930673956
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