This PhD scholarship is being sponsored by the centre. The selection of the eligible candidates is undertaken by the selection committee comprising of IIMB faculty.
The Selection Committee will evaluate applications on a competitive basis based on the following criteria: (i) the potential contribution of their research to the field of management; (ii) the student’s merit, i.e. academic performance both in IIMB and before; (iii) the clarity and significance of the student’s research plans; and (iv) the quality and quantity of any completed research; (v) alignment of research with sponsor objectives.
The Selection Committee may not choose to award the scholarship in a given year if there are no sufficiently meritorious applications.
Here are the award winners in 2025
Mareeswaran M
Finance and Accounting Area
Research Topic: Essays on Impact of Passive Ownership on Managerial Decision-Making
More about his research
The thesis studies the impact of growth in passive investing in financial markets on corporate decision-making. In recent times, financial markets have seen tremendous growth in passive investing and have overtaken active investing. Because of their trading style, passive ownership affects stock price efficiency, potentially reducing managerially relevant information in stock prices and making stock prices less helpful in making optimal corporate decisions such as investments, mergers, and acquisitions (M&As). The thesis documents important implications such as under-investment, weaker sensitivity of corporate investments, and M&A withdrawals to stock prices among firms with high passive ownership. The study provides crucial insights into a better understanding of passive ownership.
Kapil Gupta
Decision Sciences Area
Research Topic: Analysing House Price Dynamics using Novel Spatio-Temporal Methods
More about his research
The thesis focuses on developing spatio-temporal models to study house price dynamics in Bangalore and Greater London. It addresses temporal and spatial variation in prices, accommodates multiple observations per location-time pair, and proposes a scalable divide-and-conquer framework for large datasets. The models are implemented in a Bayesian framework, offering both methodological and empirical contributions to the study of real estate markets.