Finlabs India

Nandip Vaidya

Nandip Vaidya

Nandip Vaidya is B.Tech. (Mechanical Engineering) from IIT, Mumbai and PGDM from IIM, Ahmedabad. He is member of the CFA Institute, USA (and its Indian counter-part) and Global Association of Risk Professionals (GARP), USA.

After over 25 years of work experience in financial services and management consulting in India, Prof Vaidya has found his calling in academics. He teaches courses in business analytics and quantitative techniques in Finance at Anil Surendra Modi School of Commerce, NMIMS. These courses are part of the NMIMS under-graduate programs of BBA and B.Sc. (Finance) and post-graduate program of M.Sc.(Finance).

Prof Vaidya is the program chair for M.Sc. (Finance) and is also actively involved in curriculum design for all the three programs. While at the NMIMS, he has acquired certification and teaching accreditation from the SAS Institute Inc., USA for Predictive Modelling and Visual Analytics. Data analytics is becoming the backbone for various business decisions including in the domain of Finance and hence the students across the programs ie BBA, B.Sc. (Finance) and M.Sc.(Finance) are exposed to the techniques for data analytics at NMIMS. Prof Vaidya has played an active role in evolving and in consistently delivering this part of the academic curriculum.

A firm believer in experiential and application based learning, Prof Vaidya has found his varied financial services (including consumer lending, life insurance as well as capital markets) and management consulting experience extremely useful in improvising the pedagogy for the courses he teaches.

A research paper by Prof Vaidya on an Empirical Study of Market Timing Abilities of Mutual Fund Managers in India using the Treynor-Mazuy (TM) and Henriksson-Merton (HM) models has been accepted by Finance India for publishing in a forthcoming issue of the journal. He has also presented a paper on examining hypothesized performance of select Indian equity funds by constructing their portfolio using Markowitz mean variance optimization model without changing the underlying securities.

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