Dr. Nikhil Vidhani

Dr. Nikhil Vidhani works as a senior group manager in the F&A capability at WNS. He guides a team of data scientists in building data analytics apps. He holds a Ph.D. in Finance and Accounting from IIM Bangalore, where he has provided research consultancy and training in data analytics. His work revolves around predicting financial variables. He also handles software development tasks like version control, integration, and deployment. Earlier in his career, he worked with Cisco Systems as a software engineer.

In his dissertation, Dr. Nikhil has explored the return predictability literature in great depth. He proposed a new measure of investor disagreement based on return anomalies and linked this to forecasting stock volume. Besides, Dr. Nikhil has also trained hundreds of students and practitioners at IIM Bangalore in R programming, data analysis, and freelanced research projects for colleagues within and outside IIM Bangalore.

Dr. Nikhil has presented his research at several prestigious international conferences like International Risk Management Conference (IRMC), Society of Financial Markets Conference (SFM), Conference on Asia-Pacific Financial Markets (CAFM), Southern Finance Association meetings (SFA), Emerging Market Finance Conference (EMF), Southwestern Finance Association meetings (SWFA), and International Conference of the French Finance Association (AFFI). He was awarded the 2020 Mirae Asset Scholar prize and two successive Director’s Merit List awards for his doctoral work. He comes with extensive experience in data analysis and computational programming.

Publications

Sunny, A., Panchal, S., Vidhani, N., Krishnasamy, S., Anand, S., Hegde, M., Kuri, J., & Kumar, A. (2017). A generic controller for managing TCP transfers in IEEE 802.11 infrastructure WLANs. Journal of Network and Computer Applications, Vol 93, pp 13–26.
URL: https://doi.org/10.1016/j.jnca.2017.05.006 (Part of Master’s Thesis)

Revise and Resubmit

Vidhani, N., (2021). Return Predictability using Price-to-Earnings Ratio

SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3910641

Conferences: International Conference on Derivatives and Capital Markets (ICDCM2021)*, World Finance and Banking Symposium (WFBS-2021)*, India Finance Conference (IFC-2021)*, 15th NYCU International Finance Conference (2021)*

Revise and Resubmit: Journal of Forecasting (ABDC A)
* selected but not presented

Working Papers

Krishnan, M., Rangan, S., & Vidhani, N., (2021). Pricing of Earnings in the Presence of Informed Trades: A Simple GMM Approach.
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3560147
Conferences: 2015 NSE-NYU Conference*, CAFRAL at RBI*, IIM-Calcutta Finance Research Workshop*, 3rd JAAF-India Conference*, IIM Bangalore*, IIT-Madras*, IIT-Kharagpur*, University of Washington*
Under Review: Review of Finance (FT50)
* presented by co-authors

Vidhani, N., (2021). Trading Volume and Dispersion of Signals.
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3682088 (Completed paper; Part of Thesis)
Conferences: IIM Bangalore, International Conference on Derivatives and Capital Markets (ICDCM-2020), International Risk Management Conference (IRMC-2020), Southern Finance Association (SFA-2020), Conference on Asia-Pacific Financial Markets (CAFM-2020) Doctoral Consortium, World Finance and Banking Symposium (WFBS-2020), Theories and Practices of Securities and Financial Markets (SFM-2020), 12th Emerging Market Finance Conference (2020), Southwestern Finance Association (SWFA-2021) and, International Conference of the French Finance Association (AFFI-2021)
Target Journal: The Journal of Finance (FT50)

Work in Progress

Rangan, S., & Vidhani, N., (2021). New Evidence on Accounting Conservatism.
Abstract: We present new evidence on asymmetric timeliness, i.e. conservatism, where earnings display increased sensitivity to returns in bad periods. We document evidence of big bath where firms reduce earnings in periods of extreme positive returns. The accrual component of earnings explains conservatism while cash flows account for big bath. Firms increase their write-downs in periods of extreme negative as well as positive returns.