Faculty
Maryam Farboodi is the Jon D. Gruber Career Development Associate Professor and an Associate Professor of Finance at the MIT Sloan School of Management.
Her research focuses on the economics of big data. She studies how big data technologies have changed trading strategies and financial outcomes, as well the consequences of the emergence of big data for technological growth in the real economy. She also works on developing methodologies to estimate the value of data.
Furthermore, Farboodi studies intermediation and network formation among financial institutions, and the spillovers to the real economy. She is also interested in how information frictions shape the local and global economic cycles. Most recently, her research also focuses on understanding the covid-19 pandemic and associated policies.
Previously, Farboodi was an Assistant Professor at the Bendheim Center for Finance at Princeton University. She holds a BSc in computer engineering from Sharif University of Technology, an MSc in computer science from the University of Maryland, College Park, and a joint PhD in financial economics from the Booth School of Business and the Department of Economics at the University of Chicago.
Farboodi, Maryam. Journal of Political Economy Vol. 131, No. 2 (2023): 3267-3309. SSRN Preprint.
Farboodi, Maryam, and Péter Kondo. Journal of Financial Economics Vol. 150, No. 1 (2023): 46-67.
Farboodi, Maryam, and Laura Veldkamp. Annual Review of Economics Vol. 15, (2023): 23-40. SSRN Preprint.
Farboodi, Maryam, Adrien Matray, Laura Veldkamp, and Venky Venkateswaran. The Review of Financial Studies Vol. 35, No. 7 (2023): 3101–3138. SSRN Preprint.
Farboodi, Maryam and Laura Veldkamp, MIT Sloan Working Paper 5968-19. Cambridge, MA: MIT Sloan School of Management, March 2023.
Farboodi, Maryam, Gregor Jarosch, and Robert Shimer. The Review of Economic Studies Vol. 90, No. 1 (2023): 261-292. SSRN Preprint.
MIT Sloan researchers have developed a statistical method that investors and financial firms can use to value their existing data and/or a potential data stream that they are considering acquiring.
New research shows that investors are most interested in processing data about large, high-growth firms over smaller companies, even those with high-growth prospects.
