Mehdi Khorram Headshot

Mehdi Khorram

Assistant Professor

Department of Finance and Accounting
Saunders College of Business
mekbbu@rit.edu
585-475-6401
Office Location
LOW-3336

Mehdi Khorram

Assistant Professor

Department of Finance and Accounting
Saunders College of Business

Bio

Mehdi Khorram is an Assistant Professor of Finance in Saunders College of Business at RIT. He received his Ph.D. in finance from Louisiana State University in 2022. He does research on Empirical Asset Pricing, Investments, Mutual Fund, Options, Informed Trading, and Credit Rating Agencies. His work has been published in the Journal of Finance and Journal of Financial Markets.

mekbbu@rit.edu
585-475-6401
Personal Links
Personal Website
Publication Database

Select Scholarship

Publications:

Heston, S.L., Jones, C.S., Khorram, M., Li, S. and Mo, H. (2023), “Option Momentum”. Journal of Finance, 78: 3141-3192. https://doi.org/10.1111/jofi.13279.

Khorram, M., Mo, H., Sanger, G.C. (2023), “Information flow and credit rating announcements”. Journal of Financial Markets, 65. https://doi.org/10.1016/j.finmar.2023.100837.

 


 

Currently Teaching

FINC-220
Financial Management
3 Credits
Basic course in financial management. Covers business organization, time value of money, valuation of securities, capital budgeting decision rules, risk-return relation, Capital Asset Pricing Model, financial ratios, global finance, and working capital management.
FINC-425
Stock Market Algorithmic Trading
3 Credits
The course is a “hands-on” lab-based class designed to help students develop algorithmic trading strategies to invest in the stock market that can be implemented by retail and professional traders.The course has a strong emphasis on practical application with the purpose of building marketable skills for careers in finance. Students learn how to design algorithmic trading models through the use of a computerized trading platform that allows back-testing of data on thousands of different stocks. The software platform includes an automated wizard for building advanced technical trading models without programming knowledge; but also has an embedded programming language, similar to C-sharp, for those students that have those skills and elect to use them. (Knowledge of programming is not required; and there are no pre or co-requisites; but a laptop is strongly recommended.).
FINC-761
Stock Market Algorithmic Trading
3 Credits
The course is a “hands-on” lab-based class designed to help students develop algorithmic trading strategies to invest in the stock market that can be implemented by retail and professional traders. What sets this course apart from many others is a strong emphasis on practical application with the purpose of building marketable skills for careers in finance. Concepts are not only taught, they are brought to life by learning how to design algorithmic trading models through the use of a computerized trading platform, that allows back-testing of data on thousands of different stocks. The software platform includes an automated wizard for building advanced technical trading models without programming knowledge; but also has an embedded programming language, similar to C-sharp, for those students that have those skills and elect to use them. (Knowledge of programming is not required; and there are no pre or co-requisites; but a lap-top is strongly recommended.).

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