There are 4 electives on offer and students can choose any 3 electives out of the four
1) Introduction to Fintech
Course Objectives: The purpose of the course on ” Introduction to Fintech” is to provide the students with a holistic view of the Fintech ecosystem in both the Indian and global context. While most courses in this area provide either finance or a technology perspective of the industry, we aim to bridge the gap between the two through industry frameworks and use cases.
Learning outcome: This course will enable participants in identifying areas where financial technologies can be utilized to create new Fintech solutions as well as for rebuilding products & processes in Financial Institutions. This will also provide a detailed overview on emerging concepts around Open Banking and Digital Transformation along with deep diving into other segments of Fintechs such as Wealthtech, Insuretech, Regtech, etc.
2) Behavioural Finance
Course Objectives: This course complements the traditional finance courses that are mainly based on the conventional paradigm where investors and managers are generally rational.
This course will enable students to compare the traditional finance theories with the underlying prospect theory of behavioural finance. They will be able to describe the process through which the behaviour of investors contribute to asset pricing models and also become familiar with the biases that affect managerial strategic decision making.
Learning outcome: After completing this course student will know the concepts like Bounded rationality, Prospect theory, challenges to the efficient market hypothesis, key behavioural biases of individual and professional investors as well as top managers, key anomalies in the markets proving the existence of Behavioural Finance.
3) Computational Finance
Course objective: The main objective of this course is to introduce the tools of optimization and machine learning for developing and testing quantitative portfolio strategies.
- Portfolio optimization, simulations and computations
- Testing strategies using real data and decision making
Learning outcome: By the end of this course students will be able to apply the concepts to understand risk management, pricing and payoff and implement these models on Python.
4) Time-Series Econometrics
Course objective: This course is designed to familiarize students with the econometric tools used in time series analysis and implement those tools on real-world economic and financial data.
Select topics: Random walk, unit root process, seasonality, ARMA and ARIMA models, Box-Jenkins methodology, ARCH-GARCH models, etc.
Learning outcomes: By the end of this course, students will be familiar with the theoretical underpinnings of time series analysis, be able to build models with time-series data, run them and interpret the results on R.