Machine Learning-based Anatomy of Climate Risk Exposure for Indian
Firms: An Empirical Study
Professor: Piyush Pandey
UID: SOM01
Understanding brand equity of top spiritual organisations in India
Professor: Dinesh Sharma
UID: SOM02
Machine Learning for Better Models for Predicting Bond Prices
Professor: Piyush Pandey
UID: SOM03
Machine Learning model for bond liquidity classfication
Professor: Piyush Pandey
UID: SOM04
SOM01
Description: The Reserve Bank of India (RBI) recently
released a report for the fiscal year 2022-23, with a focus on
sustainable growth and reducing carbon emissions. The report
addressed the four critical aspects of climate change, including
its macroeconomic impacts, financial stability implications, and
strategies to mitigate climate risks. We plan to construct
firm-level risk exposure measures of different types of climate
risks by utilizing natural language processing techniques on
firms’ quarterly earnings call transcripts. Plan is to evaluate
if climate risk is materialized in Indian stock markets.
Number of students: 2
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 7
Prerequisites: Functional use of NLP
Duration: 2 months
Learning outcome: Contribution to investment management
industry in India to provide them inputs whether climate risk,
the most talked about risk factor, is priced in markets or not.
Hands on working on data and NLP technique
Weekly time commitment:6 hours
General expectations: Weekly online/in person meetings
for update
Description:Recently top spiritual organisations have
sperad globally and got attention of global citizens. This
research attempts to understand their equity and appeal to
masses.
Number of students: 2
Year of Study Students entering 2nd year, Students
entering 3rd year
CPI eligibility criteria:
Prerequisites:
Duration: 2 months.
Learning outcome: Role of brands in marketing and
measuring the brand equity
Weekly time commitment: 15-20 hours
General expectations: sincerity
Assignment: Marketing Management by Kotler
Instructions for assignment:
SOM03
Description: The fixed income market is very important to
the economy. Sovereign issues are influenced by central bank
policy, and corporate issues are viewed relative to these
sovereign issues. Compared to equities, bonds have lower
liquidity and transparency; hence, there is less public data
available.
Number of students: 2
Year of study: Students entering 3rd year
CPI eligibility criteria: 7
Prerequisites: MG403 prerequisite
Duration: 2 months
Learning outcome: Understanding ML tools in financial
asset price usecase
Weekly time commitment: 7
General expectations: Basic understanding of bond markets
Description:Historically a fractured market for data
dissemination, OTC bond market participants have been yearning
for a consolidation, similar to that of the public equities
markets. With the emergence of machine-learning technology, the
hopes of a fully transparent market are now on the horizon.
Market conditions and the lack of state-of-the-art AI bond
pricing solutions led to sustained inability to price illiquid
securities with precision, in addition to the inability to
compare similar bonds and build pricing curves per issuer
efficiently. Resulting in limited price discovery for bonds
without strong liquidity.
Number of students: 2
Year of study: Students entering 3rd year, Students
entering 4th/5th year
CPI eligibility criteria: 7
Prerequisites: MG403( Basic understanding of bond
markets)
Duration: 3 months.
Learning outcome: Understanding fixed income market
analytics
Description:Driver warning technologies hold the potential to
reduce traffic crashes and save precious human lives. Scientists
and engineers hypothesize that the measurements obtained from
advanced driving simulators can predict equivalent measurements
in the real world that lead to a better understanding of the
driver behaviour in critical driving situations. The aim of the
project is to study the variations in driver state using a
combination of driver behavioural and driver physiological
signals using sensor technologies in driving simulator. Driver
warning can be developed through statistical analysis and AI
techniques.
Number of students: 2
Year of study: Students entering 4th/5th year
CPI eligibility criteria: >8
Prerequisites: Data analysis tools
Duration: 6-8 months
Learning outcome: Statistical data analysis and modeling,
exposure to driver simulators and sensors
Weekly time commitment:20 h
General expectations: Coding skills
Assignment: " For details refer related informtion: 1. Related
literature
https://www.dropbox.com/sh/asvw95aqktun99f/AAA4g5OPs24LQ9kbYnEe-cVua
2. YouTube Video Link:
https://www.youtube.com/watch?v=Ta7I0OB_RWU 3. Website Link:
https://smarteye.se/solutions/automotive/driver-monitoring-system/
"
Based on the related literature give above, write a summary in 2
pages.