Lubdhak Mondal

Associate Consultant

New Delhi, Delhi, India2 yrs 1 mo experience

Key Highlights

Expert in Natural Language Processing and Credit Risk Management.
Developed innovative trading strategies with positive returns.
Strong background in quantitative finance and machine learning.

Stackforce AI infers this person is a Fintech professional with expertise in quantitative finance and machine learning.

Contact

Skills

Core Skills

Natural Language ProcessingCredit Risk ManagementRisk ManagementTime Series AnalysisSentiment AnalysisDeep LearningMachine LearningFinancial AnalysisQuantitative FinanceBehavioral Finance

Other Skills

Acoustic GuitarAdobe LightroomAdobe PhotoshopAdobe Premiere ProAsset PricingBig Data AnalyticsConvolutional Neural Networks (CNN)Creative WritingCredit Default Swaps (CDS)Credit DerivativesCryptocurrency TradingData AnalysisData ScienceElectric GuitarFinance

About

I am a graduate from IISER Pune with a BS-MS dual-degree & I specialised in Applied Mathematics. I worked on my Master's Thesis at IIT Kharagpur and NUS Singapore on the multidisciplinary topic of Credit Risk and Natural Learning Processing. I have worked in multiple projects in Options segment with the goal of generating MFT and HFT strategies. Sectoral Interests: Risk Management | Option Pricing | Quantitative Finance | AI & Machine Learning | Natural Language Processing | High Frequency Trading

Experience

National university of singapore

Visiting Scholar

Oct 2022 – Apr 2023 · 6 mos · Singapore, Singapore

Joined Asian Institute Digital Finance, NUS Singapore, as an NLP Research Intern | Guide: Dr Xao Yuan |
1. Employed 17 financial variables of macro-financial risk factors & firm-specific attributes for default prediction.
2. Developed NS Monitor backend using NER/LDA/BERT to create credit risk sentiment indices for 40k+ global companies.
3. Discovered media sentiments enhances corporate default prediction PD model specially cases involving financial frauds.
4. Assisted in developing a credit stress testing tool that can provide stressed KPIs based on user-defined securities and stress scenarios.

Python (Programming Language)Natural Language Processing (NLP)Credit Risk ManagementNatural Language Processing

Indian institute of technology, kharagpur

2 roles

Master Thesis Student

Jun 2022 – Apr 2023 · 10 mos · Kharagpur, West Bengal, India

Pursued my year-long Master's project with the collaboration of IIT Kharagpur and CRI NUS Singapore
Guide: Prof. Abhijeet Chandra
Topic: Credit Cycles and Sectoral Risks
1. Analyzed the dynamic relationship between an unique credit cycle indicator and CVaR-derived sectoral risks indicator.
2. Examined the impact of macroprudential policies, NPA, GDP growth on mitigating India’s sectoral credit risk.
3. Predicted sectoral investment strategies and acceptable sectoral credit flow using Dynamic Panel Models, VAR, ARDL.

Time Series AnalysisRisk ManagementBig Data Analytics

Research Intern

Jun 2021 – Jun 2022 · 1 yr · Kharagpur, West Bengal, India

Guide: Prof. Abhijeet Chandra
1. Performed Sentiment Analysis using BERTweet with accuracy more than 85% after
extracting and pre-processing large volume of twitter data(> 100GB) from Twitter
API.
2. Obtained narratives related to cryptocurrencies by applying the the short text topic
modelling algorithm Gibbs Sampling Dirichlet Multinomial Mixture.
3. Analysed causal relation between crypto asset prices narratives through Convergence
Cross Mapping bench marked against Granger Causality Test.

transformersDeep LearningTime Series AnalysisSentiment AnalysisTwitter APITopic Modeling

North dakota state university

Research Intern

Feb 2022 – Mar 2023 · 1 yr 1 mo · United States · Remote

Developing a novel method to financial markets in Spatio-Temporal manner with application of CNN
Guides: Dr. Indranil SenGupta(NSDU)
Dr. Dana Sylvan(CUNY)

Financial AnalysisConvolutional Neural Networks (CNN)Machine Learning

The city university of new york

Research Intern

Feb 2022 – Mar 2023 · 1 yr 1 mo · United States · Remote

Developing a novel method to financial markets in Spatio-Temporal manner with application of CNN
Guides: Dr. Indranil SenGupta(NSDU)
Dr. Dana Sylvan(CUNY)

Convolutional Neural Networks (CNN)Deep LearningMachine Learning

Indian institute of science education and research (iiser), pune

3 roles

Semester Project

Jan 2022 – May 2022 · 4 mos

Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning | Guide: Prof. Anindya Goswami
1. Examined jump-diffusion processes’ advantages over GBM models and literature
reviewed basic materials on option pricing theory & portfolio optimization models.
2. Supervised ML methods such as XGBOOST and ANN were used to price Europeanstyle
call options without using Historical or Implied Volatility.
3. Trained models to catch the option pricing mechanism better than (> 15%) or similarly
to Black Scholes Model in NIFTY50 and BANKNIFTY.

Semester Project

Aug 2021 – Dec 2021 · 4 mos

Guide: Dr. Anil Zankar
Role of Colours to Explain Complex Narratives and
Storytelling in Film Studies:
The Project is focused to understand key role of colours in the Film Study perspective and especially how it can guide
to explain certain Complex Narratives and Storytelling.
Evolution of Ethnographic films in India and the Subcontinent.
Film and Screenplay detailed analysis of more than 30 classic movies around the globe with a special emphasis on use
of intricate colours.

Semester Project

Feb 2021 – Jun 2021 · 4 mos · Pune, Maharashtra, India

Guide: Prof. AMH Chorwadwala
Isoperimetric Problems: A Calculus of Variation's Approach
A Theoretical project, which is aimed studying the Isoperimetric problems on Eulcidiean Space and Unit Sphere
Tools from Variational Calculus were adopted to solve these complex problems numerically.
Worked on SciPy and CVXPY framework to solve these problems analytically.

Indian institute of technology, kanpur

Summer Internship

May 2021 – Oct 2021 · 5 mos · Remote

1. Examined CAPM, Fama-French, and other asset pricing models along with examining the reasons of asset mispricing.
2. Developed a Crash/Timing based trading strategy using documented indicators & anomalies from Behavioural Finance.
3. Achieved a positive annualised return of 9.5% during crashes and also during bull markets it outperformed the index returns.

Quantitative FinanceBehavioral FinanceAsset Pricing