<aside>

</aside>

<aside>

🔢 QUANTITATIVE RESEARCH & MODEL RISK Systematic strategy design · Factor research · VaR/CVaR/GARCH · SR 11-7 / OCC 2011-12 / FRTB · Python · C++ · 7 projects below ↓

🏦 INVESTMENT BANKING & CAPITAL MARKETS DCF · LBO · M&A · Comparable company analysis · 6 live deal projects · Vosyn Capital Markets Intern

📊 ECONOMIC CONSULTING & RESEARCH Econometrics · Causal inference · Panel data · Policy evaluation · UW–Madison RA · DBT ML study (4,000-obs panel)

📋 AUDIT & ACCOUNTING IFRS/US GAAP · SOX/COSO · GL management · Internal controls · CA + CPA (NY) candidate · $8M+ portfolio at FIND

Technical & Quantitative Skillset

Programming & Computing

• Python (NumPy, Pandas, SciPy), C++, R, SQL, Advanced Excel, Git, Jupyter

Quantitative Methods & Financial Mathematics

• Time Series Analysis, Stochastic Processes, Optimization, Linear Algebra, Probability, Portfolio Theory, Risk Modeling

Econometrics & Statistical Modeling

• Regression Analysis, Panel Data Models, Causal Inference, Hypothesis Testing, Forecasting, Stata, MATLAB

Machine Learning & Data Science

• Supervised & Unsupervised Learning, Feature Engineering, Model Validation, Cross-Validation, Scikit-learn, TensorFlow

Trading, Backtesting & Execution

• Systematic Strategies, Backtesting Frameworks, PnL Attribution, Transaction Costs, Risk Controls, Paper Trading

Market Data & Financial Platforms

• Bloomberg Terminal, Reuters Eikon, FactSet, S&P Capital IQ, Yahoo Finance

Visualization & Reporting

• Matplotlib, Seaborn, Tableau, Power BI, Data Storytelling

</aside>

<aside>

Methodology & Research Philosophy

I approach quantitative research with a focus on robustness, interpretability, and real-world relevance. Rather than optimizing for in-sample results, I prioritize out-of-sample validation, risk-aware evaluation, and stability across market regimes. My work emphasizes clean data, realistic assumptions, and implementation feasibility, ensuring research can translate into practical trading systems.

</aside>

<aside>

🎓 Education & Academic Background

Master of Science in Financial Economics

University of Wisconsin–Madison

Graduate training in financial economics, econometrics, time-series analysis, and quantitative modeling, with applied research focused on asset pricing, risk, and market behavior across global markets.

MSc in Financial Engineering (In Progress)

WorldQuant University

Advanced coursework in quantitative finance, financial mathematics, systematic trading, and machine learning, with emphasis on research-driven strategy development and real-world market applications.

Chartered Accountant (CA)

Institute of Chartered Accountants of India (ICAI)

Strong foundation in accounting rigor, financial analysis, risk management, and regulatory frameworks, providing a disciplined perspective that complements quantitative research and trading work.

</aside>

<aside>

Publications, Research Writing & Technical Notes

This section highlights my academic and applied research in financial economics, development economics, corporate governance, and data-driven policy analysis. My work integrates economic theory, econometrics, and machine learning to study macroeconomic stability, institutional effectiveness, labor markets, and public policy outcomes, with a strong focus on emerging and global economies.

Research Themes

• Macroeconomic crises, fiscal sustainability & emerging markets

Links:

Niraj Neupane_Presentation_Sri Lankan Economics Crisis.pdf

• Machine learning & econometrics for welfare and policy evaluation

Links:

Research Project_Graduate_Niraj Neupane.pdf

• Corporate governance, firm valuation & institutional quality

Links:

Niraj Neupane_Previous Research Report.pdf

• Labor markets, gig economy & digital transformation

Links:

Gig Economy Post Pandemic World.pdf

• Geopolitics, trade dynamics & global economic order

Links:

Parallel Perspective USA-China and India.pdf

Methods & Tools

Econometrics, panel data analysis, time-series modeling, ML-based evaluation, Python, R, Stata, and policy-oriented empirical research.

Research Orientation

I emphasize rigor, interpretability, and real-world relevance, focusing on robust empirical evidence and institutional context rather than purely theoretical or in-sample optimization.

</aside>

Market Data Analysis & PnL Modeling

Systematic Trading Strategy & Backtesting

Futures Trend-Following & Risk Management

Options Strategy & Volatility Analysis

Machine Learning for Trading Signal Evaluation

Automated Paper Trading & Execution Workflow

Risk-Based Quantitative Modeling & ML Risk Forecasting

Licenses, Certifications & Professional Credentials

<aside>

</aside>

<aside>

</aside>