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Research: Evaluating the Performance and Robustness of Multi-Factor Equity Portfolios i...

Field: Finance Type: Research project Bloom: Create / Evaluate Level: Final-year / PG capstone Inspired by: MIT / Stanford / Oxford research agendas

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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Available mentors
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Enrolled students
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Core skills
About this project
Research: Evaluating the Performance and Robustness of Multi-Factor Equity Portfolios in Developed Markets

Research question: How do multi-factor equity portfolios constructed using value, momentum, and quality factors perform and persist compared to single-factor portfolios in developed markets?

Background & Motivation: Factor investing, which involves constructing portfolios based on systematic risk factors such as value, momentum, and quality, has become a central paradigm in asset pricing and portfolio management. Academic research and institutional practice have shown that these factors can explain cross-sectional returns beyond market beta, influencing modern asset allocation strategies.

Research Gap / Question: While the efficacy of individual factors is well-studied, less attention has been paid to the out-of-sample performance, robustness, and practical implementation of multi-factor portfolios versus single-factor strategies, especially across different market conditions in developed economies.

Approach & Expected Contribution: This project will construct and back-test equity portfolios using value, momentum, and quality factors with publicly available data (e.g., CRSP/Compustat). The study will assess performance, drawdowns, and persistence, employing statistical tests (e.g., Fama-MacBeth regressions, bootstrapping) and robustness checks. The findings are expected to clarify the incremental benefits and limitations of combining factors and inform both academic debates and practical portfolio construction.

Why It Matters: Understanding whether multi-factor portfolios deliver superior risk-adjusted returns and remain robust over time is crucial for investors, asset managers, and policymakers seeking to apply evidence-based strategies in real-world settings.

Milestones
1. Literature Review & Problem Definition
15 marks 18d
Conduct a comprehensive review of academic and practitioner literature to frame the research gap and clearly define the scope of the study.
2. Research Proposal & Hypotheses
10 marks 15d
Formulate specific research hypotheses and propose the empirical strategy, outlining the factor models, sample period, and evaluation metrics.
3. Methodology & Experimental Design
15 marks 18d
Design the portfolio construction methodology, statistical tests, and robustness checks, and specify the dataset and software tools.
4. Data Collection / Experimentation
20 marks 21d
Obtain and preprocess relevant equity, factor, and financial statement data, and implement portfolio back-testing algorithms.
5. Analysis & Results
25 marks 21d
Analyze portfolio performance, conduct statistical tests, interpret results, and evaluate robustness across market regimes.
6. Thesis Write-up & Defense
15 marks 18d
Draft, revise, and finalize the thesis, responding to feedback, and prepare for an oral defense of the research findings.
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Skills you'll learn
ResearchFinanceSystematic literature review in asset pricing and factor investingQuantitative portfolio construction and back-testing methodologyStatistical analysis (e.g.regressionsSharpe/Sortino ratiost-testsbootstrapping)Empirical analysis of financial markets dataData cleaning and transformation for financial datasetsCritical evaluation and interpretation of empirical resultsAcademic writing and presentation of research findings
Tools used
Python (pandasnumpystatsmodelsmatplotlib)CRSP/Compustat or comparable developed markets datasetsFama-French factor data libraryBack-testing frameworks (e.g.QuantConnectzipline)Statistical techniques: Fama-MacBeth regressionsbootstrappingt-testsJupyter Notebook for analysis and reportingLaTeX or MS Word for thesis writing
Prerequisites
Investment Analysis and Portfolio ManagementStatistics and Econometrics for FinanceFinancial Markets and InstrumentsProgramming for Data Analysis (Python or R)
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