Welcome to the Quant Sandbox

Imperial College Business School ยท Building a Quantitative Portfolio

Recommended Learning Path

This tool has 24 modules across three difficulty levels: Beginner, Intermediate, and Advanced. Follow them in order to build understanding progressively โ€” from factor theory to portfolio construction, backtesting, stress testing, and critical evaluation. Each module takes 10โ€“15 minutes.

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Step 1 of 24Beginner~10 min

Factor Scores

Learning Objective

Understand how quantitative signals (momentum, value, quality, low-vol, size) are computed and combined into a composite score.

Key Concept

Z-scoring & winsorisation remove outlier distortion. The composite score = ฮฃ wแตข ร— zแตข.

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Welcome

HEC Paris EMBA ยท Specialization Week 2026

Welcome to the Quant Portfolio Learning Sandbox

An interactive simulator for learning quantitative equity investing. Explore factor-based stock selection, portfolio construction, backtesting, and risk management โ€” guided by the curriculum of Imperial College London.

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Course & Faculty

Making the Right Financial Decisions โ€” Tuesday, April 21, 2026 ยท Imperial College London

Prof. James Sefton

Quantitative Equity Investing

Imperial College Business School ยท Morning session (9:00โ€“13:00)

Factor-based stock selection, portfolio construction, backtesting methodology, and the critical evaluation of quantitative strategies.

Prof. Andrea Buraschi

Hedge Fund Strategies

Imperial College Business School ยท Afternoon session (14:00โ€“18:00)

Market-neutral construction, factor timing, regime-conditional strategies, and the Betting Against Beta anomaly.

HEC ParisExecutive MBA Programme
Imperial College LondonBusiness School

What You Will Learn

Factor-Based Stock Selection

Construct composite factor scores using momentum, value, quality, size, and low-volatility signals to rank and select equities.

Portfolio Construction & Backtesting

Build long-only and long/short portfolios, simulate historical performance, and evaluate risk-adjusted returns with proper statistical rigour.

Risk Optimisation & Regime Analysis

Apply mean-variance optimisation, Black-Litterman views, and regime-conditional strategies to adapt portfolios to changing market environments.

Critical Evaluation & Behavioural Awareness

Identify backtest pitfalls (p-hacking, survivorship bias, factor decay), recognise behavioural biases, and stress-test portfolios against historical crises.

Recommended Starting Point

24

Learning Modules

63

Exercises

5

Factor Signals

S&P 500

Stock Universe

Educational tool only. This platform uses simplified factor proxies and public market data for pedagogical illustration. It does not constitute investment advice. See the page for full disclaimer.

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