Quantitative Trading Project (2024/25)

This project was developed as part of the MSc Mathematical Trading and Finance programme at Bayes Business School (formerly Cass). The work investigates whether the comomentum factor, introduced by Lou & Polk (2021), enhances the performance of standard momentum trading strategies in US equities.

Overview

The core objective was to evaluate the empirical performance of comomentum in an out-of-sample trading setting, comparing it with a traditional momentum strategy. Using cross-sectional weekly stock return data (1992–2024), the study implements multiple trading rules, factor standardisation, and walk-forward evaluation frameworks.

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Methodology

• Signal Construction:
  Momentum and comomentum signals were generated using rolling return windows and contemporaneous comovement matrices, respectively.

• Portfolio Construction:
  Weekly long-only portfolios were formed with continuous and threshold-based position sizing.

• Backtesting:
  The strategy was evaluated using:

  • Cumulative returns and Sharpe ratios
  • Fama–MacBeth cross-sectional regressions
  • t-statistics and p-values for gamma significance

• Key Finding:
  Comomentum did not outperform momentum out-of-sample and is likely absorbed by broader risk factors.

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Key Results

Cumulative Return Comparison

Annualised Sharpe Ratios

• Comomentum strategies failed to significantly outperform the standard momentum factor
• No material improvement was observed using either threshold or hybrid signal variants.

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Repository Structure

Quantitative-Trading-CW/
├── quant_trading_code.py         # Main script
├── helper_functions/             # Custom utilities for standardisation, Fama-MacBeth, etc.
├── datasets/                     # Input data (returns, Fama-French factors) and output data
├── images/                       # Output plots and figures
├── Literature/                   # Supporting academic references
├── Report.pdf                    # Final report (with all results and interpretation)
├── Task.pdf                      # Coursework brief
└── README.md

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Requirements

Install required packages before running:

pip install -r requirements.txt

These include:

• pandas
• numpy
• matplotlib
• seaborn

See requirements.txt for exact versions.

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How to Run

From the repo root:

python quant_trading_code.py

Ensure that:

• All datasets are in /datasets
• Figures will be saved to /images
• Helper functions are accessible from /helper_functions

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Authors

• Shaan Ali Remani
• José Santos
• Chin-lan Chen
• Poh Har Yap

Coursework Brief