BUSI70567 Applied Quantitative Macro Strategies
Applied Quantitative Macro Strategies: Course Research
Project
Create, research and critique a systematic macro strategy
• Propose a sound ex-ante hypothesis. Source idea can be from literature or inspiration Acquire appropriate datasets required for your strategy (input data, asset returns etc.)-
Clean, analyse and interpret the input dataset - Run analysis to show forecasting power
of the dataset/idea
• Transform the raw data into a tradable signal by applying appropriate filters, scaling,
portfolio construction and risk management steps
• Analyse, interpret and critique the return behaviour of the strategy
• Suggest further improvements that can be made to the strategy
Important note
The project will NOT be judged on the forecasting power/information ratio of the strategy. It will
be marked on the quality of the research and the intuition shown
Create, research and critique a systematic macro strategy
• Project to be carried out in teams of 5 or 6 (see Insendi for groups)
• Project accounts for 50% of your total grade for this module
• Project deliverables
Oral Presentation (3rd June 2025, deadline for submission 2nd June 2025 @ 14:00)
• Strategy thesis, research approach, results and suggestions (10%)
• 15-minute (maximum!) PowerPoint presentation
• 15-minute questions/discussion
Written research report (deadline for submission 10th June 2025 @ 14:00)
• Detailed analysis, methodology, and results (40%)
• No more than 20 sides of A4 including figures. Include pertinent information only.- Note:
Team members within teams will be graded equally. Please make sure you all contribute
to the presentation and the research report.
Note: Team members within teams will be graded equally. Please make sure you all
contribute to the presentation and the research report.
Example template for research report
Investment hypothesis
• Set out your ex-ante hypothesis clearly
BUSI70567 Applied Quantitative Macro Strategies
• Make references to any evidence supporting your hypothesis
Data sourcing and analysis
• Explain in detail where your data was sourced and why it is suitable for the model•
Analysis to include any lags/revisions/missing periods etc. - Signal construction and
analysis
• Explain the transformation functions used in the signal construction
• Carry out parameter sensitivity, lead/lag and jackknife analysis
• Discuss in-sample versus out-of-sample behaviour of signal - Conclusions and
summary
• Critically review the strategy - suggest improvements or extensions