Financial and ESG screening of a UK and German stock universe
Build a repeatable screen of 40 listed UK and German companies that combines financial quality ratios with emissions intensity and governance data, and test how sensitive the ranking is to its weights.
Industry project · for Fenwick & Roth Asset Management, a London and Frankfurt boutique managing £640 million for pension schemes and charities
Fenwick & Roth runs a European equity fund for UK pension schemes and German foundations. Several clients now ask how the fund accounts for climate risk and governance, and two have said they will move money if the answer is a marketing brochure. The investment director, Dr Katrin Roth, wants a screen that the team can defend in front of a client’s investment committee.
She has asked for a pilot on 40 companies: 20 from the FTSE 350 and 20 from the DAX or MDAX, spread across at least six sectors. For each company you will collect, from annual reports and sustainability reports: - financial quality: return on capital, margin stability, net debt to EBITDA, cash conversion and interest cover; - environmental data: Scope 1 and 2 emissions and, where reported, Scope 3, converted to intensity per £ or € million of revenue; - governance data: board independence, whether the CEO and chair roles are split, and auditor tenure.
You will then normalise the data within sectors, build a combined score with stated weights, and test how much the ranking changes when weights or data choices change. Missing and inconsistent data is part of the job: companies report emissions on different bases, and some change their methods between years.
Done looks like a data workbook or notebook with a source log, a scoring model, a sensitivity analysis and a four-page note for clients. Dr Roth will judge it on whether every number is traceable, whether the method is fair across sectors and currencies, and whether the note is honest about what the scores can and cannot tell a client.
Data workbook or notebook with source log + scoring model + sensitivity analysis + four-page client note
Stack: Microsoft Excel or Python (pandas), Company annual and sustainability reports, Exchange rate data, Charting tool
- Traceable data25
Every figure for all 40 companies has a report, page and year in the source log, and gaps are flagged rather than filled silently.
- Fair method25
Ratios are comparable across currencies and sectors, and scores are normalised within sectors with a stated method.
- Sensitivity tested25
The ranking is tested against at least three weight sets and two data choices, and stable and unstable names are identified.
- Honest client note25
The note explains method, results and limits in plain words and does not present scores as more precise than the data allows.
Pass 60% · Merit 75% · Distinction 90%
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