Monthly fund performance against benchmark, weakest funds named first
On the third of each month the flow reads last month’s returns from your performance data and reports each fund against its benchmark and target, weakest first. Every fund below benchmark is named, with no explanation the data does not support.
Finance · Pensions & retirement · On a schedule · The 3rd of every month at 08:00 · 5 steps · Your database
- International Equity Fund: The fund returned 0.88% against a benchmark of 1.23%. It fell short of its benchmark.
- Conservative Growth Fund: The fund returned -0.23% against a benchmark of -0.10%. It fell short of its benchmark.
- Short-Term Fund: The fund returned 0.17% against a benchmark of 0.16%.
- Stable Value Fund: The fund returned 0.32% against a benchmark of 0.30%.
- Fixed Income Fund: The fund returned 0.59% against a benchmark of 0.56%.
- Balanced Fund: The fund returned 0.96% against a benchmark of 0.87%.
- Equity Index Fund: The fund returned 1.81% against a benchmark of 1.53%.
- Social Values Equity Fund: The fund returned 1.25% against a benchmark of 0.81%.
Why teams run it
Monthly performance reporting means pulling custodian or consultant data into a spreadsheet, comparing it with benchmarks and writing the commentary by hand. A model asked to do the same will happily round a return or invent a reason for it, which is worse than no commentary at all.
How it is governed
- Read-only: SELECT queries through an administrator’s connection.
- Every figure is computed in code; the model is told never to round.
- It is instructed not to explain a move the data does not show.
- Every run is kept in the run history.
- Who runs it
- Investment officers; Chief financial officers; Investment committee secretaries.
- What you supply
- A read-only database connection to your performance or custodian data, with the query pointed at your own tables.
From template to a live agent
The same builder, tests and release review apply to a template as to anything built from scratch.
- Open it as a working flow
- Every step, source, branch and approval is already in place, and every one of them can be changed before it runs on your data.
- Prove it before it ships
- An evaluation set can gate the release, so a change that breaks an answer never reaches anyone. A question it cannot answer from the sources leaves the gate unproven, not passed.
- Keep a person on the decisions
- A run that needs a decision stops and says exactly what approving will do, and who it is waiting for.
- Publish where people work
- A form, an embedded widget, Microsoft Teams or a scheduled batch — with its own access and limits per deployment.
- What teams usually change
- Point the query at your own performance or custodian tables; Change the run day to follow your data provider’s release; Add target-versus-actual commentary rules as instructions for the report step.
Questions buyers ask
- Will it write investment commentary?
- Only what the data supports. It is instructed not to explain why a fund moved unless the table says so, which keeps plausible-sounding reasons out of a report trustees may rely on.
- Where does the data come from?
- From your own performance or custodian data in a PostgreSQL database, read through a read-only connection an administrator sets up. The query is rewritten for your tables when you adopt the template.
- Who receives the report?
- Each run’s report lands in the Output queue, where it can be assigned to someone and marked reviewed. The template itself posts nowhere; a team that wants it delivered adds that step in the builder.
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See it run on your own data
We open it in the builder, point it at a sample of your documents and systems, and run it with you.