Brett Caughran@FundamentEdge
This is super interesting as well
I've been experimenting with AI driven data dashboards to more systematically track company KPIs.
So much of the public-equity research process is about developing better revenue forecasts (which then flow down to EPS at various incremental levels).
Thus, so much of the investment research motion is about tracking data that informs more accurate revenue forecasts. This is the foundation of the alternative data industry.
But there are many helpful data sets sitting in the open. As chatbots have grown arms (Claude), I have been impressed by the ability of these tools to go grab this data.
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To me, this is the other missing piece that sits alongside Excel fluency. The ability to ingest an Excel model (eventually build it, but not today), identify key drivers (from open and proprietary data), distill that data back into forecasts, and flag back custom alerts (business momentum inflections, likely revisions to revenue forecasts, thesis validation/invalidation, etc). Now we are talking!! This is orders of magnitude more helpful than a finance chatbot wrapper.
I do wonder if these tools are progressing at such a pace that most finance professionals don't even need to learn coding agents.
For example, Perplexity Computer one-shotted something that, for sure wasn't perfect, but exhibits material progress in the capability of the harness infrastructure to build simple, powerful user interfaces.
Institutional grade accuracy remains a critical & still not fully solved issue (does this improve as firms like CarbonArc roll out MCPs??), but it is exciting to see the engineering capabilities improve so materially, in such a short period of time.
