Key insights
- The post discusses the challenges faced by smaller funds and independent analysts in accessing and utilizing expensive institutional-grade financial data and analytical tools like Bloomberg, FactSet, and Refinitiv. It explores alternative, lower-cost solutions and highlights the persistent need for manual effort in qualitative analysis and data integration. The discussion suggests a potential disadvantage for smaller players due to limited access to comprehensive data and efficient workflows, which could slightly negatively influence their ability to accurately assess US equities.
Genuinely curious about how people in smaller setups are handling this. From what I’ve researched, Bloomberg/FactSet/Refinitiv are obviously best-in-class for real-time data, screening, and core financials, but at ~$10–30k/seat, it feels like they’re really built for larger funds rather than independent analysts.
A few things that I found interesting were:
- Even with Bloomberg, a lot of qualitative workflows (expert calls, deep transcript search, broker research aggregation, etc.) seem to push people toward additional tools like AlphaSense / Tegus, which is an extra cost for an analyst * And a lot of the actual “thinking work” (pulling numbers, comparing vs consensus, reading transcripts, forming a view, prepping for calls) still feels fairly manual and fragmented
As someone new to this domain, I’m trying to understand how this actually works in practice, like if you’re using the full institutional stack, what does your setup look like today?. Do analyst have success with a lighter stack (APIs like SEC EDGAR / FMP + tools like Koyfin, FinChat, etc.) to get to ~70–80% of the workflow at a few hundred dollars instead of thousands?
Also curious about a few specific things (not sure how much people actually focus on these):
- Do you track historical consensus vs actuals in a structured way? * How do you handle earnings call transcripts — just read/search manually or use something more structured? * Does anyone try to formalise the analysis process (e.g., consistently laying out bull vs bear cases, key risks, scenario thinking), or is that just individual workflow? * Do you use anything that helps with pre-earnings prep (what to watch, key questions, historical patterns), or is that mostly manual * It feels like either the data is there but not well integrated, or the tools exist but don’t really tie everything together
So, trying to understand from people actually doing this. Is the real bottleneck still data access, or is it more about workflow/synthesis even when data is available?
And for someone low-budget but serious about public equity research, what would you recommend focusing on first?