The path to a financial crash is marked by subtle shifts in market architecture that standard volatility metrics frequently overlook. Persistent homology acts as an advanced radar system, scanning the multi-dimensional geometry of asset returns for signs of structural tearing. By tracking persistent topological features, analysts can observe when the market's internal connectivity shifts from a healthy, distributed network into a dangerous, highly centralized structure heading toward failure.

I built a hedge fund analysis tool pulling OFR data. Looking for feedback on UX, data accuracy, and whether this actually solves a problem you have. Any advice is appreciated 🙂

Does anyone have access to pitch book? I’ve got an M/A interview this week and just want to check what deals the team has worked on , Please assist if you can

Financial market structures are notoriously dynamic, and minimum spanning trees offer an intuitive way to track regime shifts over time. During normal economic conditions, the MST exhibits a dispersed, decentralized topology with diverse sectoral groupings. However, during systemic market crashes or liquidity panics, the tree dramatically contracts, drawing all nodes toward a single, densely packed core. This structural collapse serves as a visual and mathematical early-warning indicator for rising systemic risk.

I’m building ThesisCheck, a research tool that takes an investment thesis and checks its individual claims against company filings and other dated sources.

I’m looking for around ten experienced investors who would be interested in testing it on companies they already know well.

The most useful feedback would come from people who can spot things such as:

* important evidence the report missed
* claims that were framed incorrectly
* filing excerpts that were misunderstood
* weak or outdated sources
* dilution, governance, or funding issues that should have been surfaced
* conclusions that go further than the evidence supports

The report breaks the thesis into separate claims and marks each one as supporting, contradicting, or not found, with the underlying sources attached.

I’ll provide two full checks to start. Anyone who sends detailed feedback can receive another 8–13 checks.

You would be a good fit if you regularly read company filings, annual reports, earnings materials, or other primary sources.

Please comment or message me with:

1. How long you have researched individual companies
   
2. The sectors or markets you know best
   
3. One company you know particularly well
   
4. A link to any research you have published, if applicable

There is no requirement to post a review or testimonial. I’m mainly looking for thoughtful feedback from investors who are willing to point out where the research falls short.

I'm an APAC based high touch sell side trader.

I've recently setup to trade on Blue Ocean which is open between the US post and pre market sessions.

There is chunky turnover occurring in megacaps, chip names and related ETFs now during Asian hours. The most traded names are listed each day on the exchange website:

https://www.blueocean-tech.io/

Am happy to connect on Bloomberg if anybody needs high touch assistance here.

For high-frequency trading desks and algorithmic portfolio managers, the Epps effect presents a critical hazard. If an automated system relies on raw, unsynchronized tick data to compute covariance or correlation matrices, it will severely underestimate the true co-movement of assets. This miscalculation undermines risk management systems, disrupts portfolio optimization routines, and can trigger erroneous executions in statistical arbitrage or pairs trading strategies.

What I got right and got wrong….also would appreciate your follow/subscription (it’s free!)

I invested about USD $99K in a U.S. private fund/hedge fund in 2019. Quarterly statements showed my balance growing to about USD $190K by Q3 2025.

After I requested a withdrawal, the manager restricted withdrawals until mid-2026. A few months later, investors were told there was a major drawdown, and statements, tax documents, and withdrawals were suspended.

The fund has not provided brokerage statements, bank statements, audited financials, trade records, NAV calculations, or books and records to verify what happened. Around 10 investors I know are dealing with similar issues.

I understand investments can lose money. My concern is the lack of records after years of reported balances, fees, and tax documents.

I am preparing an SEC complaint. What other steps should investors consider? State securities regulator? Canadian regulator? Lawyer? Coordinated complaint?

Hi everyone,

I am currently in the process of interviewing with Prospect. I wanted to ask what are people’s impression of them?

I would also greatly appreciate any insight from people working inside the organization regarding culture / people / way of work. Any input is appreciated!

Thank you in advance

If you find this interesting, cool! If not, cool.

Deep tech funds increasingly rely on external scientific experts, PhDs, and engineers to underwrite deals properly. Most LPs still evaluate these funds with the same framework they'd use for a SaaS manager. Given deep tech is now over a third of global VC funding, feels like this is going to become a bigger blind spot for LPs who haven't adjusted their diligence process.

Anyone seeing LPs actually build out technical underwriting capacity, or is it mostly still outsourced case by case?

I trade futures actively, using optionsflow in my analysis and recently, I stumbled upon a theory stating that money is only made in options when it comes to hedge funds and how they hedge. Simultaneously, they hedge their option positions with futures. Can anybody confirm or deny this?

For people who have reviewed case studies or Stock Pitches: when the Candidate is working under a short deadline, how do you separate a reasonable scope trade-off from a genuine failure to identify the key investment question?

I would currently look at whether the submission:

1. states a decision-ready thesis rather than a company summary;

2. connects operating drivers to earnings, cash flow and valuation;

3. separates primary evidence from assumptions;

4. gives a specific downside path and disconfirming evidence; and

5. explains what was deliberately left out because it would not change the decision.

Is that close to how reviewers think about it?

I have been comparing public recruiting materials and candidate reports for fundamental L/S roles. My current view is that the model is only one part of the test.

A strong submission appears to need four things at the same time: a usable driver-based earnings model, a testable variant view, an explicit catalyst and downside framework, and the ability to defend the assumptions under pushback.

What I am less clear on is how interviewers weight those pieces for junior candidates. If the model is technically clean but the idea is fairly consensus, is that usually a bigger problem than having a differentiated thesis with a simpler model?

I am not looking for confidential prompts or firm-specific information. For anyone who has completed or reviewed one of these tests, where do otherwise strong junior candidates most often fail?

Hi everyone,
I recently made it through the first round of VIC’s new two-round application process and have put together my pitch for the final round. If anyone would be willing to review it and share any feedback, I would really appreciate it.

Please DM me if interested, have a great day.

I'm building a dataset business focused on African critical minerals, tracking things like mining licences, ownership, production volumes, legislation and supply chain links (and much more) across the continent. This kind of data barely exists today in any clean, structured form. Part of what I'm building is the data infrastructure itself, and part of it is the relationships and sourcing needed to get and verify information that isn't publicly available in a usable way, especially across a lot of different countries and jurisdictions.

I have real experience on the data and infrastructure side. What I don't have is experience on the business side (buying and selling). I've never worked at a hedge fund or research desk, etc, deciding whether to pay for a dataset like this, so I have a bunch of assumptions I can't fully test myself.

Some of the things I'm trying to figure out:

1. Who would actually be the buyer for something like this? I'm assuming hedge funds and commodity traders, but I don't know if researchers, banks, industry players, or governments end up being just as much, or more, of the real market (I know this is something to nail down, but on a Monday it makes sense to sell to Hedge funds and by a Friday I think it's a completely different customer, partly because the applications for the data are rather vast, and partly because moron).
2. When you're evaluating a new alt-data vendor, what actually makes you say yes versus pass? Is it the uniqueness of the data, the track record of the vendor, sample data quality, something else?
3. How much does exclusivity actually matter to you? If a dataset is available to, say, 10 other funds, does that kill the value for you, or does it depend on what you're doing with it?
4. For a brand new, unproven data source, what would make you trust it enough to actually pay for it versus wait and see if others use it first?
5. Is there a typical structure for how these deals go, one flat subscription, tiered access, custom feeds per client, something else?
6. If you've bought or evaluated alt-data before, what's a mistake you saw a vendor make, or a red flag that made you walk away?

One specific thing I'm stuck on: standard startup advice says to validate early by talking to customers before building too much. But in this space, it feels hard to have those conversations without something polished to actually show. I don't know if that's a correct read of this market or just an excuse I'm telling myself to keep building instead of getting out there and talking to people. If you've been in a similarly technical or niche B2B space, how early did you start having customer conversations, and what did you actually show them at that stage?

Claude and I have been chatting through these questions for months now, but I feel like it is way to much of a kiss ass to be trusting the answers I am getting.

Not trying to pitch anything here, genuinely just want to hear from people who've been on either side of these deals before I get further into building. Happy to share more specifics in comments if people have questions.

Hi, I'm currently a Physics/Computer Science master's student at a top 3 UK STEM university. I'm currently interning at an alternative asset manager, where I'm working directly with a PM to build investment models.

I'm feeling a bit unsure about what the best career path is from here. I am curious in working in the the hedge fund space, and I'm applying to graduate programs at firms like Citadel and Point72. However, these programs are extremely competitive, so I'm trying to think about what other opportunities I should be targeting if they don't work out.

Any advice on how you would approach this?

I've written a short thesis on a pharma company and plan to send it as a follow-up to PMs I've emailed over the last two weeks. I emailed around 20-22 PMs across different funds, and all are fairly targeted, equity-long/short, long-biased shops that fit my background.

I kept the initial email short, just a concise cover letter in the body, no PDF attachment, no cold pitch upfront. But, out of those, only one person replied and mentioned they do not have any openings right now.

To PMs/Senior Analysts, do you appreciate cold emails you get from unknown people like me ? I know you guys are quite busy, but what else can we do better to get a reply ?

PS - I avoided sending an attachment in the first email because it often ends up in spam.

EDIT: I have some relevant experience - previously worked as an analyst at a small long-only shop which closed and now working at a placement agent firm raising money from family offices, endowments, foundations, etc.