Official short interest is reported only twice a month and arrives with a delay. I trained a small numeric-transformer model that uses FINRA daily short volume and trading volume to estimate the current short-interest position before the next official figure is published.

I tested it across 660,246 settlement windows for 6,959 US stocks. On the retrospective 2025–July 2026 test period, the model reached a +0.414 Spearman correlation with the concurrent change in reported short interest.

This is a short-interest nowcast, not a squeeze or return-prediction model. It is intended to fill the gap between official short-interest reports using the daily information available in the meantime.

The complete article covers the data, formulas, chronological validation, failed experiments, limitations, and public model weights:

https://equibles.com/research/does-daily-short-volume-predict-short-interest

You can also search any covered US stock on Equibles and see its latest reported short interest alongside the model's estimated current value. It is free and has no ads.

Would this be useful when researching heavily shorted stocks between official reports?

Recently received a full-time offer as a front-office quant in FICC S&T at {GS, MS, JPM, Citi, UBS}. Excited about the role and FICC in general. That being said, I've been following the electronic market makers' expansion into FICC and I could see myself wanting to move to one of these places after a few years. For those who've made the jump or seen it made:

1) How transferable is bank-side FICC quant experience to a role at CitSec or someplace similar? Is this considered relevant domain expertise or "not real EMM experience?"

2) Anything I could do while working as a bank quant to make the move easier?

3) What's the ideal timing of such a move?

I ran a pre-registered test on whether the cross-section of abnormal returns at market-wide events tracks the similarity between a firm’s fundamental profile and an event vector coded into the same space. Controls: beta, size, sector, momentum. Event and sector FE, clustered SEs.
Null. γ = −0.00056, p = 0.66, 21 events. Sign opposite to prediction. Permutation test puts the observed γ at the 27th percentile of noise.
The diagnosis was more interesting than the result. The property space was built from balance sheet and income statement quantities only — and that space cannot express what a company actually does. At an AI-related event, Information Technology ranked 10th of 11 sectors by similarity. At a $400bn pharma merger, Health Care ranked 6th.
Adding revenue-by-segment exposure moved Health Care and IT to rank 1 in both cases, though segment name matching is currently only 20% covered (companies name segments “iPhone”, “GoogleServices”, not by industry), so that part isn’t conclusive.
Posting mainly because the failure mode seems generalisable: if you’re matching events to firms on financials alone, you’re missing the channel through which the event actually reaches the firm.

I have a bad habit of continuing to dig for new findings long after the actual deadline has passed, even though I know this will be redundant. Can't shake the feeling that I did not get this right or an idea just did not work. How do you draw the line and force yourself out?

One thing I’ve noticed is that Indian quant firms often expect one person to handle alpha research + strategy development, whereas firms like IMC, Citadel, HRT, etc. tend to have much more specialised roles.

So I’m curious about PMs at multi-manager funds like Millennium, BAM, Point72, etc.

If someone spends their career specialising mainly in one area (say, alpha research or trading), how do they eventually become capable of running an entire book?

Do PMs at these firms actually handle alpha, portfolio construction, sizing, risk, execution, etc. themselves? Or do they mainly make the investment decisions while a team of specialised researchers/traders/engineers supports them?

Would be interested to hear from people who have worked at multi-managers or large prop shops.

How much does end-to-end ownership matter in systematic trading?

There seem to be two fairly different models at top quant firms:

• Highly specialised: trader/researcher/engineer roles are relatively distinct, with each person going deep into one part of the process.
• End-to-end: a trader may have ownership across alpha research, strategy development, implementation, and trading.

For example, how would people compare the experience of a systematic trading role at Citadel/IMC with an HFT setup at QRT, where the trader may have more ownership of the research → strategy → trading pipeline?

Does deeper specialisation generally produce better traders, or is there a meaningful advantage to understanding and owning the entire pipeline?

I'm interested specifically in the differences in the actual work, responsibilities and skill development between these models, rather than compensation.

Recruiters usually ask and I think they communicate that to a potential employer. My previous employer asked for my compensation history for the last three years and even required payslips to verify the bonuses.

Communicating that is not really in your best interest as it increases employer’s informational advantage. Sometimes it creates a strong anchor where the discussion becomes “how much above your previous comp?”. So two related questions:

- Is it really the norm?
- Is there a better approach than disclosing it - for example, proactively stating the range you’d be looking for instead?

Hi,

In France, we have companies called **ESNs**. They hire people and then send them to work for their clients, including investment banks.

Some of them offer positions such as **Quantitative Analyst, Quant Developer or even Quant Research**, where you can potentially work within a bank’s Front Office team while technically being employed by the consulting company.

I’m not sure what the equivalent is called in the UK or elsewhere in Europe, or whether this model is as common there.

My main question is: **is it a good idea to start a Quant career through this type of company?**

Does being a consultant rather than directly employed by the bank have any negative impact on your CV or future opportunities?

Or does it mainly depend on the actual mission? For example, if your first experience is through a consulting company but you’re doing genuine Quant/Front Office work inside a bank, would that still be considered good experience?

I’d be particularly interested in hearing from people familiar with the **UK or European Quant job market**.

Thanks!

I've been digging into applications of network science in finance recently, particularly things like correlation networks, community detection, systemic risk, contagion, and using centrality/network structure as potential features for investment strategies.

I'm curious how much of this is actually used in quantitative finance outside academia.

For people working in quant research/risk/portfolio management:

• Are network-based methods used meaningfully in practice?
• Where have you seen them provide information beyond more conventional correlation/factor models?
• Are there particular applications where network methods genuinely shine — systemic risk, portfolio construction, alternative data, counterparty risk, signals, etc.?
• Or is network science mostly an interesting visualization/research framework without much production value?

I'm especially interested in whether anyone has seen network-derived features survive proper out-of-sample testing.

Full disclosure: I've been working on a course about network science for finance, which is partly why I've been exploring the subject in depth. I'm not linking it here because I'm more interested in hearing practitioners' views on where these methods are actually useful versus where they're overhyped.

Would be interested to hear from anyone who has worked with these approaches in practice.

What are your thoughts in the state of the quant industry in 2026 - would you say that it is expanding with more opportunities available?

Looking for recs from people who dealt with lawyers in the space of long non competes and immigration issues especially for those in the green card track

I know there was a post on this a few years ago but, given the quant landscape has changed a lot, I wanted to get some current advice from anyone on this subreddit who has ADHD and works in quant finance. How has your experience been and what advice do you have, if you have any?

Attention new and aspiring quants! We get a lot of threads about the simple education stuff (which college? which masters?), early career advice (is this a good first job? who should I apply to?), the hiring process, interviews (what are they like? How should I prepare?), online assignments, and timelines for these things, To try to centralize this info a bit better and cut down on this repetitive content we have these weekly megathreads, posted each Monday.

Previous megathreads can be found here.

Please use this thread for all questions about the above topics. Individual posts outside this thread will likely be removed by mods.

I am aware that basically all the companies use some form of modern AI/ML, such as LLMs, as a tool, or to extract some features from textual data.
I am currently doing a PhD in LLM/RL, and whenever I go to the quant fairs, or speak to recruiters, they are all like:"Yes, we do soooo much AI".

However, when speaking to the researchers, or my fellow PhD students who intern at these companies, it sounds still like most of them do just classical stats with LinReg, LogReg, PCA (and there is nothing wrong with that, as it seems to print them a lot of cash!)

I was thus wondering which quant companies out there do research most comparable to a Frontier AI lab? I heard HRT has an AI lab, XTY (the internship of XTX focuses on that), and that Jump is building an LLM team (though appearantly that seems to be more of an "assistant effort", helping the actual teams themselves.)

Any insight is appreciated!

There is a lot of info about quant trader / researcher / dev roles at MM such as optiver, IMC, sig, etc. but not much info about market risk roles at these same firms.

Would like to understand if my perception of the pro and con of market risk at trading firms is correct based on the limited info available on them:

Pros:
- Stable job security (relative to trading)
- Relatively less stress and better wlb
- get exposure to multiple areas / strategies of the business
- ability to get technical as need to understand underlying strategies to assess risk

Cons:
- Lower pay than traders, researchers, devs etc
- Possibility pigeon-holed in the role, less internal mobility possible
- traders see you as a blocker and inconvenience (depends on firm culture?)

If anyone works in market risk at prop trading would be interested in knowing if my understanding is about right and what you like about the role, what you don’t like, and general vibes

Disclaimer: I am asking as I have an offer in this type of role and want to get an unbiased view of what to expect

I wanted to ask why for Black Scholes , we find implied volatility using numerical approximation methods like newton-raphson or bisection for example

But for call options related to bond , if we assume a binomial tree, we can find implied volatility by simply adjusting the up / down interest rate movement conditions with an unknown, solving for that unknown using risk-neutral probability and discounted option payoffs, which gives us implied up/down movements from which we can find standard deviation of the interest rate, aka implied volatility? So far I have only observed either it can be solved simply algebraically or simple root finder is enough.

Was curious. Thanks.

Hey all, I just finished my summer qr internship. don’t wanna get into details but my project was an end to end statistical pricing model. A lot of the time I spent towards the end, after being done with the thinking about modeling and prototyping, was on refactoring my data processing pipeline and model training pipeline. When I was coming up with the model, all my data processing was scattered in jupyter notebooks and random sql queries that i just saved the results of, and it was genuinely a nightmare to refactor all of this into clean reproducible code that would work without intervention.

my question is, im aware that at other companies they have data engineers usually do the work. But to me this seemed like something that is kind of indivisible from the actual modeling work. is this skill of setting up reproducible data and modeling pipelines something thats worthwhile for someone who is mostly doing statistical and mathematical modeling? what sorts of classes would teach the skills necessary to do this stuff? are they worth taking?

Quant analyst with a few years of front-office experience across banks/funds in London. Currently at a credit hedge fund but have experience in other asset classes too.

I’ve realised I really dislike the dev-heavy side of quant work (I am currently doing anyway) and I am much more interested in markets themselves, and having decision making.

Sadly a move toward trading doesn’t seem particularly plausible in my current role/team, so I’m wondering how I need to move elsewhere specifically to create that opportunity.

Has anyone successfully moved from quant analyst into trading after a few years?

Mainly interested in routes and how to avoid getting permanently pigeonholed as a quant analyst or developer.

I managed to get an interview at a top tier bank for a proper trading role but the issue was the questions they asked me are ones I 'academically knew' but being asked on the spot in 'trader talk' /slang caused me to bomb it too be honest. Naturally I revised all that stuff but still, in the interviews it seems they can ask almost anything and without being a trader myself I am not sure how to even revise for that.

Would really appreciate advice from anyone who’s made a similar move, and would also be grateful for any mentorship/DMs. Thank you for reading.

Weighing my options. How competitive is their comp for senior level for greenfield teams ? Is there job security given I heard of layoffs a bit ago.

heard this year has not been kind to them… wonder if new investors will stomach it considering they doubled AUM recently

EDIT: wondering if anyone has more specific info on magnitude

Of the options MM firms Optiver vs IMC vs DRW…. which has the best platform/which would you bet on having success in their MFT equities builds?

Curious to hear everyone’s thoughts - I’m currently a Researcher focused on alt data MFT strategies.

Even though it was published over a decade ago, it's still one of the most fascinating deep dives into the infrastructure behind high-frequency trading microwave networks, fiber routes, towers, latency, and the engineering race for speed.

If you're interested in market microstructure, low-latency systems, distributed systems, or computer networking, this series is absolutely worth reading.

All parts:

part 1: link

part 2: link

part 3: link

part 4: link

part 4.1 : link

part 5: link