Do Quantamental roles really exist?

Hi everyone. I'm an undergrad currently studying Math and Finance at a target school for both. While looking into different hedge fund roles, I've come across the term "quantamental," but I haven't been able to get a clear picture of what an analyst at places like Coatue or D.E. Shaw actually does day-to-day, or if those groups are even truly hybrid.

I love deep research into math theorems and programming, but I also enjoy breaking down a company's fundamental business model and projecting its long-term performance. I'm currently interning at a small "old-school" small L/S fund that's deeply fundamental, and I don't really enjoy it as much.

Does a role actually exist that genuinely combines these two fields at the junior level? Or do you ultimately have to pick one side over the other?

Appreciate any insights.

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Not really no. I did one of these roles and can confirm they do not really exist. The reason is basically the two skill sets are non-overlapping and the minimal superset is very quant heavy (eg a quant can do a fundamental persons job just poorly but a fundamental person cannot at all do a quants job). But keep in mind that to create a job you need to hire people with a real bundle of skills but my point is this bundle does not exist anywhere. Eg there’s no entry level role that creates both sets of skills. In practice some quants develop fundamental knowledge in some narrow facet, and this is the real way this exists to some extent. A small number of prop firms do have these roles (which is where I did this role) but keep in mind you will have essentially no exit opportunities since the very few roles like this that exist are extremely unique. I do think this will become more common in the future because it’s easier to write small scripts with ai but realistically you do have to choose. If you like doing math, go try to work for a prop firm and hope you eventually can do it in equities where this can matter. You’d be surprised but a lot of prop firms do actually use some fundamental knowledge to an extent that would be surprising to most traditional hf analysts. 

 

The fundamental research role at de Shaw is mostly a traditional fundamental role EXCEPT de Shaw actually increasingly actually does non-long short roles like this that actually is genuinely innovative in a way that does not exist at other firms. For this reason they are ironically less selective for junior roles than p72 and citadel etc since realistically nobody young can know anything about weird shit they do anyways. It’s a good role actually. 

Yes, I did do a fundamental analyst role at a prop shop. you are incredibly pigeonholed in this role which is what I did not like. It is much better to do this if you genuinely think you can exit to doing a more traditional quant role elsewhere because the skillset is very much not transferable elsewhere. The problem I faced was essentially you become an outsider to all types of firms because you are weird in terms of skillset essentially; even though there are many of these roles now most firms with this role would rather train someone new regardless rather than hire elsewhere meaning having experience doesn’t mean you can lateral to another firm. Or if you are ok to just never leaving your role (many of these places are amazing places to work tbh, so you may very well be fine with that too). 

 

Every big pod and quant fund have people doing this. It’s a lot of data science, running regressions and trying to find signals to trade stocks.

Easiest example is using credit card data to trade retail. Everyone does it, and there’s still signal in a lot of these.

 

I work in this type of role at a larger hf platform, so yes, they do exist but the word “quantamental” isn’t used in practice by anyone serious, the roles are very uncommon relative to traditional seats, and rarely would they hire entry level IPs that would have a steeper learning curve. Team is a mix of true quant PhD backgrounds and fundamental L/S backgrounds. 

Not going to go into our specific approach but my guess is you’ll start to see similar strategies become more prevalent with the rise of new technologies & data sources, and due to the underperformance of many old school discretionary stock pickers. Some of what you’ll see out there is purely marketing and others that I meet are actually implementing some pretty unique approaches.

 

Thanks for the reply! Do you mind talking about your day-to-day, the level of math you're doing (if any at all), and how your fundamental position at a "quantamental" strategy is different than one at a pure L/S?

 

This is correct, quantamental is a process and not really a job. Any job called this ends up being 90% fundamental, and pure fundamental analysts already do data science with altdata. But they use the data differently from how a quant would make a systematic alpha out of it.

 

Interested as i believe i'll be a good fit for this type of role and there isn't lots info about this online. My current role is sell-side M&A so my practical skillset is closer to l/s fundamentals than pure quant. But i truly believe that market today is driven my structure vs. corporate fundamentals. I spent some free time i have learning about systems and theories (less proof bc i won't understand stochastic processes LMAO) and spent time learning the quant approach and these ideas just click with my brain. I think there's a great use case combining these two seemingly uncorrelated fields, especially with AI lowering the friction. I would love to learn more about making the switch or honestly just talk to more people in the industry and exchange ideas (my current job bores me yet it takes up so much time).

 

can you elaborate more on the systems and theories and how these click / can be implemented into a medium-long term equity play?

 

That's true - i'm probably not the guy to seek institutional-lvl guidance on equity strategics (fundamental or quant). But i'll try to answer his question, based on my own understanding.

  • The most important 2 idea i learned / discovered and also are applicable to medium and long-term equities were:
    • 1. markets are not stationary - in stats stationarity means the constant mean, variance, and autocovariance in a time series. Intuitive thinking, the non-stationary for markets translates to market cycles / regime changes. To people studying STEM, this is stating the obvious. But to me it makes sense because it explains why models / strategies that work in the past may not work now. That's why quant funds spend so much time predicting regime changes (e.g., Markov-switching or hidden-markov) being the popular ones and fundamentals need to get the themes right. They are cracking the same problem but taking different approaches. Fundamentals think the future is knowable (a strawman they'll fight you on), but they tend to reason deterministically downstream of an accepted theme, which is precisely what makes them fragile to regime change. On the other hand, Quants focus so much on the numbers that they could miss the most obvious causality that the fundamentals would never miss (which is why i think combining these 2 is lethal and it will become more popular soon)

    • 2. No one can really predict the future / time the market with 100% confidence. And it's totally okay. The goal of a fund (or an individual) is to structure your portfolio to achieve good risk-adjusted returns. So that when market shits the bed, you are not dead in the water and when market rallies, you are not left out of the gold rush. From a practical standpoint and this is an simplified example - you bought tesla and some cheap random shit stock that has nothing to do with each other. Over the span of 5-7 years, Tesla went crazy and random shit stock went up and down. But combined, you might find out you have less maximum drawdown, higher sharpe ratio than than owning Tesla outright. This really reinforced the idea of hedging and risk management. On a side note, that's why i'm not a fan of private equity because i believe 1. the mark-to model is literally volatility laundering 2. high execution barrier (which is why they exist but i think this advantage decays over time) and 3. bad risk management - low reported risk but high real business and investment risk (holding too many companies in the same sector for too long and it's ride or die on the macro conditions and heavy selection bias)
    •  
  • I'm not going to talk about the specific strategies because everyone does it differently. But i think above gives me a decent framework on how i approach portfolio construction. And to me the beauty of quant and fundamentals combined is that fundamentals have really good logic and framework and quant can turbocharge that via math & statistics on idea generation & execution. Instead of focusing on 20 stocks, you can expand to hundreds of them and find out the historical correlation (hint: correlations are non-stationary too) and approach regime change in both quant and thematic perspective. And obviously, top quants are great at execution so i'm not going to repeat that. That being said, i'm LEAPing on a few tech co's call options and watching / monitoring a few A&D / materials companies) 

Welcome industry professionals' thoughts on this. It's good for both me and the guy asking the question. I will simply acknowledge your feedback and not debate you because I'll lose if you actually work in HF.

 

I think the idea of quantamental is very ambiguous. how do you define quantamental? is it the fact that you use lots of alt data to catch signals and trade on them? btw this is what a lot of fundmanetal pods are doing anyway esp in consumers so not sure how you are defining it.

IMO, there is no such thing as a quantamental role. Every pod is using and sifting through datasets that matter to their strategies anyway and lots of pods employ data scientists. it seems like quantamental is used as a buzzword for outsiders or those who dont really know how LS HF space works. 

 

Yeah, I understand the alt data part, but I just really like math. I find developing algorithms and solving proofs very enjoyable, but at the same time really like fundamental research and taking a view on the actual business of a company. That's what I meant by "quantamental."

 

these are strategies i dont work on but I did hear lots of algorithmical implementations in fundmanetal seats. I guess these are quantamental to your definition. For example, some ppl out there are using instantaneous dcf modeling on whatever headlines on each company and immediately long/short based on that. tbh - there is no complex algorithmical / mathematical models that are used for fundamental stock-picking approach. the most it can get complex in the fundamnetal world is just instantaneous pricing discovery once new KPI is updated on a stock that flows through the financials (aka EPS revisions) and algo buyers rush in to discover the true "price". 

 

there are roles where youre basically framing credit card data for the fundamental analyst.  your view on the company isn't really going to matter if we're being honest (the analysts view and pm's view matter).  they'll only care about your point estimate - which by and large is just going to be basic regressions.  

 

agreed... every pod meets the definition of quantamental and it means nothing

 

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