What does a model look like at a L/S hedge fund? What is the diligence process like?

Can someone please describe in painstaking, excruciating detail what modeling looks like at a top L/S SM? I am in PE right now and we are only able to make these absurdly detailed models because we get access to every single piece of information down to the G/L level and are able to create models with dozens of spreadsheets and call dozens if not 50+ customers to project account level details in many cases.

I have a hard time understanding how it is possible in a HF role to create models with builds behind even business unit revenue level line items without having more detailed information than is typically presented in 10-Ks or earnings calls. How is this possible? How do you build these models and what is the diligence process? I simply do not understand one bit. 

Comments (82)

  • Associate 2 in PE - LBOs
Dec 21, 2021 - 5:46pm

Look OP here I am going to continue my rant and say I was trawling through some tech 10-Ks and there's just nothing to go from. Nothing. And to know that the average L/S does not hire market consultants or FDD guys also blows my mind. Who figures these things out for you? PE in a nutshell is, get the workbooks from sellers and send to your Big4 of choice to get them to nail down all the nitty gritty accounting stuff (not really relevant for HF I guess?), get market work done by Bain (why does nobody do this in concentrated L/S, this is so crucial), and get your Big4 of choice to do all the cuts of financial data that you want (BUT WHERE DO YOU GET THIS DATA IN THE PUBLIC MARKETS?). I'm not talking about alt data, I'm talking about actual company financial information. What if I want to take my own build of Net and Gross retention but I can't cut it how I'd like because I don't have the underlying data? What if, instead of "Revenue Line A" I actually want to build it up from units * price and get that information? What if I want to toggle # of sales reps because I want to jockey management to hire more reps and can show the ROI on that would be accretive? None of this is possible without comprehensive information, yet still L/S funds SOMEHOW have to model. HOW can this be possible? What is going on inside these funds? Surely they are not just throwing a dart at a market and riding tech beta all the way home?! 

Dec 21, 2021 - 11:11pm

Because modeling is pretty stupid/pointless and the only reason you do all that is to justify to lps the fees you charge.  Suprised you think so highly of the pe diligence process, everyone knows its a joke.

Hf guys can simply just trade off "tips" and the actual underling financials don't matter...only the direction of the stock.

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Dec 22, 2021 - 6:13am

Every PE analyst / associate has had the experience of building a hugely detailed model at maximum granularity building revenue, COGS, etc by geography & product. Principal / partner looks at the output and says the following:

  • This business is not a 7% grower, this is a GDP+ category 
  • I don't believe you can add 300 bps of EBITDA margin 
  • We are not going to win this unless we are 5% above the management case

The idea that "the model" helps you make good investments and have greater certainty in their outcome is a fallacy. PE alpha is driven by leverage, illiquidity, a bit of smart sector allocation and a lot of spotting frauds / pumped-up numbers that don't get caught in today's credulous public markets.

Dec 22, 2021 - 12:56am

Will being able to model out net and gross retention to a tee for the next few quarters REALLY change your thesis on the company? If ROI from marginal sales reps is 1.67 instead of 1.72, does it even matter? Will building out a hyper-granular AR collection schedule move the needle on whether you invest or not?

It turns out most companies' long term success can be boiled down to a few key factors. That's the beautiful thing about public markets: all you have to do is have a different view on one aspect of a company than the market. You don't have to care about anything else; if the market thinks net retention will 110% and you think it will be 115% and you're right then you will outperform over time.

  • Analyst 1 in IB-M&A
Dec 22, 2021 - 4:54am

Look OP here I am going to continue my rant and say I was trawling through some tech 10-Ks and there's just nothing to go from. Nothing. And to know that the average L/S does not hire market consultants or FDD guys also blows my mind. Who figures these things out for you? PE in a nutshell is, get the workbooks from sellers and send to your Big4 of choice to get them to nail down all the nitty gritty accounting stuff (not really relevant for HF I guess?), get market work done by Bain (why does nobody do this in concentrated L/S, this is so crucial), and get your Big4 of choice to do all the cuts of financial data that you want (BUT WHERE DO YOU GET THIS DATA IN THE PUBLIC MARKETS?). I'm not talking about alt data, I'm talking about actual company financial information. What if I want to take my own build of Net and Gross retention but I can't cut it how I'd like because I don't have the underlying data? What if, instead of "Revenue Line A" I actually want to build it up from units * price and get that information? What if I want to toggle # of sales reps because I want to jockey management to hire more reps and can show the ROI on that would be accretive? None of this is possible without comprehensive information, yet still L/S funds SOMEHOW have to model. HOW can this be possible? What is going on inside these funds? Surely they are not just throwing a dart at a market and riding tech beta all the way home?! 

Inclined to agree. Whenever I have built >5 tab models looking to project individual accounts / similar level of detail, I have sought company's guidance and a bunch of other stuff mentioned in your comment. The thing is, for anyone looking to own it in the same manner as a PE and maintain it as a PortCo, they have the access to build these. Even then, I feel the need to build this to this level of detail is optional / very case dependent.

The PE modelling process is just an extension of the 10-20 tab sell-side operating models: often longer monitoring time frames on PortCos, more granular cuts (instead of forecasting just by SKU based on mgmt estimates you may even call up customers who buy the goods and try and balance the supply with their demand pipelines etc) and more inter-dependence between advisors. You don't often get the CDD advisors' data feed into the sell-side model in IB, barring directional alignment, but there may be more overlap while in PE.

To be honest, not the biggest fan of PE models. At that point modelling is far removed from "valuation" in the sense of "structuring" an efficient transaction (i.e., a la HF looking for some creative bet) and much more what a F100 FP&A person may do when preparing their budget after engaging some advisors or by extending the remit of their auditors (give or take).

It's also funny how life comes full circle: top grads flock to IB shunning FP&A / CFO track roles in corporates so that they can exit to PE. Once in PE they get to work on basically forecasting the general ledger for PortCos or to be PortCos. I digress.

Dec 23, 2021 - 5:37pm

Sometimes I step back and think about how little you can know about the company you invest in as a public equity investor. Even if you take a deep dive and use alt data, you get a relatively small part of the picture vs private markets

Dec 24, 2021 - 5:36pm

To be honest from the HF side as an intern if all someone is doing is reading the 10-K or earnings calls it isn't enough. You have to go out of your way to collect additional public information through visiting physical locations, attending conferences where a company speaks, channel checks, industry reports, etc. If it's a goods company and you can find out an average dollars per transaction you can build out a model that way for example. If it's an advertising company and you can find details on ad rates, you can build a model based on the number of ads served. If it's a company largely with recurring revenue and you know the number of subscribers and average subscription paid, you can model it. At the end of the day models and price targets aren't absolute, they're relative to what you know and estimates. The most important thing IMO is to build an information advantage to skew a bet in your favor and understanding the business rather than having the "perfectly built model". Simple models can go a long way

  • Investment Analyst in RE - Comm
Dec 21, 2021 - 6:57pm

this podcast was largely about tiger's VC/growth business, not their l/s business

  • Analyst 1 in HF - EquityHedge
Dec 21, 2021 - 7:14pm

The ideal approach at a MM is to keep the model as simple as possible while still being able to solve for the key variables that will drive a beat/miss. You don't want to let "perfect be the enemy of good" and you need to be quicker than the street/consensus so more often than not you're operating with a limited portion of information in order to arrive at 80% of the answer. IR also becomes your best friend as they can help clarify some of the vagueness in the filings but this varies by company. Would be curious to hear input from someone working at a SM and how this differs with a longer term investment horizon.

Dec 21, 2021 - 8:51pm

This post is such a classic PE false precision superiority. A better model does not make a better investment. The companies that provide the most information/line items are often the worst investments.

Riding tech beta? PE is all about timing the macro cycle with leverage. It's way higher beta than L/S. A few 8x EBITDA entries and 15x EBITDA exits with leverage can make a PE fund and that is not skill with a 2000 line model

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  • Associate 2 in PE - LBOs
Dec 21, 2021 - 8:59pm

false precision superiority

There is no superiority. I just don't understand how what you describe occurs. How do you construct the model on that opportunity. You need to identify the drivers of value. Constructing the model more than anything is a tool to understand what elements of the business will drive value (e.g., by playing with the drivers you can see what business changes lead to what returns results). Let's say we are talking about a company that does not provide the most information line items. What are you modeling? What are the things you have to model to get comfortable with the investment? Without those provided by IR, how do you get those? 

Dec 21, 2021 - 11:06pm
  1. Open and read the filings. There is a lot of info in there 
  2. Earnings calls / transcripts. Management remarks / Q&A will be focused on drivers 
  3. No offense but just changing inputs in your 30 tab ideal isn't understanding drivers whether (oh wow when unit volume goes up so does revenue!!!) 
Dec 23, 2021 - 2:48pm

Here is an example - take a drilling rig company. You can see the history of how many rigs operate in the US per month and well as day rates. You can track a company's market share over that period as well. You know the rough cost to operate a rig, the maintenance capex, and SG&A. From there, a very simple model/math will get you where you need to be. It can be as easy as I have 50 rights, getting 25k a day in revenue, with 10k a day in cost and 75mm in SG&A and 1mm a rig per year in maintenance capex. So know you know that 1 rig will get ~9mm in revenue, with ~3.6mm in opex, 1mm in maintenance capex thus 4.4mm in cash flow per year.  A company might own 150 rigs and have 50 operating. So you can easily calculate breakeven and incremental cash flow with very simple numbers. Projections can be based on overall market expectations etc. but the point is keeping it relatively simple. From there you can play around with subtle changes to rig count or day rates but you don't need to get too complex.  Then you layer in mgmt comments, industry info, what is the competitive advantage of the company (location, IP, brand, mkt share, etc.) things more qualitative in nature to get a view on where it might go. 

In contrast, a PE guy might model out the cash flow of every single rig, run sensitivities based on every specific rig contract, factor in wages of every rig worker comp package, try to do something fancy with basin level projections, have the SG&A be extremely detailed, blah blah blah. But how much more are you learning by doing that and what is the likelihood of that being more impactful than just multiplying 50x15kx360 - 75mm, and changing the 50 to 75. You're not really going to learn that much and getting too detailed is kinda a complete swag anyway. Granted a drilling rig company is a more simple business model but the same concept can be carried over to even more complex industries/companies. Not to mention what's the point of having 10+ variables, go forbid something like 50+ variables - all moving in different directions that is entirely outside of your control. 

  • Research Analyst in HF - Other
Dec 21, 2021 - 10:29pm

I don't actually model much beyond simple price x volume type napkin math. just look like sell side/consensus. if those numbers look attainable. it'd a lot of probability and estimation moreso than pure modelling.

  • Analyst 1 in IB - Gen
Dec 21, 2021 - 11:16pm

Classic pe nerds here. 

"How does anyone make money in LS if i cant build a 24 tab model to the ledger level detail and have bain make a 150 page market study to read!"

I honestly think PE is just as pathetic/unimaginative than IB, if not worse.  They just think because they call themselves "investors " they somehow arent.

Dec 21, 2021 - 11:17pm

currently in college so take this with a grain of salt:

for trying to find the present value of cash flows, i know some analysts prefer to reverse engineer the growth assumptions and discount rate that is baked into the present stock price. so for example if the present stock price implies a 7% yoy growth in revenue but you believe that this is too low, then you know the stock is undervalued at the current price. this works especially well if a single metric like revenue growth or expansion in profit margins is a driver for value moving forward. the reason this works is that you want an investment to be painfully obvious so your forecasts for growth in the future should be wayy higher than what is priced in which provides you a margin of safety. buffett actually said that a good idea only takes him 1-2 hours to get through, same with bill ackman. bill actually did a couple hours worth of work in 2008 on a home-run investment so more time (and a more complicated model) does not necessarily mean that your investment will pan out the way you want. 

Dec 23, 2021 - 6:07pm

then again bill ackman did spend 15 million on herbalife due diligence, and if you spend 15 million on anything, you're going to go long or shot. sunk cost fallacy applies to this scenario since it's tough to walk away after investing so much

Jan 9, 2022 - 5:44am

I see what why you went where you did, but most people in HFs aren't backing into current stock prices. Current prices already have market sentiment and thoughts baked in so there is no use to doing so. The idea of diligence and modeling generally revolves around being able to assess if assets are mispriced and whether you can find value where others haven't.

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  • Associate 1 in IB-M&A
Dec 21, 2021 - 11:57pm

I don't think you realise how useless models are in general - whether in PE or L/S. We all know you can just toggle parameters to get to where you want to.

  • Research Associate in ER
Dec 22, 2021 - 12:08am

have also wondered the opacity and narrowness of filings and modeling as a newbie. As a follow up to this, is there any truth in saying that larger value is placed on those metrics more easily estimable (revenue, ebitda, etc.) as a result of this?

Dec 22, 2021 - 12:15am

IB guy with no investing experience here.

A lot of people are trashing models on this thread. How else can one value a public company and arrive at an investment decision?

It's simple enough to tick and cross some boxes to find out whether it's a "good" company, but surely any sophisticated valuation requires a model, and that model requires assumptions.

Whether there are 200 assumptions or 10, I think OP wants to know how you get comfortable with outside-in diligence on these assumptions without support from external advisers.

  • Associate 1 in IB-M&A
Dec 22, 2021 - 12:39am

Everyone will have a different view - but imo it's all qualitative decisions backed by stuff you see directionally. Also you don't need absolute numbers - you just need a sense of relative to historicals. But yes if models worked and it were easy, the hit rate wouldn't be so low in public markets. Models are useless when multiples trade where they are today anyway 

  • Associate 2 in PE - LBOs
Dec 22, 2021 - 8:50am

Yes OP here, agree. Also, fewer drivers but how do you identify them without building and sifting through a gargantuan model to test everything. And even to the guy who said 110 vs 115 net retention as a way to identify outperformance, I totally agree with that, but how even would you develop that view without the data. Thanks again. Great thread so far.

Dec 22, 2021 - 12:39am

In my opinion models are mainly meant to see the downside of the investment along with seeing how the company views it's growth prospects/financials and those general aspects of a company . The real alpha is generated by talking to management, channel checks, talking to customers, viewing the macro enviroment/industry tailwinds, capital allocation efficiency, alt data, and the rest of the gauntlet. After doing this, you have to make a judgement call with whether the prospects of the company are better than the consensus. Modeling is just doing the due diligence that the company isn't a ponzi scheme albeit some TrashCos have fallen through the cracks.

  • Principal in PE - LBOs
Dec 22, 2021 - 12:54am

#1- private equity models are stupid. Way too much granular detail to be useful.

#2- HF models are indeed based off of 10k/q plus earnings reports and calls. Intellectually, they're a bit lacking but for any given stock only like 3 KPIs matter… so you barely need a model anyway. Any analyst worth their weight in salt can tell you the eps impact based on a few metrics. If I told you the top line miss and bottom line beat, most decent HF monkeys could within 5 minutes tell you everything in between and the likely reasons - without running a model.

In reality, if I were starting/running my own company and wanted a financial analyst to build out a model to understand in a bit more detail, it would be more detail than a HF model (basically just meant for a Michael Scott-type audience) and less detailed than a PE model which is intellectual masterbation.

  • Analyst 1 in IB-M&A
Dec 22, 2021 - 4:43am

True but want to take this thought along and see whether it translates well to other HFs too (not just L/S).

If we take something like Distressed or Turnaround focused funds (blurred lines here), would they not benefit from knowing exactly where the problem lies / what they can turnaround or work on? In that case, does the question not come down to knowing more granular builds (or at least being in possession of more granular historical data)?

  • Associate 2 in PE - Other
Dec 22, 2021 - 9:56am

Issues in distressed / turnarounds tend to be more global vs. so nuanced you need a microscope in a model to see it. This doesn't preclude granular modeling, but just as a general statement which I believe is more or less consistent 

  • Principal in PE - LBOs
Dec 22, 2021 - 10:29am

When a business goes bankrupt, it's very obvious why. There's no mystery and hypothesis, and even if there were an excel model is not the answer.

Whats a bit tough for people to deal with is that the model is truly like 5% of the deal/investment calculus.

Additionally, you should understand that part of the reason for the disparity in levels of detail is the availability of information and how it restricts the parties in possession of it. A HF analyst in many cases doesn't want any more information even if they could get it because it restricts them in freely trading. That should tell you how valuable that margin information is.

Whats most valuable in a distressed situation is all the legal docs and understanding the guarantors, transfer restrictions, and legal entities. Has nothing to do with modeling.

  • Associate 2 in PE - LBOs
Dec 22, 2021 - 8:53am

This is the closest I've gotten to a real answer so I'm desperate for a response.

1. How do you identify those 3 KPIs
2. Once you identify, without the data, how do you develop a view on them. (Eg people have been saying alt data but I don't understand the link between alt data and the key KPIs mainly because I don't use alt data.)

Most Helpful
  • Principal in PE - LBOs
Dec 22, 2021 - 10:42am

This is the closest I've gotten to a real answer so I'm desperate for a response.

1. How do you identify those 3 KPIs
2. Once you identify, without the data, how do you develop a view on them. (Eg people have been saying alt data but I don't understand the link between alt data and the key KPIs mainly because I don't use alt data.)

1. If you cover a given sector/company, it's very obvious to you. If it's not very obvious to you, you're incompetent. For proctor and gamble it's top line organic growth. For Adobe it second and third derivative revenue growth, i.e q/q accel/decel in growth, margin by which they beat guidance/consensus growth estimate. For Chipotle it's same store comp growth, accel/decel, and 4-wall margin. For a bank it's roa/roe, asset growth, and loan loss trends. For an HMO it's utilization and reimbursement rates and so on…

2. how do you develop a view? That's the entire game everyone is playing. But the obvious way to accurately get a view isn't really a 'view'. A real view is a differentiated take, and reading the WSJ or getting some dog shit commoditized credit card data from Yippit isn't it either. It's accumulating disparate datapoints both quantitative and qualitative, and using that often conflicting body of information to assemble a reasonable expectation. Keep in mind the most likely outcome is already priced in. You need to know what the most likely outcome is, so you know what's priced in. Then you need to try to discover when that most likely outcome is unlikely to happen (which is inherently a divergent view), whether it will be better/worse, and how the stock will react. That's basically the whole game. Its understanding what people think and why, if they're likely to be wrong, and how they'll react when they find out. Within that framework how valuable do you think a model is? Asked an other way, how differentiated is your 6th grade arithmetic?

  • Analyst 2 in IB - Gen
Dec 22, 2021 - 6:52am
  • Based on my little L/S experience based on a recruiting case study (successful)
  • Context: L/S tech and tech-enabled focused fund investing in stocks where the mispricing is due to temporary situations (ie: Boohoo.com share price tanking in 2020 due to supply chain allegations)
  • Approach:  granularity in key drivers, in my case: revenue, a specific cost item, NWC and an item of the cap stack, which were all interconnected in a way (don't want to be more specific than this for privacy reasons); more simplistic approach to other items less relevant to the thesis; we then ran sensitivities on those items as a sort of "stress test".
  • Once decided where short or buy the stock, we have we have then evaluated potential M-T share price movements (ie: was the company a potential target? if so at what price? was there any potential target the company could have bought? etc.)
  • Analyst 1 in IB-M&A
Dec 22, 2021 - 6:29pm

Yes. Also, did the unbundling of research and execution fees apply to US too? Would be interesting to see how many HFs are splurging on research and models. 

Dec 22, 2021 - 4:52pm

Think this is a really good thread - don't think OP is trying to come across as "superior".  It's a good question that junior PE professionals deal with and I've been trying to really simplify my operating models. For example, I've stopped building my SG&A projections off each item in the GL, but instead taken a fixed vs. variable cost view.  Much easier to explain drivers and then you can supplement with components in your description.

Dec 22, 2021 - 9:50pm

Pizz did u just blatantly copy paste another person's work and pass it off as your own? You must actually work at a hedge fund I'm impressed

Big4 Audit --> TMT L/S

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Dec 23, 2021 - 2:19am

The reality is 90% of that detailed PE model is BS. It literally doesn't matter for an investment thesis. Its just extra work because you can. Not to mention the likelihood of detailed projections occurring is pretty slim - at least on a QoQ or YoY basis, even if directionally correct longer term. Once you have a view on a sector and a company's position you know for the most part whether its a good idea or not. The model to a degree is justification. Not to be cliche but you think someone like warren buffet has crazy operating models, of course not. Ultimately, PE firms have the time and resources so they can build them...but those aren't driving the decisions and are completely unnecessary. Hence why for the most part they dont exist in the public markets. The OPs inability to comprehend this is almost some form of denial for all those hours spent in excel. Frankly, its some PE people struggle moving to HFs...bc you need to make decisions with imperfect info. 

Dec 23, 2021 - 4:02am

Hedge fund models are quite different from P/E models. In P/E, you have granularity because you have access to opco as the buyer. In the HF space, that would be MNPI. Therefore, hedge fund models need to incorporate all known data (10-k/q/investor presentations) but cannot exceed that level of granularity on a per company basis due to the aforementioned restraint. Whereas PE analysis is like solitaire, HF analysis is like poker. 

Generally, you'll start with a sell-side model that is a fully fleshed 3-statement model + all schedules. You will isolate critical factors and use your firms' resources to develop an edge on them. For example, say you are analyzing an industrial company that manufacturers and ships components used for cameras. The critical factors here may be 1) firms desire to expand is decreasing operating margins through an increase in SG&A, 2) new orders for buyers (say Apple and Samsung), and 3) input costs. 

You develop an edge by:

1) scrapping job posting info and through surveys with HR specialists within that space to see if employment is ramping up faster/slower than expected

2) new orders are increasing by reviewing downstream manufacturing activity from Samsung/Apple (e.g. Foxconn capacity levels)

3) input costs through satellite images on primary sector activity (actual mining of the raw material that will feed into the component) with observations on each bottleneck in the supply chain (refinery activity, transportation, etc.)

You use this information to develop a mosaic view of the company -this gives you incremental insight vs other HF or public equity investors. Remember -MNPI is "against the rules" so you are assuming most participants are basing their buy/sale decisions on the information that exists on the sell side model (all known press releases, filings, etc.). Your additional insights from scrapping, surveys, imagery, etc., gives you an edge from a probability standpoint.

Most MMs are looking to trade the quarter, so the view is primarily driven by whether EPS or a KPI will beat or miss- not whether the stock is going to go up or down. The PM will determine the timing (have a view on the multiple and price action).

To summarize, a HF analyst is primarily focused on whether KPIs will beat or miss in the quarter and year but is limited by MNPI. The longest models I have seen were ~10 sheets or so (cover, one for each statement, 2-3 schedules, ratios, valuations, earnings tracker vs. street & guidance, etc.). That information is all required to establish a view on a stock, but MOST of that (statements, ratios, schedules) will come from a sell side model, so you can focus on critical factors.

Dec 23, 2021 - 4:08am

Hypothetically speaking -- the edge is derived by developing insights on information that the sell side has not analyzed, and ideally, that most other buy sider analysts have not figured out/done yet/know of.
 

For example, perhaps everyone already uses glassdoor and indeed data to develop insights on hiring...but you may have a contact who works at a HR company that specializes in recruiting industrial engineers and salespeople. That person gives you color and what they are seeing with hiring trends (ex-MNPI!!!). If you talk to this person weekly, and last week they went from "hiring is continuing at a strong pace" to "hiring dropped off, no one wants to move forward...budgets are being halted", you can quickly look at information across the supply chain and your rough order book analysis to determine what's driving this, and if it'll be material to your company's earnings. A halt in hiring could be either 1) a sign that a company has reached its hiring objectives or 2) that something really bad happened, or something else completely. But the better you get at having sources of edge (mainly: PEOPLE IN OTHER INDUSTRIES WHO CAN GIVE YOU TIDBITS OF WHAT's GOING ON) the faster you will be at taking new information and applying it to your model to see the impact on earnings. 

  • Associate 2 in PE - LBOs
Dec 23, 2021 - 9:33am

Phenomenal answer. You have a paragraph that says "Most MMs are looking to trade…". Can you do an equivalent paragraph for how what you describe plays into the SM space?

Dec 23, 2021 - 10:58am

Remember that IB/PE are "across the wall" - that depth is not allowed for public equity investors. So there is nothing as detailed as a levered fin/lbo or accretion/dilution model on the public side.
 

Going back to the question, SM models are usually similar to MMs except that the longer horizon means the forecast period is years instead of quarters. They still want stocks that'll beat vs street/buy side, and so sources of edge are somewhat similar. However, most SMs are smaller and have fewer resources (and headcount per each decision maker), which mean they tend to specialize in a few key competencies such as legal/corp structure analysis, sector focus, long term estimates (TAM and competitive analysis).

The models don't get more granular than MMs because most SMs will also start with sell side models (financials/estimates/schedules/ratios), and so they may only add 2-3 additional sheets (dcf/valuation, TAM/industry insights, etc.).

There's a gradient between SMs and MMs so keep that in mind. In short, besides the time periods, there is not that much of a difference between SM and MM models.  

Dec 24, 2021 - 2:31pm

ER models have the constraint of being limited to public information. So yes, most L/S models start from an ER model with a few sheets of proprietary value-add. Public equity investing is very different than private equity investing (or m&a). This is why the approach of Icahn, Buffet, etc. doesn't work for the average investor. By and large, Buffet & co. approach companies with the real possibility of buying it out or driving big change. The value-activism-raiding approach is primarily the domain of celebrity single managers and is not "true" long/short equity. By and large, long/short equity strategies are trading-based (P72, MLP, Melvin, etc.) or quantitative. The analytical skill set is similar at the analyst level but a SM PM would suck at an MM and, generally, vice versa.

  • Investment Analyst in HF - EquityHedge
Dec 28, 2021 - 9:27pm

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Total Avg Compensation

January 2022 Hedge Fund

  • Portfolio Manager (8) $1,718
  • Vice President (20) $488
  • Director/MD (11) $434
  • NA (5) $306
  • 3rd+ Year Associate (21) $288
  • Manager (4) $282
  • Engineer/Quant (54) $252
  • 2nd Year Associate (28) $241
  • 1st Year Associate (67) $189
  • Analysts (201) $166
  • Intern/Summer Associate (17) $122
  • Junior Trader (5) $102
  • Intern/Summer Analyst (219) $83