Hedge Fund Case Studies 101, Part 3: How to Build the Financial Models You Need to Land Offers – and Put Together Your Stock Pitch
Numi Advisory has advised over 400 clients by providing career coaching, mock interviews, and resume reviews for people seeking jobs in equity research and investment management (full bio at the bottom of this article).
One of the many benefits of a multi-part series like this is that I can drive you crazy – or at least make you keep reading – by withholding key information until the end.
In this case, though, I’m going to do something even better and give away some of it here in Part 3, before the conclusion to the series in Part 4.
This time around, we’re going to turn our attention to financial modeling and valuation in hedge fund case studies and learn what you need to know, how it’s different, and how much detail to provide with limited time and resources.
Our interviewee returns today, with even more tips to share on all of those topics – plus the all-important “how to put it all together and make a coherent presentation” point.
Let’s get modeling:
The Purpose of Models in Hedge Fund Case Studies
Q: So let’s talk about my favorite topic in the world: building financial models.
You mentioned in Part 1 and Part 2 that you’ll always need a model or valuation for use in your case study… and we know that in PE interviews, the purposes of these models is to answer the question, “If we buy this company, are we likely to hit our targeted IRR over 3-5 years?”
What’s the purpose of models for HF case studies? Valuation?
A: Most of the time, yes.
If you’re interviewing for an event-driven fund, you might analyze an M&A deal or some other type of event, but most of your modeling work in HF case studies will be valuation-focused.
More important, though, is how you use the valuation to justify your investment recommendation:
- If you’re making a long recommendation, you have to argue that the stock is undervalued, and ideally that a) Even in the worst case scenario, you stand to at least maintain principal or not lose money; or b) That while you could lose money in the worst case scenario, it’s an asymmetric risk profile because there’s (for example) a 50% chance of losing 10% and a 50% chance of gaining 40%.
- If you’re making a short recommendation, you argue that the stock is overvalued, and ideally that a) Even in the best case scenario, you won’t lose (much) money; or b) That while you could lose some money in this best case scenario, it’s also an asymmetric risk profile because there’s a 50% chance of the stock declining by 40% and a 50% chance of it increasing by 10%.
You could also use fairly simple financial analysis to justify your recommendation and explain why the “downside case” doesn’t present that much risk.
For example, maybe the company trades at only a 10% premium to its tangible assets (not too likely these days, but you never know).
Q: Great, so it sounds like scenarios could potentially be very important depending on how you’ve set up your model.
Before we jump into that, though, what’s the overall process that you recommend for building valuation models? And what methodologies do you use?
A: There is nothing special about the methodologies – you still use public comps, precedent transactions, and the DCF unless it’s an industry where one of those doesn’t apply (e.g. the NAV model for oil & gas companies or the dividend discount model for commercial banks and insurance firms).
You should not spend hours and hours adjusting for non-recurring charges and taking care of minutiae – your time is much better spent picking solid companies and transactions in the first place and finding data to support your predictions for the company’s own performance.
Here’s the process I recommend:
- First, identify the 2-3 key drivers for the company in question. For many companies, these will be variants of “units sold” and “average price per unit,” and your job is to figure out what’s driving those.
- Next, gather historical data on the company – not just their financial statements, but also how these metrics have trended over the past 5-10 years. If it’s a retailer, for example, you’d look at trends in # of Stores, Sales per Square Foot, and Square Feet per Store over that time period.
- Build a 3-statement projection model for the company over a 3-5 year period, with these key drivers as your inputs to the model. Do not obsess over small items on the statements or items that do not change (if it doesn’t balance, simplify and tweak). Focus on the main value drivers: revenue growth, EBIT/EBITDA margins, and CapEx and (maybe) Working Capital requirements. Make sure you can make simple adjustments to these variables in your model.
- Select public comps and precedent transactions for the company – use the “Related Companies” feature in Google Finance to find and screen comps. Precedent Transactions can be more time-consuming to find and are generally less important than public comps anyway, so you can even leave those out if it’s taking hours and hours and you’re not getting anywhere.
- Build a DCF analysis for the company where you can vary the discount rate, the Terminal Value, and the 2-3 key drivers you identified in step #1. This analysis should be linked to your 3-statement model.
- Summarize your findings at the end and come up with a valuation range for the company. Again, you need to be able to say something like: “In the worst case scenario, we expect a value of $XX per share, and in the best case scenario, we expect a value of $YY per share.”
Level of Detail Required
Q: Thanks for the outline. I think most readers are familiar with the basics of valuation, so the more interesting part is probably what happens in steps #1-3.
What level of detail do you need?
A: It depends on the fund you’re interviewing with. At many long/short equity funds, they won’t necessarily value tons of detail on the revenue and expense drivers. However, they want to see that you understand the key drivers of the business and how it makes money, and your model should reflect this understanding as well.
However, I have seen more activist-oriented funds and funds with a lot of former private equity guys that do care about granular detail and do want to see that you’ve thought through those metrics.
Also be aware that some funds are betting on quarterly results, in which case more details around metrics like same-store sales and the performance in individual stores and/or geographies might help you.
So I hate to give you an “It depends” answer, but it really does.
You need to think about the fund’s strategy and what their portfolio looks like – the more diversified it is, the more of a “high-level view” they’ll take and the less they’ll care about granular details.
Q: OK, so maybe we could take a specific example here… let’s say you’re pitching a healthcare company. Should you project revenue from each major business line (e.g. primary care, specialty care, consumer healthcare, and nutrition)?
Or should you go down to the level of individual drugs or treatments? Or do something else entirely?
A: It’s almost impossible to go down to the level of individual drugs (or products, or retail stores, etc.) in the span of a few days, and you’ll probably lack the data to do so anyway – so I would avoid that.
Maybe if you’re 1000x smarter and more efficient than the average person you can try it, but otherwise avoid it.
Another risk is that the more time you spend on this exercise, the greater the chance of a stock moving away from you. So it’s a trade-off between time and production. You really want to think of the 80/20 rule here because you just won’t have unlimited time and/or data.
You do want to make it more detailed than a simple revenue growth percentage projection, but using hundreds or thousands of individual drivers is overkill.
The same applies in other industries: for oil & gas, sure, maybe run a NAV model for each of the company’s major geographies, for a commercial bank you can project the 3-5 major loan categories separately, and for Apple you can look at the iPhone vs. iPad vs. iPod vs. laptop and desktop segments separately (but please, don’t use Apple to begin with).
Q: And are there any special metrics or ratios you should pay attention to?
A: Personally, I am always cash flow-biased.
That might be because I’ve worked in PE before, where cash flow truly is king – but it’s also because other metrics and ratios can disguise how well a company is actually doing, whereas it’s harder to do that if you scrutinize cash flow generated (whether it’s Levered FCF, Unlevered FCF, or another variation).
Then you have the usual profitability margins and growth rates, all of which are useful for comparing the company to its peers.
One metric I like to look at is Return on Invested Capital (ROIC), which is Net Operating Profit After Taxes / Invested Capital.
“Invested Capital” can be defined a couple different ways, but some people use Total Assets – Cash & Cash-Equivalents – Non-Interest-Bearing Current Liabilities – (Occasionally other operationally-related long-term liabilities such as Deferred Revenue and Deferred Taxes). For some companies, it’s close to Total Debt + Preferred Stock + Equity.
NOPAT, or Net Operating Profit After Taxes, is basically just Operating Income * (1 – Tax Rate), similar to the calculation starting point in Unlevered FCF.
This one is important because any business can grow… but at what price?
All else being equal, it’s better to invest in businesses with a higher ROIC because each dollar of investment results in higher growth.
Q: But wait a minute, you’re using tax-effected Operating Income as part of that calculation, which is usually very far off from Free Cash Flow…
A: Right, that’s why it’s only one of the metrics you look at.
Cash flow is still king, but you do also care how much a company can grow its operating profits based on each dollar invested.
Where to Find the Data
Q: So I think everything you’ve described so far makes sense – but finding all this data for use in your models is another issue. What do you do when you have very limited time?
A: There are a couple tricks you can use to save time:
- Start with sell-side equity research models – don’t rely on them for projections, of course, but some of these models have tons of historical data in them, which will save you a lot of time. There are some risks, but that is one method.
- Also remember that most companies actually have historical financial statements IN Excel in the investor relations section of their websites. This may sound silly, but I’ve seen many people waste time inputting all the historical data manually.
You could also start with an existing 3-statement template or valuation template and change around the revenue and expense assumptions, but that tends to work better if the template is for a company in the same industry.
Q: Yeah, those are good points to keep in mind. But how do you actually come up with the projected numbers in a model?
Let’s go back to the retail example – you’ve said you shouldn’t rely on equity research, so where would you get numbers for the # of New Stores Opened, changes in Sales per Square Foot, and so on in future years?
A: The easiest approach is to take a look at what estimates already exist and then modify them based on your primary research (interviews with customers, suppliers, executives, reading reports, etc.). For example:
- The company’s most recent investor presentation has extremely optimistic projections of 50 new stores being opened in the next year, and the company currently has 1,008 stores. Management is also predicting that average Sales per Square Foot will increase from $333 to $372.
- But then you speak with three of the company’s biggest vendors and find that pre-orders for this next year are lighter than expected and that there’s still a lot of unsold inventory from the past holiday season.
- You also check the earnings call transcripts of a few peer companies and find that economic conditions in the Southwest of the US have been worse than expected, resulting in lower consumer spending; 20% of the company’s sales come from there.
Projections in investor presentations are almost always incredibly optimistic, but in this case there’s evidence that sales growth will be more sluggish than the company is expecting.
So you’d run the numbers and see what slightly lower growth in the Southwest and in the product categories of those three largest vendors would correspond to, and maybe you’d reduce the estimates, say that 35-40 new stores will open, and assume that Sales per Square Foot will only increase to $355.
You would continue to do that for the rest of the projected years as well; it gets less and less accurate by the time you reach year 5, but for valuation purposes the next 2 years matter the most anyway.
Q: Great, thanks for explaining that in detail. So what are the risks of using equity research for parts of this process?
A: The main problem is that you may not be able to justify the numbers.
That can be a big issue if the PM of the fund, or any interviewer, points to a random number in the model and asks where you got it from.
It doesn’t matter as much for the historical metrics, but you need to be prepared to justify everything in your model.
The Best Case Scenario…
Q: Awesome. I hope everyone’s taking notes.
So you’ve been mentioning how important it is to consider the Downside case and what happens if a company completely tanks.
Should you include multiple scenarios in your model, e.g. with the CHOOSE or OFFSET function in Excel and different numbers for the key drivers in the future period?
A: If you have the time to come up with those different sets of numbers, yes, it’s helpful.
But make sure that your Downside case is a TRUE Downside case if you want to go that route – I’ve seen models where someone says the Upside case is a 50% increase, the Base case is a 40% increase, and the Downside case is a 30% increase.
Q: Yeah, that’s a good point to make. In our case study on Best Buy, the Downside case has the company dropping from $50 billion in revenue to only $30 billion over 5 years.
A: That sounds a little extreme, but anything is possible when the company is suffering and facing potentially major structural headwinds.
Remember that you’re focused on those asymmetric risk profiles – if your model shows that the company has a chance of going up 50% in the next year in the Upside case, going up 10% in the Base case, and falling 10% in the Downside case, that could still be compelling because the “expected value” is a 17% increase if you assume equal probabilities.
Q: Besides this argument about the probabilities of various scenarios, how else could you use the valuation in your case study?
A: You could also look at the output from the Downside case and say, “Even if the company performs poorly and the Downside scenario comes true, it would still be undervalued… according to the implied valuation range across all methodologies in that scenario” (or vice versa for a short recommendation).
And you could also use the valuation to figure out where in the cycle a cyclical company is – so if it’s something like chemicals or semiconductors and you can compare the current multiples to historical multiples and argue that it’s entering an expansionary cycle (or the opposite for s short), that’s another option.
Finally, there’s the simple argument that you see in banking all the time: that if a company is in-line with its peers in terms of revenue growth and margins but trades at a different multiple, it may be overvalued or undervalued.
Modeling On the Job
Q: Thanks for pointing out those use cases – they’re certainly useful if you don’t have much time to make your case.
Out of curiosity, how much does all of this help on the job? Is it similar to what you do in real life at a hedge fund?
A: Again, it depends on the type of fund and the background of the people there. Some will be more modeling-intensive than others, but there’s always some level of technical work required.
If you get a job at a buy-side firm, they want to see somebody who can hit the ground running as much as possible because their time is money. In fact, you’ll probably have to show that you can model even before you’re considered for an offer.
So modeling is definitely important, and anything you can do to learn valuation and how to build a clean model in advance will give you a big leg-up.
That’s why I’ve recommended your financial modeling courses to clients, and will continue to do so – they’re the most effective way to learn these skills and prove that you can do all of that and more.
Q: Wow, I didn’t want you to shamelessly plug my products and services. Yours are acceptable to promote, though.
A: We’ll get to that at the end…
Putting It All Together
Q: OK, so let’s take a step back and put everything together… in Part 2 we went through an outline of what a stock pitch might look like, but maybe we could extend that here and include more on the valuation side.
A: Sure… here’s how the pitch began (the Recommendation part):
“I recommend shorting Retailer X, which currently trades at $45.00 per share, because it’s overvalued vs. peers by approximately 20-25%, its key metrics such as sales per square foot have been stagnant despite rising valuation multiples, and the company has had increasingly poor transparency on many of these key metrics over the past few years.
Catalysts to push down its stock price in the next 6 months include the expiration of key reseller agreements with 3 of its top 10 vendors and the first earnings results post-acquisition close of a smaller retailer last year. Investment risks include potentially better-than-expected integrated company results, as well as faster-than-expected market growth, but we could mitigate those by longing one of its competitors to reduce potential losses from unexpected market growth.”
And here’s how you might fill in each of my recommended points in more detail:
- Company Background: Summarize the company’s business, key geographies and customers, and what its current market cap, valuation multiples, and growth and profitability are.
- Investment Thesis: The company is overvalued by at least 20-25% vs. peers, but consensus hasn’t “noticed” because the company has done a good job of obfuscating key metrics and not disclosing its reliance on its top 5-10 vendors. Its recent acquisition has also under-performed relative to expectations and the premium paid.
- Catalysts: The upcoming renewal of key vendor contracts and the first earnings call post-transaction-close could push down the company’s stock price as the market finally notes its weaknesses.
- Valuation: Even in an “Upside” scenario, with Sales per Square Foot and Total # Stores increasing at a 5% premium to management’s expectations, the company is still overvalued by approximately 5-10% vs. peers; and in the Base case and Downside scenarios, which have been reduced from consensus estimates based on conversations with vendors and suppliers, the company is overvalued by 15-25%. These results hold for both public comps and the DCF analysis (you would paste in the output for both here).
- Risk Factors and How to Mitigate Them: One of the company’s key geographies could produce faster-than-expected growth, or the post-merger integrated results could be better than expected. To hedge against that risk, we could long a competitor in that geography, or long another company that has made a similar acquisition recently.
Q: Great, thanks for expanding on that. I think a lot of people struggle with the risk factors and how to mitigate them. Any tips if you’re presenting a Long recommendation?
A: You could take a similar approach and recommend shorting a peer or buying a protective option, but you could also raise other points:
- Sustainable Competitive Advantage: Does the company have a key patent, legal ruling, or regulation in its favor? Does it have a “network effect” in its business (e.g. Facebook) that protects it from competitors?
- Cash on Balance Sheet: A high cash balance and/or tangible assets can also be a hedge against an extreme downside scenario.
- Low Valuation Multiple: And finally, of course, if the company is trading at a low multiple already, that is arguably a hedge as well because its valuation couldn’t fall as much as a healthier company’s valuation could. Just be wary of value traps.
Q: Great. Any other good sources for example stock pitches and case studies?
- Seeking Alpha – Some good material but there’s no filter or quality control, so “browser beware.”
- Value Investing Congress – Even more.
Finally, check out any of David Einhorn’s presentations – the one on Green Mountain Coffee is fantastic and (temporarily) resulted in a massive drop in the company’s stock price.
The Top Case Study Mistakes to Avoid
Q: Yeah, if you Google “Value Investing Congress note” each year, you can get stock picks and examples of the rationale used by some of the top fund managers out there.
Before we move on, any thoughts on the top mistakes to avoid in case studies?
A: Besides the “not giving a clear recommendation” one, a couple I’ve seen before:
- Weak Risk Factors and Mitigants: This is very common if you’re moving in from banking or sell-side research, because you rarely think about these factors there.
- Being Unable to Explain Part of Your Model: They could point to any number included anywhere in your presentation or model and ask you to explain it. If you don’t have a set of notes in front of you that explains where the key numbers came from, you’re shooting yourself in the foot. And yes, bring these notes to the interview.
- Lack of Energy / Conviction: If you had $10 million and were not 100% ready to put your own money behind this idea, you’re doing something wrong. Much of the job is selling the PM on your ideas, so you need to express this enthusiasm from the start.
Q: All good points. So with mistake #2, what happens if you really don’t know where some random number came from? Are you screwed if you can’t explain it right away?
A: I would offer to get back to him and explain it. Say something like, “I don’t know that exact number. No one else has asked me before. But I understand why it’s important. I can get back to you later with that number. But just at a high level, my intuition leads me to think X, Y, and Z. And I’m happy to follow up in an email afterward.”
But if you say something like this, you really do need to follow-up afterward or you look even worse (yes, they will remember it).
Q: Very similar to tips for sales & trading interviews when problems come up there as well…
So, now, moving onto our real example of a case study that could use improvement and how we can give it a “makeover”…
A: Next time, next time…
Coming Up Next
Yes, we’re almost done – admit it, you’re a little sad, aren’t you?
Complete Hedge Fund Case Study Series:
- Part 1 – Hedge Fund Case Study Overview
- Part 2 – How to Generate Investment Ideas and Research and Structure Your Case Study
- Part 3 – How to Model and Value Companies and Deals for Use in Your Case Studies
- Part 4 – Walkthrough of an Actual Hedge Fund Stock Pitch / Case Study
Numi Advisory has advised over 400 clients by providing career coaching, mock interviews, and resume reviews for people seeking jobs in equity research and investment management. With extensive investment experience in equity research and private equity and now working as an analyst at a long/short equity hedge fund, Numi has unparalleled insights into the recruiting process and advancing on the job.
Numi customizes solutions to each client’s unique background and career aspirations, and teaches clients the most efficient and impactful methods to achieve successful results on their career search. He has helped place over 50 candidates in leading buy-side and sell-side jobs. For more information on career services and client testimonials, please contact firstname.lastname@example.org, or visit Numi’s LinkedIn page.
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