Building Your First Stock Pitch
This article is intended for freshmen looking to understand how to build a stock pitch for their club applications, and sophomores who need to create one for the workshop processes here at IU.
Today we want to take a bit of time and provide some advice for creating your first stock pitch. Whether it’s for a club, a workshop or just for your own personal investing it’s important to understand how you might recognize winners and losers in different industries, and how the screening process differs across sectors. We’ll try incorporating as many examples as possible to explain these concepts. As we’ll continue to mention, these are all just nuggets we’ve picked up from mentors and experiences throughout our time here at IU, and we implore you all to ask questions. So let’s get right into it.
Start With The Industry
Too often, underclassmen (ourselves included) make the mistake of selecting a company to pitch without having the context of industry trends that may benefit or disadvantage the company. In Knall-Cohen, which I (Nikhil) had the privilege of helping lead last year, we deliberately spend all of first semester building towards an industry report to ensure students have the necessary context as it relates to the value chain of a particular business, and how the supply and demand of particular inputs can affect both price and volume.
Now, none of this is to say you should build out an industry report for your stock pitch, but you certainly need to understand how things operate (for example, when I arrived for my first internship at Barclays, I had no clue that, like McDonalds, Marriott is simply paid franchising fees from independent hotel owners and doesn’t own their hotels). I’ll be the first to admit that I use ChatGPT when I’m getting into the intricacies of different industries, and I’ll bump this course that helped me out with prompt engineering for that. But because this understanding of operations is so essential, I always recommend that the first stock you pitch should not be a complex business.
Particularly if you are pitching in an interview, it’s imperative you spend more time shaping and framing your investment thesis as opposed to simply explaining what the company does. This can be tough if you’re pitching a company in industries like semiconductors, healthcare, insurance, or A&D. On the flip side, if you pick a name tied to consumer or real estate, you might spend less time figuring out how the business makes money, affording you more time to put towards thinking about…
Identifying Industry Trends
What are the investment drivers in the space? What do people following the industry currently care about and why? After my internship this summer, I find Mergent Online’s Investext function (here) incredibly useful for staying plugged in with what investors are reading.
Identifying trends sounds easy, in that you can just write about whatever hit words come up most on WSJ articles as it pertains to your industry. But in order to really get a sense of what investors are curious about, take note of the types of questions being asked by sell-side analysts on calls. Realistically, you’re not going to read the last five transcripts for an industry with 10 companies, but maybe you can look at the last two transcripts for those companies and screen for particular words, like “AI” in semiconductors.
Another lens to find industry trends through is supply and demand. One example with respect to metals: investors care primarily about the price of the underlying commodity when analyzing metals & mining companies (e.g. copper for Freeport McMoran). This is because metals companies have a high degree of operating leverage, so any incremental changes in the price of the metal essentially moves directly to the bottom line of the miners.
Note: operating leverage simply refers to how much the company’s profit grows when sales are growing. Companies with high fixed cost bases (like miners) and low variable costs have high operating leverage when the price of the commodity goes up
Now, it’s hard to see that dynamic changing because companies’ high fixed cost bases aren’t going anywhere. So then you focus on what’s driving price, which according to our economics classes, is supply and demand. On the demand side, investor excitement was picking up on the theme of powering generative AI capabilities, which would require extensive electric grid capabilities and, as a result, increase copper demand. On the supply side, I’ll defer to an Odd Lots podcast from May with guest Jeff Currie:
…there (were) structural supply constraints, which we called the revenge of the old economy. Put bluntly, poor returns in the old economy saw capital redirected to the new economy, starving the old economy of the investment it needed to grow the supply base. Pretty straightforward story. Still the story in markets like upper or even oil to a lesser extent, but it's pretty much apparent across the old economy. So structural supply story very much intact.
…if you look at what happened with copper in the 2000s, we went from $2,000/ton to $4,000/ton over that first three years, and then around 2006-2008 it exploded to eight thousand dollars a ton because that's when (miners) started to spend. But they had to achieve that confidence that still is not apparent in this market. And then let's say, you know, the final five to six years is when you begin to deep bottle neck the system and then the investment plays out and you know, it takes another let's say seven years and you get actual supply. So where are we in that process? We're still in that first three years, creating conviction around (whether our estimates are) for real. You need to be above $10,000/ton before people really start to be confident that they can make this type of investment. And I think the other thing too is not only does their prices have to reach those levels where the break evens begin to happen, but they got to go above to create some type of confidence that they have some type of cushion, which means we like to see much higher prices before you start to see that supply response
You’ll notice that supply and demand and the resulting effects of potential mismatches is a key focus across many industries, and I’d encourage anyone understanding an industry to focus on the inputs (supply chain) and the end markets (demand).
Once you can identify the trends that are driving investment in and out of the space then you have a clearer view of …
The Winners and Losers
Now that you know the trends facing an industry, let’s start screening for who is best positioned to succeed. If you’ve done your due diligence on the trends within the industry, the winners and losers should be easy to identify. A simple example: omnichannel retail.
Let’s remember what shopping might’ve been like in 2010. Without the sophistication of shipping networks globally, consumers didn’t shop online as much not because the platforms weren’t there (eBay had ~90m active buyers in 2010), but because shipping took too long. Thus, it made more sense to go a department store, whose offering of goods was diversified enough for consumers to find what they needed.
Then around the middle of the decade, Amazon inflected up their spending on growth of fulfillment centers, which allowed them to meet further last-mile demands (see chart below from Ada Insights).
At the same time, brick-and-mortar retailers like Walmart and Best Buy started realizing that their stores could be used as fulfillment centers for last-mile demands, and started becoming more deliberate with their omnichannel strategies. The omnichannel industry trend was born and they were identified as early winners.
We've invested in our stores to serve as both shopping locations and fulfillment centers for online orders. This allows us to leverage our physical presence to offer faster delivery options, including same-day and next-day pickup.
Doug McMillon - 4Q18 Earnings Call
Taking a different approach, Nike integrated its e-commerce platform with physical stores, ushering in the era of data-driven customer engagement through their direct-to-consumer (DTC) strategy. See the below side from one of my roommate’s freshman year case competitions.
On the flip side of this trend were the department stores, who, instead of diversifying their strategy to cater to a changing consumer, stuck with the old ways of relying on back-to-school and Black Friday, among other shopping events to drive foot traffic. In reality, since 2010, consumers had started liking not needing to go to the store to pick everything up, or even the buy-online-pickup-in-store (BOPIS) offerings that the brands mentioned above were popularizing. And of course, COVID accelerated this trend because department store foot traffic was outlawed. Nonetheless, the omnichannel industry trend is one in which the winners and losers could have been identified and invested in.
Finding Winners That Aren’t Priced Like Winners
And herein lies the tricky part: valuation. Pay attention in K201/K303, folks. You’re going to need to because in order to get good at valuation, there’s no way around using Excel; the earlier you can learn how to use the tool, the earlier (and hopefully better) you can build the model. We’re going to do a few more deep dives into financial statement analysis and model assumptions, so I’ll leave this part pretty sparse before wrapping on types of catalysts. But you should walk away from this article knowing that you can’t properly arrive at a price target for a company without a three-statement model that culminates in some sort of valuation method (DCF or multiple valuation is pretty standard although SOTP can also be used).
There are two sources of capital for a company: debt and equity. Fundamentally, stock valuation proposes the question of what the company’s is worth. To find this, we will often start at enterprise value, which is meant to capture the value of the full company by summing equity value and debt value and subtracting cash. Doing so captures the market value of the company’s equity and the financial obligations of the company, as well as the cash it has to pay off those obligations (net debt). Based off that, we can derive what the stock is worth by isolating equity value (stock price*shares outstanding) and hopefully find some sort of disconnect between the current price to validate an investment decision.
To do so, we’ll project out the financial statements of the company, using intuitive assumptions to eventually arrive at levered FCF, which is what shareholders really care about. Still, we have to account for a risk factor tied to the uncertainty of businesses, hence why we use metrics like WACC and CAPM to quantify the risk-adjusted cost of capital for a company.
Levered FCF is the cash available to shareholders after financial obligations have been met. Unlevered FCF is the cash available to creditors and shareholders. Investors care primarily about how much capital is available to them, hence why FCF is the paramount projection of valuation.
Keep in mind that qualitative and quantitative factors both come into play when valuing a stock. Instinctively, you will either gravitate towards the story being told (qualitative) or you will make the numbers work in a spreadsheet fit for the gods. Wherever your intuition takes you, aim to counteract it by being one of two things.
The most reliable valuations come from imaginative number crunchers and disciplined storytellers.
Aswath Damodaran - “Dean of Valuation” (see his Youtube class here)
We’re keeping this stuff at a pretty high level right now, but we’ll dive further into these concepts in future articles.
Three Types of Catalysts
I’ll leave you with three buckets of catalysts that you can use when creating pitches: revenue growth, margin expansion, and multiple re-rating. Ultimately, there’s a bit of cannibalization here because future revenue growth expectations can lead to a multiple re-rating today, which has been common in the case of AI companies. But we’ll get to that later, and focus on the P/E ratio to wrap this up.
If we look at the above ratio, there are two ways in which the stock price of the company goes up, all else equal. The EPS of the company can increase (earnings driven) while the ratio remains constant or the vice versa (multiple driven) can happen.
Revenue Growth (earnings driven): Revenue is a function of two things: price and volume. If one of these is going up, you’re in good shape. If both are going up, you’re in great shape. Price is a function of supply and demand (see metals example above), and is more important for some industries than others. Price can also be analyzed in terms of the elasticity of the good or service…
The beauty of the SaaS business model has been the ability of enterprise software companies to lock corporations into their software such that the switching costs are unfeasible, and gradually take price. Think about if J.P. Morgan decided tomorrow they no longer want to pay for Microsoft 365. No Excel, no Outlook, no Powerpoint. The migration to a secondary platform (Google, for example) would create a gaping productivity hole in the interim that can be quantified as a switching cost. Hence, J.P. Morgan would be considered a price inelastic consumer of Microsoft 365.
Now maybe one client doesn’t make all the difference, but when you have that moat established with thousands of large enterprises (not to mention the innovation happening with Copilot), you are also able to create high earnings visibility and predictability, lowering the volatility and risk of owning the company, which can be a re-rating catalyst. Hence the cannibalization.
Volume, on the other hand, can be thought of in terms of market share or TAM. Either the pie is getting bigger or you’re getting a bigger slice. Once again, if both are happening, you’re in great shape.
How do I quantify price and volume? Start with a historical context. Particularly in cyclical industries (those that move with the business cycle) like consumer and industrials, there’s usually a lower/upper bound to volume declines/growth in down-cycles/up-cycles. You don’t always have to stay within that range if you think there’s a new driving trend that is either secularly negative or positive, but it can be a good barometer to see if you’re calling for something to happen that’s never happened before. On price, it can be a bit tougher because you might have an industry with price competition where it’s a race to the bottom, or the elasticity of the good or service might be changing. Price dynamics are extremely dependent on industry, so we’d hesitate to make a generalization on how to quantify that.
Margin Expansion (earnings driven): Now, instead of asking yourself how much money the company can generate at the top line, you’re thinking about how much they can keep at the bottom line. You could be killing it with 20% y/y revenue growth, but if your costs ballooned by 35% y/y, your net income has gone down by 15%, which is a negative to FCF, all else equal.
Costs are fun to think of in terms of semiconductors, and Moore’s Law specifically, which has allowed companies to continually reduce the cost of computing power while increasing its capacity. As the number of transistors on a chip doubles roughly every two years, the industry scaled processing power without a proportional increase in costs, meaning semiconductor companies can produce more advanced chips at a lower cost, widening gross and operating margins.
In this context, margin expansion is not only a function of traditional cost-cutting measures but also of technological innovation, as smaller, more powerful chips create pricing power and efficiency simultaneously. It’s interesting to think about margin evolution as Moore’s Law inevitably reaches its physical limits, prompting new innovations in chip architecture and materials science, which could unlock further profit potential.
A few ways in which margins can increase:
Cost reductions from production efficiency
Pricing power from innovation
Operating leverage (see above) - fixed cost absorption
Vertical integration/supply chain optimization
Multiple Re-rating (multiple-driven): This one’s a bit harder to pitch, and can vary depending on the industry. Much of the time, multiple re-ratings are simply driven by future growth expectations, which can be spun as a revenue growth catalyst. But there’s a few considerations that have boosted the company’s value that has nothing to do with earnings…
Capital return policies: whenever management adopts a shareholder-friendly return policy, it tends to attract investment that has nothing to do with the earnings profile of the company. Just look at Meta’s progression from bleeding money in AR/VR to initiating a dividend.
Regulatory backdrop: using utilities as an example, as government spending shifted towards renewable spending, companies tied to clean utilities (NextEra Energy) saw their stocks tear. In my view, this had as much to do with revenue growth as it had to do with having a price-insensitive buyer of their power projects. Doing this decreases the risk profile of the company’s earnings, creating less volatility and justifying a higher multiple
We will explore in future articles why earnings predictability decreases volatility, and how traditional valuation metrics such as CAPM reward lower volatility through a higher stock price
Brand strength: look no further than Costco, the biggest valuation anomaly of them all. Brand strength can carry a multiple past what should be possible based on all foundational valuation theory, simply because people love the story and recognize the moat of the business
This is a tough one to find differentiated information on, however. You can’t just say the company is going to go up because of strong brand strength, there needs to be an incremental piece of information tied to it. For example, in the case of Costco, their ability to raise prices and face relatively little pushback from consumers is a testament to their brand strength, and is a revenue tailwind.
Hopefully, this was a helpful primer on building your first stock pitch, and the examples used correctly conveyed some of what we were trying to say. Please feel free to comment any questions or thoughts you might have!