I initially posted this on twitter -- reposting it here because it's applicable for students interested specifically in the discretionary long/short equity. This style is what you should expect at MMs (podshops) like Baly/citadel/p72/etc. and sector focused SMs with a low net strategy. High net funds (viking) will benefit from this style too.
- Your job is to find attractive trade ideas. Doing so in a repeatable way requires a strong process. This means you need to focus on a coverage group you can maintain through many earnings cycles. Some industries are a coverage group, but most need to be broken down into a specific verticals. For example, breaking up "Media" into "ad tech" and "streamers". When you build your coverage list, include companies from other sectors and industries if they compete in your vertical. E.g. AMZN should show up in coverage of e-commerce, hyperscale/csp, and streaming. Yes, companies exist in multiple verticals. Do this in excel --> list your stocks, briefly describe them, and organize them into verticals (direct competitors). You'll also create comp tables for each vertical (market cap, EV, sales/ebitda/eps est for FY / NFY / NTM, & calculate their valuation e.g. ev/ebitda on a FY, NTM, and NFY basis)
- Limit your initial coverage to 25-30 companies within 2-3 verticals to start. You will expand coverage into adjacent verticals as your understanding of the industry and its dynamics evolve. "Getting deep" into an industry is hard and is "bi-directional" -- you need to complete both a bottom-up and top-down process. The top-down part is done by reading industry research, industry surveys, gathering a list of key words, etc. The bottom-up part requires an initiation on each stock you cover. This is labor intensive and is your "ramp" process, where you need to read (at minimum) item 1 & 7 on the 10-k, last 4 earnings press releases & call transcripts, guidance commentary, and investor day presentations. Your goal is to understand what the business does (where you can describe it in 1 sentence) and what the goalpost for the company is. E.g. A company's investor day deck states "we are targeting $10bn in ARR by 2027" or "we plan to increase production to 100k units by 2030". This is a must have -- it's the "fundamental bet" of a company. Beyond that, analyze the company's product-market fit (how good is the product, what makes it different), industry dynamics (how exposed is it to competition, themes, its value chain, etc.), and the strategic operating plan of management (company's goals/opportunity and how management plans to get there). Use OneNote and Alphasense to help speed up the process and organize your information.
- After industry (and company) initiations, you can move on to an "earnings recap" note.. In your earnings recap, describe how each company did (vs. their own guidance and vs. the street at minimum), what drove performance (commentary from management), guidance (including what assumptions are baked in, e.g. "our guidance of $200mn sales next quarter assumes macro remains soft"), and your updated view metrics (through the forecast period). Try to draw "themes" you are seeing e.g. "coffee chains are seeing higher coffee bean prices but have been successful at passing it through to consumer".
- Now let's get to modeling. Before you do an individual company model, build a "vertical dashboard" to track KPIs, things that impact companies across the vertical, and important industry trends. E.g. Smartphone shipments, market dare data, coffee bean prices. For individual company modeling focus on 2 primary things: an earnings process that allows for a quick update and assessment, and a valuation module. Yes, you need a 3 statement model because you need to do all sorts of analysis for scenarios (more on this later). The more data you have the better, but starting off with 3-yr historical annual data + last 2 full years of quarters is fine. Your forecast period at minimum should be 5 years out. Both alphasense and bamsec have great tools to speed up this process (takes me about 3 min per period per statement, so for 3yr = ~30min, for 8 quarters ~70 min). Your earnings section needs to be set up to make a forecast and quickly assess actual data when it comes in. I always compare my forecast to guidance and the street for each period I forecast-- you should do the same. Include analytics such as q/q, y/y, average q weight, seasonality (ave q %), and 2-year stack. I typically start forecasting at the annual level and then calibrate to the quarterly level. E.g. "company's strategy means they can realize 15% y/y.. seasonally most of their growth happens in q4". Valuation needs to be simple and useful. No DCFs; use multiples. Valuation does not happen in a vacuum -- the multiple is a proxy for the denominator in the Gordon growth/DCF model "(r-g)". To understand how r and g have changed, compare the chg in rates for r, for g you should look at the trend of estimate revisions through the period. Also compare the chg to your peer estimates vs. your co's. If there's no chg to the premium/discount your company is trading on vs. peers, is the multiple really telling you something about a view on earnings? Don't try to trade the multiple-- see it as the money weighted view of the metric. When the multiple is trading below the average, since the last report period, it may suggest the market is expecting a lower metric and vice versa. This tends to be true unless there has been material changes intraperiod. Remember to compare vs. peers (if your co's multiple is higher in the quarter, and so too is all the co's peers, then is that really a strong idio signal? Probably not). Keep your analysis focused on the metric (sales, ebitda, earnings) and try to stick with the average multiple or simply the last print as your baseline.
- Okay now that you generally know the process, how do you find trade ideas? Step 1: Go back to your framework of product-market fit, industry dynamics, and strategic operating plan. With those in mind, read sell side research. Pay close attention to the arguments per sell side analyst and their metric forecast (don't worry about the price target). Lay out the information to understand the spectrum of views. I use a spreadsheet for this (a sheet in my company model excel workbook). Identify the critical factors and what you think would make the sell side analyst change their view. E.g. Bofa Analyst thinks Snowflake billings will reaccelerate because of improving SMB trends. Now you know if SMB trends get worse, that analysts estimates will likely move lower. Step 2: Get hyperfocused on those critical factors -- do everything you can to understand them at the same level or better than the sell side analyst. Step 3: Conduct scenario analysis to understand how changes in the critical factors would roll up into sales, ebitda, eps. Consider the change to growth rates and what that would imply on a multiples basis (look at historical context and spread vs. peers). Step 4: Determine what scenario (or mix of scenarios) is the most likely outcome and what the "path of monetization" looks like. The stock will tend to trade around the midpoint the most likely outcomes. You buy at value when the stock is underestimating the odds of your outcome, but ultimately your payoff is in being right about the actual outcome. The path of monetization is when you look at the calendar and identify the upcoming events that will provide the information that will prove your thesis correct. That could be the upcoming earnings, an investor day event, 2-3 quarters down the line ("factory won't come online until next year, so it's a q4 story"), or even years out. Step 4 requires good judgement -- read @PTetlock's book Superforecasting.
And that's it for the general process! Some additional tips are to write up ideas in memo format to get feedback, have a routine for checking news, and to use a set date during the week testimate revision updates. Can analysts incorporate more into the process? Yes - what I laid out should be seen as the bare minimum. Let me know what you think and this has been helpful.
Quick comment re: sell side -- you are not "trading" vs. sell side, however, they're a useful proxy for the "arguments" and "math" (the scenarios) that the buy side are considering too. It's not so simple as just having a view vs. consensus, but it should start there.
Bookmarked 🫡
Amazing
Great insight! I noticed you mentioned OneNote and AlphaSense to organize information. Do you recommend any other software for a team of analysts to collaborate?
We use MS Teams.
This is great, been looking for something like this for a long time!
Thank you, this is great. I'm currently an MF PE Associate and intend to recruit for L/S equity over the coming months. I'm finding it hard to come up with thoughtful pitches outside of my direct coverage area because I'm not sure where to start filtering the opportunity set.
Would you recommend a similar process for coming up with a pitch, e.g. choose an industry and figure out the winners / losers, or should I just choose a random company and come up with a view? But then again I guess it's hard to come up with a view on a single co without having a view on relative value / industry as a whole.
Would pick a random company you find interesting and form a view. Way too much work to pick up coverage for one, singular stock pitch.
As the other commenter said, it’s a lot of work to do an industry coverage process for just a single stock pitch. I would pick a small vertical (2-5 direct competitors, ideally in same geo).
But why not focus on a name within your coverage group?
I mostly focus on a relatively niche industry (one of energy / banks / real estate) and would seek to pivot towards a more generalist sector (TMT / consumer / industrials) if moving to publics. I was a generalist in banking so was thinking of bringing back those chops and pitching a more generalist name.
Would you generally recommend pitching a name within or outside of the PM's coverage?
Thanks this is very helpful
Thank you so much. This is great.
I don't get this whole HF fund thing (really don't want to be cynical, I'm from Europe where HFs are much less common).
I've always thought it was pretty clear empirically that fundamental-research/stock picking is not able to outperform the market consistently. Most hedge funds that make a killing are using quantitative strategies.
Now I have discovered the HF forum and read, that in US plenty of bankers / business guys (non-quants) still go to HFs doing fundamental research.
Would you say these kind of seats really generate alpha?
I don't think you understand the purpose of a HF (or at least L/S Equity). It's not necessarily a function of generating alpha, but more so generating absolute returns without market volatility. Sure a pension fund can buy the S&P500 index, get their whatever 10-15% (in a good year) and call it a day. But this fully exposes their capital to market swings, whereas allocating their capital to L/S Equity HFs might only offer them 8-10% but with far less volatility.
I think you're confusing HFs with LO Asset Managers, which you could classify as your traditional stock-pickers. Sure, these are having a tougher time as they are typically benchmarked against the market, and comped on relative basis.
Alos, not sure where you're based in Europe, but here in London HFs are very common, with new funds being launched every month
Note: I'm making these numbers up just for the sake of illustration
Thanks for the explanation - as said I really did not mean in cynical and fully admit that I don't have much knowledge about the HF space.
I've studied economics and in my finance classes I've always been bombarded with these "monkey in pine-stripe suit throwing darts is better at stock picking than hedge funds" illustrations and papers that state that most active money managers generate less return than S&P500.
i mean by defnition if you are generating returns without market volatility, that is alpha
unless you are at a fund deliberating taking an "alternative beta" i.e. return for a compensated factor, such as momentum
if you are stock picking at a market-neutral or low-beta hedge fund you absolutely have to generate alpha to generate absolute returns
Think about a normal risk model for equities.
E(r)= risk free rate + b1(f1)+…(b&f&)+ error
Error = the idiosyncratic risk in a name. Contributes ~8-15% to the return of a name on average. Long/short equity is primarily about harvesting the idio returns of a stock. It is not something you can extract systematically (large N). Analysts within hedge funds are the “factor” a PM wants exposure to.
Would say the usual academic critiques (survivorship bias etc) are pretty true for ‘old school’ traditional stockpicking hedge funds. Modern HFs ie the pod shops are pure alpha factories in a way that is very clearly benchmarked, measured, controlled for etc. Citadel et al are where they are because they are consistently able to find and back the best tapent/alpha-generators (while churning out the other 90%) in a way the industry was unable to when it was less mature.
Amazing!
What's your twitter?
Bump
This is super helpful!
Something that I often struggle with is identifying the ways to track and quantify the thesis, especially if most of the incremental news between quarters is qualitative. Is the best practice to just have a range of outcomes and implications on the stock?
For example, let’s say I’m looking into an advertising company that benefits from higher ad spend and I believe it’s going to grow faster than expected this quarter because of a major product launch in the industry that will likely drive an uplift in ad spend. The news about the major launch would be well known but the key debate would be how much would it impact ad spend. If there’s no concrete data here, then I’m ultimately just guessing and there wouldn’t really be a way to track my guess ahead of earnings. Is the idea to just be prepared with a range of outcomes and act quickly?
Yeah that's the starting point.
For the co, you may have a few scenarios outlined: (1) ad spend rising materially, (2) normalized ad spend, and (3) weaker ad spend. Each scenario will have an associated "path of data". For example, in scenario 1 you would expect ad spending data to increase each month or qtr. Assuming no one knows the future and no incremental info is available, the stock will roughly trade at the midpoint between the three scenarios, which is reflected in consensus estimates. If you think scenario 1 is going to be hit, then you price that scenario on today's (consensus) multiple.
As you go through the week, some news/eco data/commentary may shift your understanding. E.g. This week you have a call with a channel partner who sells ad space to the retail channel. The partner comments that he's seeing ad spend decelerate right now, but generally thinks things will be "okay". This commentary tracks to scenario 2. As you get more info that tracks towards a thesis, you will begin to have a view and build conviction in it.
Doing the work beforehand allows you to have a good understanding of what the path of data needs to be for a thesis to succeed. For example, if economic data is strong then you should expect that scenario 3 is underpriced (because lower odds of weaker ad spend when eco data is strong).
Here's a basic illustration of how this works in the real world:
1. You establish a base case view from management. For example, Nutanix CEO is guided to 20% ARR CAGR by FY26 in their November Investor Day presentation. The street initially thought the outlook was conservative and estimated closer to a 27%+ CAGR; but after their last earnings, revised it down to the ~20% the company guided to:
2. They had a strong beat & raise to end CY 23, with ARR of 30%. However, their CQ1 24 report came in a bit softer, which means those upside (right-tail) estimates needed to come back down (unless you think last fq had seasonal chunkiness that should be seen-through).
3. The NTM multiple rerated lower after earnings, but you think multiple can expand modestly back to ~40x from 35ish due to strong execution and ARR growth on track. The multiple above 40x would need to see a reacceleration beyond the terminal ARR implied by guidance (20% CAGR).
4. Your read throughs and research would need to narrow in on why last fq was soft (slower new logo wins), whether the company can see reacceleration w/o lots of new logos (can expansion alone drive higher ACV/ARR?), how does macro factor into this (if macro flips, is upside back on the table?), and was the 30%+ scenario contingent upon VMware displacement (share gains from VMW shedding customers) or can it happen w/o?
5. Data points are incremental if they add insight to those questions (critical factors).
tracking investment thesis
wow, great color
Another common idea I’ve heard is that you don’t necessarily want to come to a single price target but instead view the stocks through the lens of a risk/reward to focus on range of outcomes.
Your process seems to use the scenario analysis/range of outcomes for a single price target, which seems flipped. Is there a reason for doing it one or the other?
Also, in your process, you find the risk/reward with bull and bear scenario outcome and not the base case, right?
Wow
You think the same applies to a REIT? How would you do it if so
Haven’t traded REITs but would assume it’s similar. Just need to know what the “fundamental bet” is.
dint mean to throw that, misclicked mb
First of all, thank you very much, it is super insightful!
Are there any good resources to learn how to build an efficient Excel spreadsheet for stock coverage analysis as well as L/SEquity-style valuation?
Thank you
Start with learning how to model 3-statements. There are lots of training programs that can teach you that (e.g. Wall Street Prep, BIWS, etc.). From there, learn how to model the quarter (Gutenberg Research - Crowdsourced Earnings Modeling Community his book is probably the best reference for this style).
At this point, you now need to learn how to model companies within the vertical or sector you cover. In software, find a book on SaaS modeling (Barclay's has a guide which is very helpful for beginners too). For energy, it is going to be something else. Focus on is how to model revenue and costs within your space.
At the company level, you will draft a revenue build from the company's earnings press release or regularly released investor package (presentation or data file, etc.). This workflow is very specific, but the lessons you have learned in the vertical/sector revenue build side will help you understand how the company's KPIs roll up into revenues, margins, etc. Analysts who cover the stock will know the company's lore and where to find new information. For example, AMZN's RPO is published in the 10-Q in a sentence -- this is a major KPI that AMZN will trade on, and it's not laid out to you on a silver platter. Expect each individual company to have it's own unique workflow. There are some parts that get harder, e.g. a company may provide a data point only on call backs (when IR/mgmt calls sell side or key buy side investors after the earnings call).
Valuation is tricky -- besides what is generally known about valuation (value of a security = sum of future cash flows discounted to present value), there aren't many great practioner books on valuation. I recommend sticking to market multiples for now and then joining a team/pod where you can learn how they apply valuation.
Maybe I'll write a post on valuation one of these days. :-)
Would really love to hear your thoughts on valuation; especially any color on differing philosophies between the pod world and the more typical positive net funds.
I feel like my struggle at times has been balancing the basic principles of it all, with the realities of how the market operates.
First derivative application is the classic value investing process: a DCF very clearly illustrates what the value is for a business that has xyz when it comes to the growth rate, margins, reinvestment rate, and discount rate. Compare to today's stock price. Sensitize and assign weighted probabilities to diff. assumptions, and you have a straightforward risk/reward.
In practice this is only useful for sanity checking and establishing wide bands of value. Lots of extrapolation occurs based on the most recent data/history, as well as the broader market + sector backdrop, and the prevailing narratives. Trends in KPIs for the company (what ultimately drives changes on the margin for the key inputs above) matter most, so the direction of revisions and results matter more than just being at a discount historically / to peers, or a discount to "intrinsic value" - which btw I've gotten farther away from believing such a thing.
But where I've struggled in the past is underestimating or overestimating multiple expansion/contraction, and I've ended up gravitating too much towards the longer term view, or that a company with xyz profile + historical + peer comps "should" trade in this range. VRT is a good example, and I underestimated how much more the multiple would expand, and I had rimmed some of the position in my PA early as the risk/reward didn't seem that great anymore. I know not apples to apples for a pod shop trading comparison, but wanted to mention it. But narrative (namely that AI was one of the few areas to have a lot of conviction for 1H24, amidst a sea of uncertainty), kept the story going, as well as the fact that demand has continued to outpace supply here and that the direction of numbers keeps improving. This is where it feels like "normalizing" growth rates for Y+1+2+3+5, or establishing a longer term assumption for DCF bands, goes a bit out the window - or that you just need to lean into the weights on the tails more.
I'm not trying to say I anchor everything off a DCF btw, in fact mostly the opposite. Obvs multiples are an efficient shorthand for a reason, and I recognize that embedded in the math of a multiple are the exact same assumptions of the DCF. But I've struggled to deal with companies that seem to perpetually trade at extended multiples, where they always "feel" expensive. My sense has been that on the margin for making money, its somewhat irrelevant at times. This is the multiple awarded to these kind of stocks in this sector with these trends or revision path, and just using them without getting caught in the weeds of it all is most efficient.
My other thinking has been that the trajectory of value for most companies can look like a sin wave (either upward or downward slopping over the long term), and you get deviations into peaks and valleys based on what I've shared already.
Although we haven't even covered the dynamics of peak on peak or peak on trough multiples here, but I word vomited a bunch and will prob get a few downvotes, so I'll leave it at that for now.
Super helpful post. What’s your twitter handle if you don’t mind sharing?
How much time do you spend to stand up an entire individual company model? I know you gave some time estimates for filling out certain periods, but to start annual and refine to quarterly with all historicals you want + functioning projection period, mgmt KPIs from press releases & transcripts, and your own metrics on those historical financials like salesforce efficiency, incrementals, etc.? My working process has been to start with some base industry / company reading and then get models set up before I dive into key analyses / calls as I imagine the model can inform those calls/analyses. But I’m having a hard time figuring out much time to sink into the model
This is very useful, thanks.
For the modelling, would you not recommend doing your regular DCF as well? If so, would you use CAPM or exit multiple?
Ut sunt minima provident explicabo qui eos. Commodi incidunt quidem ut id tempora in placeat. Accusantium sit mollitia delectus exercitationem rerum delectus veritatis.
Animi ipsa tempora itaque et. Veritatis eos molestias et fuga consectetur sequi ut. Tenetur facere quibusdam doloribus.
Harum voluptatem quibusdam fugit non sed. Ad rerum dolor itaque quam reprehenderit rerum sunt est.
Alias asperiores occaecati qui fugit. Porro consequatur eligendi animi neque quam distinctio.
See All Comments - 100% Free
WSO depends on everyone being able to pitch in when they know something. Unlock with your email and get bonus: 6 financial modeling lessons free ($199 value)
or Unlock with your social account...
Rerum voluptatibus et incidunt exercitationem. Quis expedita soluta dolor labore dolore minus dolorem. Facere aut quis a nemo voluptatibus aliquid. Deleniti rerum eos labore praesentium deserunt. Similique explicabo cupiditate exercitationem ea ut libero. Voluptatibus omnis vero sed quas id quia ut. Excepturi repudiandae provident nam fugiat ut.
Sit qui quia at est quo unde iste. Labore debitis impedit tempora nemo. Delectus aut nihil autem numquam. Numquam nihil iste ut est earum eos. Itaque consectetur eum tempora aperiam ipsa consequatur eveniet.