As per title. I generally find models to be a complete waste of time in most cases. It helps to understand the drivers of the company’s financials but forecasting quantitatively and accurately seems to be a complete waste of time versus all the qualitative/directional feedback you get or alt data stuff. My PM still wants them done up so I get it done but wondering what others think
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They're fine tools for people to get geeked up about, but I agree with you they're not the end all that most people think.
It’s so people can point at something not to really get to an answer.
People don’t really follow sell side models because they track some version of the market anyway. On the buyside, there’s some degree of backing into what assumptions there needs to be in order to hit a minimum threshold and asking if that’s possible or reasonable and what probabilities are for the company to hit said numbers to achieve the valuation.
But everyone is really just using models to ‘point to’ something and have it as a basis for financial due diligence and a record of fiduciary duties and or taking the proper care and all that. Whether it ACTUALLY matters will depend on if it’s a good story ALREADY
Agree and would also add that models are good for laying out your thoughts on paper to see and compare the magnitude of impact to returns of various hypothetical scenarios you want to explore. Once you have those scenarios mapped out it is up to you to determine what you think the likelihood of each occurring is, or for private market investors, which avenue of return generation you want to pursue.
It helps to generate a range of outcomes, not a point estimate (though many shops use it for only the latter purpose). And based on current stock price, does it provide asymmetric risk/reward? As your thesis is proven out, you continue to underwrite the thesis based on incremental information (the rigor of using incremental information depends on the style of investing) and assess whether the asymmetry persists or it's time to move on.
It's more difficult to model businesses with embedded optionality and good management team that keeps on executing in an unexpected but beneficial ways. Again, this is moment where some firms prefer to be precisely wrong, while some others prefer to be roughly right (or don't even try) because they are focusing on the qualitative.
My analyst’s models are so accurate they’re practically crystal balls.
Which markets they forecast?
Mostly derivatives on MD’s rage level
I just like watching my models walk away
Do I believe in models I make? Haha no…
IMO models add the most value when you evaluate the most downside case. Modeling growth in a business, even if modest, isn't really that useful given you don't know if the business will grow going forward for a multitude of reasons. But understanding what would happen to the business if things went to shit is valuable as you can really bottom out what your risk is - best case scenario. Otherwise the models aren't all that useful given how many variables are involved
Some decent responses already, but the value of models (I will refer to models here as specifically DCF) isn’t to be able to predict the future better than others (no one can do this well), it is to test out difference combinations of growth, profitability, and risk to see what is priced in and the range of possible outcomes that could play out. When you find a stock that is currently discounting an implied set of expectations that seems to be more bearish than your expectations, or that is on the lower end of your range of scenarios, the risk-reward is favorable.
Some common pushbacks on DCFs: “oh you can make them say whatever you want”; “They’re so sensitive to the inputs”; “All of the value comes down to the terminal value anyways”.
1.) Yes, you can make them say whatever you want, that is what makes them valuable. You can isolate particular variables for sensitivity, and you can construct varied scenarios and combinations of growth, profitability, and risk to assess the full risk-reward distribution. You can also identify which variables are the most impactful to the valuation (growth for a high ROIC company, ROIC improvements for a high-growth company) to give yourself a better frame for evaluating incremental changes over time. 2.) If you use a three-stage model (as I do), it takes all of the heavy lift out of the terminal value. Further, this is a fundamental truth of equities as going concerns that the bulk of the value is far out on the timeline and not overly dependent on what happens in the next 1 to 2 years. Equities are long-duration assets. 3.) Just because you aren’t modeling out the variables explicitly when relying on multiples does not mean that you are not doing a DCF. The value of an asset is the value of its future cash flows discounted back to the present, period. Just because some choose to ignore this reality does not mean they are avoiding it. It is blissful ignorance.
The last thing I will say is that building the model, if you are doing it correctly, gives you a much stronger appreciation for the business quantitatively. It forces you to dig into the notes and finer points in a filing and actually think about each and every line item. It gives you a better feel for the underlying mechanics of the business, how it makes money, and sources of financial risk and opportunity for improvement.
As you can probably tell, I value modeling fairly highly, but this doesn’t mean that I don’t use multiples or other techniques. Valuation should be multi-dimensional with checks against different tools built into your process. There is no one perfect way to do something. My modeling process is my core approach, but then I surround that with other views of the valuation for context and balance. People like shortcuts when it comes to investing, but shortcuts typically don’t have high ROIs IMO.
I like this quote from statistician George Box that I think sums up my view. TLDR: “All models are wrong, but some are useful.”
Are you the author of 'Pitch the Perfect Investment'?
The value of a model is just to force you to explicitly state your assumptions so they can be checked for plausibility. It's easy to say "I think Apple will sell a lot of iphones, therefore $300 price target!" and who knows, I can't tell if you're right or wrong. But if you explicitly spell out "$300 price target is justified by a PE multiple of 30 from making X dollars by selling Y phones at average price Z" then you are forced to check each of those numbers for plausibility and revise anything that's obviously too optimistic.
Yes absolutely. You create a thesis with words and then you explain the thesis with numbers via a model. This is one of the basic principles of investing I think
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