If you have the time, please share a bit more detail about the modelling approach + assumptions used for the DCF. I'd find that pretty interesting and suspect o

If you have the time, please share a bit more detail about the modelling approach + assumptions used for the DCF. I'd find that pretty interesting and suspect others would also. Whenever I've tried to run my own DCF analyses, I've found some parameters and modelling assumptions have a major impact on the resulting estimate. Discount rate parameter is one. The model and assumptions used to estimate revenue and operating/net profit when extrapolating 5 - 10 - 20 years into the future also make a large impact. Suppose in our DCF analysis we aspire to forecast company performance over a 25 year time horizon and then discount the profits back to some NPV. Then we need some way of extrapolating the company's revenue for the next 25 years. One way to do this could be to put together some kind causal model where we identify the major factors that contribute toward generating revenue -- maybe this could break down revenue by segment or major product line, and attempt to predict supply, demand, price points, production capacity etc for each product line, using knowledge of how the industry works. Then we'd have to figure out how to forecast all of those drivers of revenue, e.g. forecasting the capex used to build new plant to increase capacity & so on. Another way could be to ignore trying to build a semi-plausible generative model for revenue and instead just do some kind of simple empirical model only -- e.g. fit a regression model to the trailing 5 years of historical revenue numbers or annual revenue growth rates combined with some kind of prior to force revenue growth to decay toward something unobjectionable the further into the future we project. I've got some code that automates calculating a DCF. I've adopted some form of the latter approach to forecasting revenue -- fit a simple empirical statistical model to historical revenue -- it isn't very intellectually satisfying as it doesn't incorporate any real world knowledge about the causes of revenue in specific industries or companies. So, many forecasts of revenue generated by this approach will be quite wrong, leading to quite misleading DCF estimates. But on the other hand, I can run my kind of dumb DCF analysis in a completely automated way using inputs that are reported in company financials in a standard format, which is much quicker and easier. When i run my crude DCF analysis over INTC and AMD -- using a somewhat arbitrary 6% discount rate -- I get the estimates INTC -- priced by mr market at $48.07 / share quantile 0.2 $87 / share quantile 0.5 $140 / share quantile 0.8 $196 / share AMD -- priced by mr market at $108.41 / share quantile 0.2 $42 / share quantile 0.5 $77 / share quantile 0.8 $121 / share the "quantiles" are different scenarios of assumed future revenue growth trajectories. quantile 0.2 assumes relatively poor revenue growth. quantile 0.8 assumes relatively great revenue growth. quantile 0.50 is some kind of midline. market valuation for AMD is either based on something that isn't discounted future earnings, or market's forecast for how future earnings will evolve is very different to my crude backward-looking model.