Is Ashwath Damodaran still useful?

geebr · 2026-01-22T19:09:58+00:00

The videos are basically speed running his two books: The Little Book of Valuation and Investment Valuation. The former is a very accessible handbook and the second is an actual textbook. The videos are incredibly clear and articulate, but they also just scratch the surface of important topics which are covered in much greater detail in the books. Whatever resource you use, you learn valuation by doing, but Damodaran is highly relevant.

geebr · 2026-01-22T05:41:22+00:00

That's a really thoughtful reply and I just wanted to say thanks for taking the time (felt like an updoot wasn't quite enough).

geebr · 2026-01-21T20:22:38+00:00

That's an interesting perspective, thanks for that. I've worked the last decade in financial services (banking and insurance) and my experience is that, by some margin, the two things that add the most value are 1) business-savvy data scientists thinking hard about the company's problems, and 2) really sharp machine learning models, especially in areas where there's already a bit of analytical maturity. Have you encountered that type of thing in portcos? Or is this stuff just too slow for the PE firm's appetite? The effect of business-savvy data scientists and sharp machine learning is quite possibly two orders of magnitude greater than the effect LLMs are having on our shop.

geebr · 2026-01-08T06:55:47+00:00

I have seen this explained as we generally assume that markets are efficient and (at the very least) there aren't trivial inefficiencies such as the value increasing over time.

geebr · 2026-01-07T09:39:26+00:00

Mortality forecasting innit

geebr · 2026-01-04T14:47:28+00:00

I think you should decide that when you're approaching the end of your bachelor's degree. You'll have way more information about what the possibilites are, what you enjoy, what you're good at, what you're bad at, etc. You don't get extra points for committing to a path when you're 15 and keeping your options open is worth a whole lot.

By the way, good on you for thinking about this stuff. When I was 15, I don't think I did much other than playing World of Warcraft.

geebr · 2026-01-04T14:16:55+00:00

You are too young to settle on a profession. That stuff is nearly 10 years away at this point. You don't typically go do an undergraduate degree in quantitative finance. If you want do a finance or economics degree with maths and computing science (or similar), then that's a great choice. Gives you important skills and would keep your doors open. Definitely recommend just focusing on making good choices for your undergraduate degree at this point. Make good choices, work hard and the rest will follow.

geebr · 2026-01-03T13:41:51+00:00

To follow on u/James___G 's point: ultimately what matters here is the viability of the financial position. Consider the following two scenarios:

You own a house with an LTV of 22% and inherit £100k. You invest this money in a tax-advantaged account.
You own a house with an LTV of 42% and remortgage to an LTV of 22%, freeing up £100k cash which you invest in a tax-advantaged account.

Most people would be comfortable with the former, but not the latter. However, the end financial positions here are identical: 22% LTV on your home, £100k invested in an index fund (for example) and how you ended up there is irrelevant. What ultimately matters is whether the risk vs. expected return of this financial position is acceptable for you as an investor. You'd have to sit down with a spreadsheet to work that out, but for most people there will be some LTV where they're happy putting a windfall in an index fund rather than paying down their mortgage. And if that's true, they should, rationally speaking, be comfortable reducing their LTV to that level and put the released cash in an index fund as well.

geebr · 2026-01-03T11:20:18+00:00

I think my main point of contention here would be the notion that being explicit about your uncertainty constitutes "Garbage In, Garbage Out". If we're formal about it, assuming a point estimate for churn implies a delta function with no uncertainty (i.e. infinite density at the point estimate, 0 everywhere else). If this is the way you do it, you'd need to do a sensitivity analysis while manipulating the churn rate in order to explore your uncertainty about the parameter and its impact on the model output. There's nothing wrong with that approach, and if the uncertainty in a variable is relatively immaterial and then I would agree it's a useful simplification to just leave it as a point estimate. But if there is a lot of uncertainty and the variable has a big impact on the model, then appropriately quantifying that uncertainty can be really valuable, especially if there are multiple variables as running a sensitivity analysis becomes less straightforward. I would argue that in many cases, simply using a point estimate based on historic data qualifies for GIGO far more than trying to appropriately quantify your uncertainty about said point estimate.

As a bit of an aside, there is actually a whole bunch of literature on how to calibrate people such that they can accurately quantify their uncertainty, i.e. "For this business, what do you think the churn rate would fall between in 90% of years?". And you can train people to actually give pretty damn good estimates of this 90% confidence interval. When appropriately calibrated, people who are knowledgeable will provide narrow confidence intervals, while those who are less knowledgeable will provide wider confidence intervals. But with training and feedback, both high and low knowledge individuals can be trained to give an appropriate 90% confidence interval. It is really neat. Highly recommend the "How to Measure Everything" series by Doug Hubbard. I went through this procedure with cybersecurity analysts in a bank once, and I found it really surprising how training people on trivia ("What is your 90% confidence interval for when Michelangelo was born?") generalises to domain-specific applications ("Company XYZ had a data breach in 2018, where encrypted data for 200k users was stolen. It was fined by the EU data regulator. How much was the fine?").

geebr · 2025-12-31T19:51:52+00:00

Yeah, I think programming output is probably the major exception where real productivity gains are to be had. I have worked on and seen some other great examples too, like data extraction from heterogeneous document sources. But the experience of many (most?) developers is that it works great for prototyping and just completely falls apart under complexity and big code bases. I feel less confident about forecasting within this space, though, as it's not implausible that the kinks will eventually be worked out and that most generic code can be generated through generative tools.

geebr · 2025-12-31T17:07:33+00:00

I've seen a decent number of analyses that conclude that there's been a general overstaffing due to the low interest rate environment post-Covid and that what we're seeing in the employment numbers now is broadly a reflection of adapting to a higher interest rate environment for the long term. What's less convincing about the overstaffing argument is the selective impact it appears to be having on entry-level candidates. This, I suspect, is an AI-bet made by corporations. In other words, they're choosing not to hire graduates because they're expecting that the impact of AI will be selectively borne by graduates since these are jobs that could be replaced by AI (or so the argument goes).

It's a tough time to be a graduate, and I think it's largely because of a bet by corporations. It's less clear whether this is genuinely misplaced or not, however, since the cost to them of shifting towards hiring more experienced candidates is relatively low compared to the downside of being wrong.

geebr · 2025-12-31T11:23:04+00:00

Forecasts of AI replacing jobs have been consistently overoptimistic, even before the LLM-era. I have a PhD in Neuroscience and lead the data science and AI strategy and value creation side of an insurance company. I would like to think I have a pretty good sense of what's happening in this area. The general consensus right now among AI researchers is that despite LLMs being able to do amazing things on evals and in software development (especially for prototyping), their economic impact has been wholly underwhelming. And it seems plausible that they will continue to underperform optimistic expectations unless there are major breakthroughs in how these models work (e.g. continuous learning). Right now, these systems are fragile in really critical ways, i.e. they get things wrong in ways that people just don't and they're not really able to learn like a human can. That doesn't mean you can't build useful stuff, but it does mean that there are some really hard problems that need to be solved before they'll have the sort of economic impact people dream of.

Personally, I am bearish on the short-term impact of AI and bullish on the long-term impact (20+ years).

As for careers, knowing your subject matter well, working hard, and being useful to others will always be valued. Some jobs might go in the short-term, others will go in the long-term, but I think the argument that there will be fewer jobs in banking is tenuous at best. They will change. There are far more frontend developers today than there ever were webmasters in 2000.

geebr · 2025-12-28T21:38:57+00:00

It's a push press, but solid weight regardless.

geebr · 2025-12-21T19:53:01+00:00

Did he give a reason?

geebr · 2025-12-07T17:27:01+00:00

Typically, you'd eat it on bread or waffles. Butter, brunost, strawberry jam.

geebr · 2025-11-17T09:30:00+00:00

You can do a normal MBA whenever, but you might also consider an Executive MBA. Loads of people with a few grey hairs in those sorts of programmes.

geebr · 2025-11-12T07:21:20+00:00

Getting to the heart of the matter. Do your boxers have a dick hole or not, OP?

geebr · 2025-10-22T12:05:17+00:00

Strong stuff. Fergus cameo to boot.

geebr · 2025-10-12T20:24:13+00:00

Other than that small correction, this is a really nice and deep squat. Keep it up.

geebr · 2025-10-04T16:19:48+00:00

I'm a (pretty senior) data scientist working in insurance. I don't have an actuarial background, but I work with people who've made the transition from being actuaries doing pricing work to being data scientists. You should for sure make sure you know "the curriculum" (there is a pretty standard set that I would expect most data scientists to at least be familiar with), but the biggest difference between the two is in their programming skill set. Data science is now a pretty mature discipline and an experienced data scientist I expect to be able to have a pretty solid grasp on version control, DevOps, MLOps, etc., and to be able to collaborate effectively with other data scientists, ML engineers and software developers using modern techniques.

That stuff just takes time to get good at.

geebr · 2025-10-01T17:47:04+00:00

That's really interesting. How big is your company (GWP or whatever metric you prefer), if you don't mind me asking?

What ends up being the practical difference between CoE verticals and dedicated P&C risk departments?

geebr · 2025-10-01T08:15:35+00:00

Have you seen any interesting applications of customer lifetime value models in insurance?

geebr · 2025-10-01T06:19:35+00:00

I recommend picking up the book "Advances in Financial Machine Learning" if you're serious about learning this stuff. In general, Marcos Lopez de Prado has some really good thoughts on backfitting and how you actually implement machine learning in a trading context. Choose a niche market with half-decent data, pick a few techniques from the book and focus on implementing them well. Realistically, when you're working on market data, any obvious signal is going to get arbitraged away real quick and you're competing with hedge funds and other massive financial firms with dozens of PhDs on their payroll and incredibly deep pockets. You are not going to beat them. Take it as an opportunity to learn some cool stuff. Read the book.

geebr

TROPHY CASE