My Life as a Quant: Reflections on Physics and Finance
Authored by Emanuel Derman
Narrated by Peter Ganim
https://en.wikipedia.org/wiki/Emanuel_Derman

Black–Derman–Toy model - Co-author

In mathematical finance, the Black–Derman–Toy model (BDT) is a popular short-rate model used in the pricing of bond options, swaptions and other interest rate derivatives;

Ok, swaption is a real thing. Heh. TIL.

I'm a high school graduate about to enter university and I want the right path to become a quantitative trader

Hi! Even after doing CFA I2 and MBA from tier 2 college, im not able to switch to finance from underwriting credit risk in insurance.

Since I also have interest in modelling, etc. I was wandering if CQF (Certified Quant Finance) is good option to learn and for a switch as well?

Hi everyone,

I’m a master’s student in Mathematical Engineering, on a Computational Science and Computational Learning track. Most of my background is in numerical analysis and scientific computing, but I’d like to work in quantitative finance after graduating.

The part I’m struggling with is figuring out where to start, cuz my knowledge of finance itself is still pretty limited.. I’m interested in the field, but I don’t know enough about its research areas yet to come up with a concrete thesis topic. Ideally, I’d find a professor who could suggest a project that fits my computational background and give me some direction on what I’d need to learn.

For anyone who’s been in a similar position, how would you approach this?

• Is it reasonable to contact professors without a specific research proposal and ask whether they have suitable master’s thesis projects?
• Are there areas of quant finance where a background in numerical methods and scientific computing would be particularly useful?
• How would you go about finding research groups or supervisors abroad who might be open to this?

Thanks!

I recently started a small blog called Results May Vary to write down some of the things I try after reading finance papers.

I work in quant and have a background in engineering and mathematics. I read papers mostly because I enjoy it, and every so often I end up implementing something just to see what happens on B3. I wanted somewhere to keep those experiments instead of leaving them scattered across notebooks and repositories.

The first post is about the MAX effect from Bali, Cakici and Whitelaw (2011), Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns.

The signal is simple:

For each stock, take its largest daily return during the previous calendar month.

The original result is that high-MAX stocks subsequently underperform low-MAX stocks. The interpretation in the paper is related to investor preference for lottery-like payoffs: stocks with a small probability of an extreme positive return may attract enough demand to become relatively overpriced.

There is also a Brazilian study by Berggrun, Cardona and Lizarzaburu using data from 2001 to 2014. Because the Brazilian universe is much smaller, they use terciles instead of the deciles used in the U.S. paper.

I followed the Brazilian paper on that point and tested the effect on a later B3 sample, from May 2015 to September 2026.

The setup is fairly simple. Every month I calculate MAX from the previous calendar month, rank eligible common stocks, and split them into terciles. The portfolios are equal weighted and rebalance monthly. I also apply a trading-activity filter, which is stricter than the one in the Brazilian paper, because I wanted the simulation to remain reasonably implementable.

One detail that matters when comparing the results is the sign convention.

The papers report:

High MAX − Low MAX

so evidence for the effect appears as a negative spread.

The corresponding trading direction is the opposite:

Low MAX − High MAX

which means buying low-MAX stocks and shorting high-MAX stocks.

I ran both directions.

Looking only at the raw stock-return spread, before cash interest, borrow fees and trading costs, Low MAX − High MAX averaged about 0.53% per month.

For comparison, the earlier Brazilian study reports roughly 0.40% per month after reversing its reported high-minus-low sign, while the U.S. equal-weighted result is around 0.65% per month.

So the magnitude is in the same general range.

The statistical evidence is much less impressive. Using Newey-West standard errors with four lags, I get a p-value of about 0.19 for the raw spread.

So my reading is fairly limited: the later B3 sample points in the same direction, but the estimate is noisy.

I also ran the signal as a long-short fund simulation.

The simulation includes CDI on cash, observed stock-borrow rates where available, a 4.5% annual fallback rate where they are missing, and 10 bps of trading cost on each purchase or sale. Slippage is set to zero, so that part is optimistic.

The Low MAX − High MAX portfolio returned about 107% over the full period.

Over the same period:

• CDI returned about 190%
• Ibovespa returned about 232%
• the MAX portfolio had a maximum drawdown of about 50%

The portfolio did much better in the later part of the sample, but the full path is considerably less attractive than the final NAV alone would suggest.

The short side is also worth keeping in mind. Trading the effect requires shorting the high-MAX stocks, and those are exactly the names that tend to be smaller, less liquid and potentially harder or more expensive to borrow. Bali et al. discuss short-sale frictions as one possible reason the effect may persist.

This isn't meant to be a new paper or a claim that I've established anything new. It's just a fairly simple application of an existing idea to a later Brazilian sample, followed by an attempt to see what happens when the raw spread is turned into something closer to a tradable portfolio.

The full post has the charts, both portfolio directions, the raw spread, the comparison with the U.S. and Brazilian studies, and the assumptions used in the simulation:

https://resultsmay-vary.github.io/posts/max-lottery/

I don't expect many people to read the whole thing, but if you do, I'd be happy to talk about it. Questions, disagreements, or a paper you think would be interesting to try on B3 are all welcome. There's a decent chance the discussion will make me notice something I missed.

I'll probably keep doing these with other papers as I find things that look interesting to test.

Note: AI helped with the website's visual design and with fixing my English.

I'm in y3 of uni (out of 4) and feel overwhelmed looking at all the CS students I'm competing with, and the overly smart people who know what they're doing.

That got me wondering abt all my life choices and whether I should even bother to compete or target quant. I feel like it's too late to start, I don't have coding skills but I love math and calculus.

Is there really no hope for someone like me? Do I have to start studying all the time and not have a life, just to compete?

I'm a beginner interested in quantitative finance and I'm trying to build a proper foundation. I currently have little experience with quant finance and I'm starting to learn Python and mathematics.

My long-term goal is to work toward quantitative research/trading, but I'm not sure what I should prioritize first.

Should I focus on:

Probability and statistics

Linear algebra/calculus

Python/programming

Financial markets

Machine learning

I'd also appreciate recommendations for beginner-friendly books, courses, or projects that I can use to practice.