Quant trader here... I'm a big seller of this list. Making money tends to be a relatively empirical endeavor. It's all about having information about the future and using that in judiciously way and less so about any particular theory or model. I see someone else mentioning Grinold and Khan "Active Portfolio Management", I can't recommend it enough, it's basically a how to for making money quantitatively in a principled way, there are lots of "tips and tricks" that go on top of this and it really helps to have some good intuition for the space you are trying to operate in (by that I mean understanding the eigenvalues and eigenvectors of your risk matrix). T-costs are also extremely important and the main "enemy" it's trivial to make money if you don't have to pay to trade.

Steven Boyd at Stanford and his students / colleagues are probably the richest seam of up to date portfolio optimization wisdom. If you are using python you shoult probably be using CVXPY to build your portfolio. He has lots of good papers, e.g. see [2].

Of course you also need an "edge", that information about the future, and that's the jealously guarded part...

[1] https://books.google.co.uk/books/about/Active_Portfolio_Mana...

[2] https://stanford.edu/~boyd/papers/pdf/cvx_portfolio.pdf

Hey there! I'm the author of the article. I just arrived here because I saw a crazy uptick on google analytics. I'm glad most of you liked the article.

I found other impactful and more recent papers via MirrorThink.ai that discuss various aspects of quantitative finance, trading, optimal execution, energy prices, GARCH, option valuation, portfolio selection, Kelly Criterion, Capital Asset Pricing Model, optimal trading signals, Efficient Market Hypothesis, Black-Scholes model, and market overreaction. Here are some key findings from these papers:

1. Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading (Published on 2020-01-07) - This paper discusses the use of Long-Short Term Memory (LSTM) networks in quantitative trading to minimize risk and maximize return based on historical performance. It highlights the benefits of quantitative trading, such as lower commissions, anonymity, control, discipline, transparency, access, competition, and reduced transaction costs.

2. A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance (Published on 2021-05-02) - This paper presents a Markov-switching trading algorithm that uses the VSTOXX index to enhance the performance of a EUR stock portfolio. The algorithm is based on the mean-variance portfolio selection, which aims to maximize the Sharpe ratio.

3. Price discovery in the cryptocurrency option market: A univariate GARCH approach (Published on 2020-08-31) - This paper applies two different GARCH processes to Bitcoin and CRIX, showing that the GARCH(1,1) option pricing model provides realistic price discovery within the bid-ask prices suggested by the market.

4. The Capital Asset Pricing Model (Published on 2021-09-03) - This paper discusses the evolution of the Capital Asset Pricing Model (CAPM) and its connection to behavioral accounts of evolutionary asset pricing, segmented markets, multifractality, and the fractal market hypothesis. It highlights the importance of considering heterogeneity among investors and the implications for the efficient market hypothesis.

[1] https://doi.org/10.3390/app10020437

[2] https://doi.org/10.3390/math9091030

[3] https://doi.org/10.1080/23322039.2020.1803524

[4] https://doi.org/10.3390/encyclopedia1030070

Probably better to learn the basics and get a good overview, eg by reading Grinold & Kahn instead:

https://books.google.com/books/about/Active_Portfolio_Manage...

I stopped at the table of content but the bibliography covers what any introductory finance 101 course would cover. I interpreted the title as suggesting there was a bit of novelty in there so it's a bit disappointing.

Without the above papers you cannot invest while claiming doing anything else than playing at a casino. But it's clearly not sufficient to design a profitable quantitative strategy in 2023.

Thanks for sharing! My list of 12 most useful resources for machine learning quants can be found here: http://gokhanmergen.com/quantBibliography.html

This list was compiled in 2009 before I took a full time job in an algorithmic trading company, but it's still relevant :) If anything ML is more relevant than ever in trading, except perhaps Deep Neural Nets, Transformers, Large Language Models etc are the norm today.

Kinda tangential, but hopefully someone lurking in here will bite:

With the growing popularity of passive strategy among institutional and retail investors, will EMH break down and create opportunities for active strategy again? As I understand it, active strategy is a borderline fools' errand on the timeline of ten or more years. But if everyone just buys the S&P, surely that means fewer eyeballs on price discovery and more pricing inefficiencies, no?

Table of contents? Blast from the SEO spam of the past.

Snark aside, very decent bibliography for the intended audience: independent traders who are building automated trading programs for their personal accounts.

Nice article, I'll definitely read some of the outlined books. Thanks for sharing.

My personal experience is that you don't need to fully understand the Black Scholes Pricing model in order to trade profitable options.

As an example consider the public income trades, such as NetZero, Boxcar, M3, Theta Engine. Trading those doesn't require you to understand how Implied Volatility.

One can argue, however, that selling options and hedging them isn't the 'Quant way' of profiting from options.

I am not a quant but I am working for one. Am I wrong to think that the quant analysts or "scientists" are not actually figuring out something fundamental about the market? You win by being "more complex" than your competition, which in turn makes the whole market more complex and this goes on endlessly.

I recommend anything by Marcos López de Prado:

https://quantresearch.org/

He has written a book and published several articles on financial machine learning, including what to look for,how to avoid overfitting, in detail, and done so far better than I've seen elsewhere.

Good luck!

This is what I wanted to do when I was getting my math degree! I wanted to be a quant. Things went a different direction and I'm a programmer now. Is there any hope for me? Think I could still chase it down in my spare time, or is it something I need, say, a master's degree for?

Stephen Ross (https://en.wikipedia.org/wiki/Stephen_Ross_(economist) )'s books on APT / CAPM are pretty good too imho.

I cannot open the link

Commenting to remind myself later

The author seems to imply that there's a Nobel Prize in economics. There is no such prize. There's only: Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel