Ibrahim Siraj @IbrahimMSiraj

Associate Professor of Financial Accounting; Seeking intelligible truth while being comfortable with unintelligible chaos. Joined August 2012

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635

Shane Parrish @ShaneAParrish

4 weeks ago

10 Lessons from my conversation with @typesfast 1. Play Against Weak Competition: Charlie Munger told Ryan his freight business was brilliant because of “dumb competition.” Competing in AI means battling Stanford PhDs. In logistics, coding makes you revolutionary. Find markets…

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PyQuant News 🐍 @pyquantnews

3 months ago

OpenBB dropped the $0 cost financial AI agent workstation. You can get 405 free data sources (and an AI Copilot). Here's what you missed (in 90-seconds):

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PyQuant News 🐍 @pyquantnews

4 months ago

Numerical models for quant finance and trading. An entire master's degree in 21 Jupyter Notebooks. Save 2 years of your life and $90,000 of your money. It's all here free:

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Patrick Collison @patrickc

a year ago

@Scholars_Stage I think it's stratified by generation, but here's an attempt. (This isn't the list of books that I think one ought to read -- it's just the list that I think roughly covers the major ideas that are influential here.) The Tinkerings of Robert Noyce Seeing Like a State The Dream…

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PyQuant News 🐍 @pyquantnews

a year ago

The *harsh* truth (written by Karpathy):

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Nathan Baugh @nathanbaugh27

a year ago

I love this letter CS Lewis sent to a young writer, especially this line: "Much of my best work, or what I think is my best, is the rewriting of things begun years earlier."

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Reads with Ravi @readswithravi

2 years ago

3) Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville “Very clear exposition, does the math without getting lost in the details. Although many of the concepts of the introductory first 100 pages can be found elsewhere, they are presented with remarkable…

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Compounding Quality @QCompounding

2 years ago

15 timeless investing principles, visualized: 1. Not investing is risky:

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Giuseppe Cavaliere @CavaliereGiu

2 years ago

Hi #EconTwitter! 📈 Keen on the latest in causal inference with panel data models? Check out 👇 this new #econometrics survey by @ArkhangelskyD (@CEMFInews) and @guido_imbens (@Stanford)! It covers everything from difference-in-differences and two-way-fixed-effect…

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NBER @nberpubs

2 years ago

Newly uncovered threats to causal interpretation of Difference in Difference estimates, and the solutions the literature has proposed with step-by-step examples, code, and a checklist, from Freedman, @ajhollingsworth, @KosaliSimon, @coady_wing, and Yozwiak nber.org/papers/w31842

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Roxana Gutiérrez-Romero @Roxanagutz

2 years ago

Superb, an updated version of the diff-in-diff estimator for cases where the treatment is continuously distributed at every period. Consider instrumental variable cases as well. Stata and R packages computing the estimators will be available soon, some can already be computed by…

Clément de Chaisemartin @CdeChaisemartin

2 years ago

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Ethan Mollick @emollick

2 years ago

It is startling to see how much of the world's R&D spending comes from (mostly American) tech giants. The R&D spending of Amazon is greater than the R&D spending of all companies and government in France. Alphabet beats Italy. Pepsi's R&D spending beats all sources in Nigeria.

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Santiago @svpino

2 years ago

To start with machine learning from the very beginning: • Ultimate Beginner's Python Course (freeCodeCamp) • Machine Learning Crash Course (Google) • Intro and Intermediate Machine Learning (Kaggle) Take them in that order. They are all free. These courses don't get too…

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PyQuant News 🐍 @pyquantnews

2 years ago

Financial machine learning: Code, papers, and resources for using Machine Learning to make $ in the market. All free:

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Paul Graham @paulg

2 years ago

One of the most consistent mistakes I made when I was younger was to spend my time learning about things that other people thought were interesting — things that were cool or prestigious — instead of trusting my own curiosity.

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Michael Mauboussin @mjmauboussin

2 years ago

We have a new report out today, "Birth, Death, and Wealth Creation," on the the demographics of public companies and their patterns of wealth creation. morganstanley.com/im/publication…

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Paul Graham @paulg

2 years ago

There's an interesting form of luck that affects startups. You start a successful startup, then a few years into it some new innovation appears and you're uniquely poised to benefit from it. Smartphones did this for Facebook. AI seems to be doing it for Replit.

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Crémieux @cremieuxrecueil

2 years ago

probably be helped to some degree. Sources: nber.org/papers/w30622 fivethirtyeight.com/features/suici… papers.ssrn.com/sol3/papers.cf… ajph.aphapublications.org/doi/full/10.21… More: bfi.uchicago.edu/insight/resear…

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Santiago @svpino

2 years ago

This is a great time to break into AI and become the builders of the next decade. Language models are one of the most exciting places to start. You need 4 things: • Python • What the heck are embeddings? • A model to use • A sample to build Here is one idea:

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Santiago @svpino

2 years ago

10 topics I would focus on if I were to start my Machine Learning career again: 1. Python 2. Data Structures & Algorithms 3. Probabilities & Statistics 4. Learning Algebra 5. Calculus 6. ML algorithms 7. SQL 8. Testing 9. Version control 10. LLM / Langchain Here is a roadmap: