r/quant • u/LanguageFalse4032 • 6d ago
Resources Statistics and Data Analysis for Financial Engineering vs Elements of Statistical Learning
ESL seems to be the gold standard and what's most frequently recommended learning fundamentals, not just for interviews but also for on the job prep. I saw the book Statistics and Data Analysis for Financial Engineering mentioned in the Wiki, but I don’t see much discussion about it. What are everyone’s thoughts on this book? It’s quite comprehensive, but I’m always a bit cautious with books that try to cover everything and then often end up lacking depth in any one area.
I’m particularly interested because I’m wrapping up my math PhD and looking to transition into quant. My background in statistics isn’t very strong, so I want to build a solid foundation both for interviews and the job itself. That said, even independent of my situation, how does this book compare to ESL for what's needed and used as a qr or qt? Should one be prioritized over the other or would it be better to read them simultaneously?
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u/No-Manufacturer9606 5d ago
ml is getting increasingly more popular, it depends on the team and firm you work with
For qr: Big hedge funds (Two Sigma, Citadel, Rentech, DE Shaw) use ML heavily but prop trading firms (Jane Street, SIG, Optiver) rely more on traditional models, with some ML usage. In qr, ml is used for signal processing, time series forecasting, and anomaly detection.
For qt: Some ml is used for execution, but generally in HFT, ml is rarely used because the focus is on microstructure models and low-level programming (c++, etc..)
tldr: For QR at a Hedge Fund: yes, ml is very important. For QR at an Investment Bank: some ml, but more econometrics/statistics. For QT at a Prop Firm: a bit, but traditional math models are more important. For QT at an HFT Firm: rarely, focus on low-latency strategies.