Sources: TowardsDataScience ;
These are some essential reads for financial engineers. Some of these texts will commonly be found in Financial Engineering (FE) courses. As a self-taught learner I studied what was taught in various university courses for FE and followed their curriculums. Most FE programs feature the following texts during the first or second semester. This section has the most theory. The other sections are far more relevant to applications of quant finance.
This one’s a recommendation from a reader. A refresher on various math concepts necessary for the following readings, ‘A Primer for the Mathematics of Financial Engineering’ mixes math and finance to prepare the student for their journey through Financial Engineering. This seems to be a great first read for the uninitiated!
A first textbook for many financial engineering students. This text lays the foundation for Financial Engineering. When more efficient methods for options pricing were discovered, quants flocked to the fold and some of the earliest FEs like Edward Thorp built their funds capitalizing on inefficiencies in derivative markets.
With no mathematical backrgound I am not sure where to start, it depends on if you want to learn about the profession in a conceptual manner or if you actually want to become one. “Options, futures and other derivatives” by John C. Hull is usually recommended as a suitable first read, that does however require you to be familiar with math. I would say that undergraduate courses in at least calculus (single- and multivariable), statistics and PDEs is required in order to be able to consume the book somewhat successfully. Another vital aspect of the quant profession is programming, do you want to code all day? Learn how to and create some Monte Carlo simulations and some PDE-solvers (finite difference for example). With that you should have a basic foundation from which you can continue learning, actually getting a job will most likely require a significantly higher effort though.
‘Advances in Financial Machine Learning’ (De Prado)
This text has already made waves in the FE world and will continue to do so for some time. This is the de facto text for financial ML at the moment. It covers a decent bit of theory and provides great explanations for applications of machine learning in markets. This book is incredible value and a must read for someone who knows their way around ML but doesn’t know where to start using ML in finance.
Similar to the first text, a foundational FE book. Your best bet is probably to do some further research and pick which text fits your learning style better. I would recommend checking out both, but I understand wanting to move onto other topics.
Dense but full of great knowledge, this is similar to the previous texts but has some added applied theory. If you can make it through one of the two previous texts and this read from Joshi you’re in great shape for learning any other branch of quantitative finance. Also, this one is conveniently hosted on Dartmouth’s website. Any single selection from the previous three texts would offer the same breadth of knowledge offered for derivative pricing during most Master’s programs in Financial Engineering.
This text will read with many similarities to Baxter but with some refreshing sections on Forex, Bonds, and other asset classes.
This one is not a technical reading the like the prior. This is a refreshingly fun read that will be a nice break from combing through pages and pages of math and statistics. Written by Nassim Taleb, the ‘Incerto’ series is an all around great read by one of FE’s greatest operators and thinkers. Taleb is widely regarded, and I highly recommend checking out this incredible series. I own the collection and have recommended it to many friends, none (but 1 stubborn fellow) of whom have been disappointed.