Quant Finance
Quantitative Finance is a field of applied mathematics that deals with mathematical modeling of financial markets. It is a highly interdisciplinary field that involves concepts from mathematics, statistics, computer science, economics, finance, etc. Quantitative Finance is a highly sought after field in the industry and is a great career option for math enthusiasts.
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Why Quantitative Finance?
Quant Finance is an exciting blend of theory and practice. People working in this field are called quants. Quants are highly paid professionals and are in high demand in the industry. If you are interested in finance and want to apply your mathematical skills to solve real world problems, then Quant Finance is a great career option for you.
Learning Roadmap
- Learn the basics of finance and economics.
- Learn the basics of probability and statistics.
- Learn about Stochastic Processes & Time Series analysis.
- Learn the basics of machine learning.
- Learn the basics of financial mathematics.
- Learn the basics of derivatives pricing.
- Learn the basics of stochastic calculus.
- Learn the basics of portfolio management & risk management.
- Learn the basics of algorithmic trading.
- Learn the basics of quantitative research.
- Learn the basics of quantitative risk management.
Learning Resources
Start only if you have interest in finance and statistics.
Finance & Economics
Start learning about finance, investment and trading. Discover it yourself without any guidance. That’s the best way to learn about latest courses and topics.
One of the best courses I completed myself if MIT 18.S096 - Topics in Mathematics with Applications in Finance. It is a great course that will introduce you to the basics of finance and economics. It will also introduce you to the basics of derivatives pricing and stochastic calculus. It will entrigue you to learn more about finance and economics. Might be a bit difficult for beginners which you may continue after taking some statistics courses.
Probability & Statistics
Build a strong foundation in probability and statistics. I recommend Harvard’s Stat 110 and Sheldon Ross’s A First Course in Probability. Also, learn about stochastic processes and time series analysis.
You can explore Probability & Statistics with R by Dr. Siva Athreya. Its intended for M.Sc. Data Science students at CMI. Don’t forget to explore Introduction to Stochastic Processes by Dr. Parthanil Roy.
Machine Learning
Learn the basics of machine learning and deep learning. I recommend Andrew Ng’s Machine Learning from DeepLearning.AI. One can take Andrew Ng’s Deeplearning Specialization if already familiar with machine learning. If you find it too big of a commitment, then you can take MIT 6.S191 Introduction to Deep Learning taught by Alexander Amini and Ava Amini. It is a great course that will introduce you to the basics of deep learning. It is a great course for beginners.
Financial Mathematics, Portfolio & Risk Management
One of the most difficult things for an undergrad to grasp because it deals with a lot of advanced mathematics at the level of graduate courses. I recommend finishing MIT 18.S096 - Topics in Mathematics with Applications in Finance at this level. It will revise essential math and introduce you to financial modeling.
Learn about derivatives pricing and stochastic calculus. Top resources at this level are:
- Paul Wilmott on Quantitative Finance
- David Luenberger’s Investment Science
- Barter Rennies’s Financial Calculus
Options, Futures & Other Derivatives
Check out Mark Meldrum’s playlist on Youtube. It is a great resource for learning derivatives. I recommend going through the book John Hull’s Options, Futures & Other Derivatives along with the course.
Learn about portfolio management and risk management. I recommend going through Caltech’s Pricing Options with Mathematical Models and Financial Engineering and Risk Management Specialization by Columbia University. Going through all the courses might be a bit difficult but definitely checkout the 4th course on Advanced Topics in Derivatives Pricing.
Quantitative Research
Having a good understanding of statistics and machine learning is essential for quantitative research. But nothing beats the experience of doing research yourself. I recommend cheking out WorldQuant’s BRAIN Research Platform. They offer weekly research paper access and a great platform to simulate your aplha ideas. The best part is the opportunity to get monetary rewards for your research and a chance to get hired by WorldQuant.