Best way for fundamental analyst to improve quant skills?
I am a fundamental credit analyst that is becoming more involved in the strategy side so would like to employ some more quantitative techniques. Examples
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fair value spread for credit sub asset class based on macro variables
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fair value spread (on systematic basis) for individual issuers based on macro and credit specific variables
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maybe some kind of liquidity/risk on vs off indicator
Already have the job I want so purely about learning the skills. In terms of background I have the math/stats/econometrics one would get from an MA Econ (though rusty) and some basic r programming skills from DataCamp/fpp3 etc.
What would you recommend as the most efficient path to getting to where I want? Unfeasible? MS data science? Boot camp? Self study as I have done
thank you!
Ah, a fundamental analyst looking to swing through the quant jungle, I see! Here's how you can sharpen those quant skills:
Brush Up on Math and Stats: Since you've got an MA in Econ under your belt, you're already familiar with the basics. It's time to dust off those textbooks and refresh your knowledge. Focus on probability, statistics, and econometrics – these are your bread and butter in the quant world.
Advance Your Programming Skills: You've dabbled in R, which is great! Now, take it up a notch. Python is widely used in quantitative finance for its powerful libraries like pandas, NumPy, and scikit-learn. There are plenty of online courses on platforms like Coursera or edX that can help you level up.
Learn Financial Modeling: Understanding how to build financial models is crucial. You can find resources on WSO or take a course on financial modeling to get a grip on how to construct and use these models for credit analysis.
Dive into Machine Learning: Machine learning can be particularly useful for predicting fair value spreads and risk indicators. Start with the basics and gradually move to more complex algorithms. There are many free resources online, including Google's machine learning crash course.
Practical Application: Apply your skills to real-world datasets. Kaggle is a great place to find financial datasets and challenges that can help you practice your skills.
Networking and Mentorship: Connect with quants in your network or through platforms like WSO. A mentor can provide guidance tailored to your career path and interests.
Consider Further Education: While self-study is a strong start, a specialized boot camp or even an MS in Data Science could systematically structure your learning and fill in any gaps.
Remember, the most efficient path is the one that aligns with your learning style, available time, and career goals. It's not unfeasible at all – just requires dedication and a bit of that analytical mindset you surely possess. Swing confidently, and you'll get there!
Sources: Good time for a new analyst to join L/S hedge fund?, Quant Researcher in Quant Funds: Dead-End Career Path?, Q&A: Former Long/Short Research Analyst at Top HF -> VP of Growth Equities at BB, Compensation Structure At Quant VS Fundamental Funds, Leaving a single manager to start a book at a platform
Bump- anyone?
I think that would be an overkill. Not saying you shouldn't learn if you are interested, but your alpha typically does not come from heavy data crunching and it could potentially be harmful.
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