A Beginner’s Guide to Data Science in the Portfolio Management Process
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Portfolio optimization made simple with a deeper understanding of the statistical computations at work.
Nov 6 ·6min read
A prospective client calls with $10,000,000 to invest. How does a portfolio manager/advisor go about determining the appropriate mix of stocks, bonds, cash, gold, bitcoin, or whatever? How does this manger ensure that what she is doing outperforms or — more importantly — is a more appropriate fit for her client than the results achieved by a monkey throwing darts at a dartboard?
These are questions that Data Science can help answer!
At the onset of the Portfolio Management Process, portfolio managers need to understand their clients’ objectives and constraints in order to construct an appropriate asset allocation. Reviewing a client’s objectives and constraints includes understanding her risk/return appetite, liquidity needs, investment time horizon, tax profile (asset location can be a powerful tool!), unique circumstances, and any current legal issues. With a solid understanding of these objectives and constraints, portfolio construction can begin.
An analysis of how categorical inputs like liquidity needs (none, current, future, etc.), time horizon (short-term, long-term, etc.), or taxes (pays taxes or does not pay taxes) blend together in the decision making process is beyond the scope of this blog. Rather, my goal in writing this blog is to explore the critical statical computations, such as mean, covariance, correlation, and standard deviation, that go hand-in-hand with proper portfolio construction/optimization.
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