I am still in a quant-mood at the moment, so today I will go through some work I’ve done on portfolio optimization with US large cap equity sectors. I am doing this to augment my current MinVar framwork, which I use for my own investments. A quick re-cap on the basics of portfolio optimization, with advance apologies to PMs reading this and lamenting that I’ve missed something. Finance has two workhorse models; the tangent portfolio, which places the investor on the efficient frontier, where risk-adjusted return—or the Sharpe ratio—is maximised. Or the minimum variance portfolio, which offers exposure to the combination of assets with the lowest variance, or standard deviation, regardless of return. These portfolios often are estimated given a set of constraints, as I explain below. Assuming most portfolio allocation decisions start with one of these ideal models in mind—you either want to achieve the best risk adjusted return or the lowest volatility—the difference between the textbook models and real-time allocations is governed by the following layers of complexity.
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