How Investment Firms Manage Noise in Markowitz Portfolios

# Markowitz portfolio in reality


# Markowitz portfolio in reality












I am in academia and begin to work on topics including portfolio optimization. I just read lots of paper discussing different extensions to the Markowitz approach, given different (possibly unrealistic) assumptions. Since it is an old method and I am away from the real finance organization, some facts from the real work might help our research a lot. I am just curious about:

- Is Markowitz mean-variance portfolio still widely used in companies?

- If yes to 1, it is well-known that Markowitz is prone to noise, for example, will large assets pools. Do you care about or really use those fancy method in papers?

- Many recent papers just consider the simplest mean-variance problem, i.e., without constraints or transaction costs. Is this practical? For example, in reality, do we need to first specify that, the exposure to certain a factor/sector should be among a range? Or what's the most commonly-required constraints, are they linear equality/inequality/ non-convex? Do they come from the government/investor, or simply the analyst's belief that they are helpful or easy to communicate with shareholders?

We academia researchers know some fancy tools, but I really want to stay close to the real needs. Thank you!

## Answer by demully (score 2)

https://quant.stackexchange.com/a/50735

Let’s put it this way. Classic MV is still used, but its shortcomings are universally appreciated. In its favour, the process is logical, conceptually intuitive, and non-quants easily understand it.

But the optimisation produces some very unintuitive results, no different to multicollinearity effects in regression analysis. That’s a harder one for the non-quants to grasp, especially if they have convinced themselves that the return on this is X and that is Y. If you really believe that Shell is +10% and BP is -10%, these optimal portfolio really is long/short 10x capital! The model is just a function of its assumptions, after all.

The way that most investment houses deal with this problem is akin to sampling errors in academia. They don’t wish to pick fights with their experts (who just might be right even if they are extreme!) So they oversample, requiring every forecaster to also star their assumptions on multiple related proxies.

Given these, the risk/asset-allocation group can infer outlier forecasts, and deduce more consensus forecasts for X given a multiplicity of forecasts for Y and Z. Ditto for Y and Z, of course.

They can also require their forecasters to produce a range around their forecasters (that the central portfolio team can freely adjust); that allows them to Monte Carlo the impact of different assumptions. This does not eliminate the Markowitz “noise”; but it does measure it, which informs how the central powers the choose to review and revise their assumptions to ensure more internal consistency with external intuition.

Outside of “risk parity” (in multi-asset) and “factor-based” “smart beta” (which demeans stockpicking gurus), Markowitz remains the default model. Academia tries to innovate the model. Finance tries to bootstrap its way around the problems, with workarounds within workarounds within workarounds. Most of these simply just trying to dial down the overconfidence and inconsistency in forecast that causes Markowitz and optimisation to habitually over-position portfolios.

Hope this helps, and happy to answer any follow-ups. No longer in this game professionally, after 20 years!