You may be doing it wrong: A tale of two diversification strategies

The single greatest misperception we encounter with clients, and many advisors as well, is the idea that material diversification can be achieved with a large number of individual stocks or stock mutual funds. Not only is this untrue, but it’s less true now than ever because of high average stock correlations within and across markets.

Valuation Based Equity Market Forecasts: Q2 2014

Any analysis that relies on the past to offer guidance about the future makes the strong assumption that the future will in fact resemble the past. We have no guarantee that this will be the case. Many optimistic analysts assert that the invention of central banking, global communications and trade, robotics, 3D printing, Paul Krugman, or any number of ‘game changers’ that have evolved over the past few decades renders comparisons with our past misguided.

Track Records are Rubbish (or Why Managers are Factors in Drag)

As usual, you are the butt of the joke.

Everywhere you turn, you are bombarded with 1, 3, and 5 year track records for investment products. The investment management industry knows that you are influenced by percent symbols preceded by large numbers, so they market products with the best 1, 3 and 5 year track records, prominently featuring them in newspaper and TV advertisements, knowing that you will be unable to resist the urge to chase into those funds to avoid missing another year of riches.

Cluster shrinkage and the curse of dimensionality

At GestaltU we see ourselves as incrementalists. We aren’t so much prone to true quantum leaps in thinking, but we excel at finding novel ways to apply others’ brilliant concepts. In other words, we appreciate the fact that, for the most part, we ‘stand on the shoulders of giants’.

The Black Box: Eyewitness Testimony and Investment Models

We’ve been spending a lot of time recently discussing the quality of investment modeling, and the reliability of back-tests.  Specifically, we covered multiple discovery and degrees of freedom as two compelling reasons for out-of-sample performance decay.  Both of these sources of decay relate to the model itself.


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