The Magic of Mean Reversion: More Than Meets The Eye

While each flip is random, the aggregate is not.

While each individual flip is random, the aggregate behavior is not.

I flipped three heads in a row on the crowded floor at a conference. No big deal. The next toss comes up…heads. Some eyebrows are raised as I prepare to flip again…heads. That’s 5 in a row. A small crowd gathers. “He’s due for a tail” someone says as my thumb flicks the coin into the air…heads. “What are the odds?!” I hear. Someone chimes in – “the odds are growing that he’ll get tails next time.” The coin flips through the air once more…

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The Pre-Mortem: sidestepping disasters before they happen.

Projects fail for many reasons, even after enormous effect and planning.

Projects fail for many reasons, even after enormous effort and planning. Pre-mortems can help avoid disaster.

Let’s suppose your team needs to decide whether to pursue a project (investment related or not), and it’s time to discuss the risks. What’s the best way to do it? Gary Klein, a research psychologist and currently Senior Scientist at MacroCognition, found that ‘prospective hindsight‘ — imagining that an event has already occurred — increased the chances of identifying the reasons for failure by 30%! In any risk management context, that’s worth learning more about.

Enter the Pre-Mortem.

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Model Risk and Financial Charlatanism

But when we modeled it, the brakes worked!

But when we modeled it, the brakes worked!

One of the hot buzzwords in financial risk management these days is “Model Risk”, as if this concept is in some way new. Unfortunately, it’s only recently – a full 6 years after the onset of the Global Financial Crisis (GFC) – that the idea of Model Risk is getting wide coverage. The concept is rather simple: in essence, it says that a model is, well, just a model. It’s not reality. But since we quants / financial engineers / “rocket scientist” types tend to put things a bit more quantitatively than that, the notion of Model Risk includes what’s called “goodness of fit” or measures that assess the appropriateness of a given model for a given situation. They help you understand when the model may need tweaking, may no longer be appropriate or if it’s predictive value may have fallen too low to use. I take pride that Investor Analytics was the very first risk management specialist firm on Wall Street to actively share the results of our Model Risk measures. It wasn’t easy: for many years before the GFC people thought we were a bit nuts to call attention to the limitations of models in our industry.

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