Some contrarian ideas

Charts below show volatility-normalized (monthly window) pairs. In my opinion, they present interesting contrarian ideas.


Stocks vs. VIX products:

Long-term Treasury vs Stocks:

Stocks vs. REIT:

Volatility Premium Paradox

Most of the market volatility exist because the market crowd is being biased and irrational and consistently fails to estimate the future.

At the other hand, the same market crowd wants a premium for being exposed to market volatility.

It sounds a paradox: the market crowd wants to be paid for consistently failing to do its job of correctly pricing the assets. They want to be paid - and, actually, are paid - for the failure.

Stocks and Volatility: Possible Regime Change

One of the indicators I pay particular attention to is an indicator of a relationship between stock ETF and VIX ETFs. In the long run, VIX ETFs are just proxies to leveraged SPY short position, but their relationship is remarkably cyclical. Last time I noticed an extreme position in August 2015 and it worked perfectly.

The indicator is a basket of 3 ETFs: SPY for stocks and VXX, VXZ for volatility. Each part is normalized on realized 21-day volatility.

It seems like the indicator is going down in coming months. This may be when the market stays in regimes like:
- volatility is down, VIX futures are in a steep contango, market upside as usual or narrow range like in first half of 2015 (but I must say that was quite unusual).
- stocks are falling steadily, but no significant surges of volatility, VIX futures curve is flat.

In these market regimes volatility selling with stock index hedging could be a good idea.

Simplest Way to Improve Your Return to Risk Ratio

There is an industry standard to understand price volatility as a measure of risk. Most traders are too serious about it, they think it is a must to adopt the same understanding for their own. Actually, a risk is a pretty vague a notion, you can define it various ways. This is an advantage for a private investor, you can define what a risk exactly means for you.

You can understand a risk as a probability to underperform some risk-free benchmark on some time horizon. And now we have a parameter here - the time horizon.

If you hold a stock portfolio, the more the time horizon, the less the chances to underperform a risk-free benchmark because most factors creating local volatility have cyclical, mean-reverting nature. The longer you wait, the less are the losses they inflict to your portfolio. They may create a local volatility, but the overall action along the time horizon may be insignificant.

For example, you may observe periodical risk-off/risk-on trends, created only by massive emotional contagions. They create volatility spikes, extreme price actions, ruining your Sharp ratio. But they are cyclical and mean-reverting on scales rarely beyond a year.

So, the simplest way to improve your return to risk ratio is to redefine your risk to exclude local volatility as a measure, and to extend your time horizon so you could ignore short-time cyclical price actions.

Illusion of Control

It is fascinating how easily Wall Street can trick highly educated people into playing games little different from a primitive slot machine.

Philip Fisher about Economical Forecasts

"The amount of mental effort the financial community puts into this constant attempt to guess the economic future from a random and probably incomplete series of facts makes one wonder what might have been accomplished if only a fraction of such mental effort had been applied to something with a better chance of proving useful". Philip A. Fisher, Common Stocks and Uncommon Profits.

Price Dataset Illusion

What we have at hand is the market historical price dataset. We draw conclusions out of it. We develop strategies out of it.

But is this a real dataset? Does it have consistent inherent properties we can rely on?

We can consider a price movement as any of two: 1) risk premium realization or 2) risk realization. But the truth is, in every moment of history, we have a different combination of risks available.

Most risks we have now probably were never realized in history. Whatever was realized in history, may have little or no relation to what we have now.

That makes the whole historical dataset more or less useless. That makes those conclusions and strategies more or less dangerous.