Most investment managers try to use survey data or conventional oscillators to measure sentiment. The former can be distorted by the positions or biases of the respondents. The latter suffer from a varying cycle length for market moves and they must either use fixed lengths which are too inflexible or adaptive ones that presume that recent experience is most relevant (for which there is little evidence).
A simple solution is to use the market’s pricing of put options since they are used as protection against a decline in the S&P 500. We can replace that series with a function and constrain it to reasonable boundaries so that it is usable across market conditions where volatility varies considerably.
It is important to recognize that extremes in fear or optimism have a lingering impact on market returns since participants who have shed exposure take time to rebuild positions (and the converse). This is not a business of only buying when fear is high and exiting shortly thereafter.
Why Focus on Fear?
Every manager takes risk and rarely are those positions not correlated with the S&P 500. They may trade corporate bonds or emerging markets. They may “hedge” their duration or equity exposure but beta shift is common. A manager may think that the market they are long will outperform over time and they try to ignore S&P short term volatility. Such nonchalance can be very expensive.
Trend followers can certainly succeed over time but in the case of equity exposure, sentiment matters more than (say) with commodities since there is no such thing as equity overproduction or stock shortages in the conventional sense. Mean reversion is thus more common and sentiment is the key driver. Adding a strategy such as this, dovetails well with algorithmic trend following and with long term strategic positioning. The fear function can also be calculated for any contract or spread so we can see how much gas is in the tank for a specific position that is being held for other fundamental reasons.
This chart shows you the Vix index (plot 1) and my synthetic version (plot 2). You can see how my version is bounded to some extent at the low end so we can use the same values across different market conditions to find low risk buy points. A certain amount of lingering in the buy zone is good. Too much of it is a sign of a bear market.
This chart shows you how a volatility adjusted “SynVix” gives you many elevated readings over a 1 year period where normal Vix would have rarely done so.
Finally we need to adjust for where we are in the Trauma Window. You can see how my measurement of fear stay elevated for a while after a bout of trauma whereas Vix quickly reverts to much lower levels.
I am not just eyeballing these relationships and going with them because they make sense. The signals are evaluated in an algorithm so we can see when they succeed and when they fail. We must know how they worked in the bear markets of 2008 and 2012. I also want to have sell signals that succeed because I cannot be sure that the upward bias of the S&P over the last 10 years will continue.