Dark Clouds Looming?
At Cydonia Investments, we believe that the market is the best and most efficient aggregator of information.
Our flagship Systematic Financial Risk signal aggregates market based forward-looking indicators (e.g. implied volatility) to gauge sudden increases in financial instability.
When these indicators are together deteriorating in a rapid pace relative to past values, it could be a cause for concern.
Financial Risk Signal
Track financial market risk with the below tool. High risk regimes are highlighted in red.
Signal < 0
Low risk regime: better investing environment for risky assets like stocks
Signal > 0
High risk regime: worse investing environment for risky assets like stocks
Signal < 0
Low risk regime
Signal > 0
High risk regime
Historical Result
A high risk regime has in the past been associated with lower forward equity returns compared to a low risk regime. Below analysis looks at historical returns of the S&P 500 index one year after observing the risk regime. As you can see in the below chart, historically a low risk regime has been associated with higher returns for the S&P 500 index.
Annual S&P 500 returns in different risk regimes
Source: Yahoo finance and Cydonia Investments. Based on data between January 2001 and August 2025. Historical results is not an indicator of future performance.
FAQ
A robust way to detect financial instability that has stood the test of time
What data is used?
One can’t drive a car by just looking in the rear view mirror.
Instead one needs to look forward….
Macroeconomic data like employment, inflation and GDP growth are lagged and subject to future revisions. That is why we prefer market determined data because they are forward looking and more reactive to new information.
Our risk signal uses among other indicators
- Implied volatility
- Credit spreads
- Govt. bond yields
- Equity market pricing multiples
How is the signal calculated?
Cydonia Investments has developed a risk regime signal that aims to measure when indicators of financial instability suddenly moves together in a significant way.
Using machine learning, one can gauge factors that are driving the joint variations in market data indicators e.g. credit spreads and implied volatility.
When the variations can be explained using just a few factors instead of a diverse set of factors, it is a sign of financial market fragility.
Crucially, our risk signal looks at changes of these factors that best explains the variation, rather than the absolute levels, to judge when market conditions are deteriorating.
How to interpret the output?
A higher value indicates increases in financial market instability and worsening enviroment for risky assets.
A lower value means a more resilient financial market. There is a more diversified set of factors that can explain the join variance of market indicators.
Does it always work?
No!
If it did, it would be overfitted to the data and loose its usefulness.