The edge behind Market Sentiment – Part 1

This will be the first part in a series of posts dedicated to exploring and explaining the edge behind the market sentiment tool.  The market sentiment tool is designed to help traders understand and predict market trends and future moves (and volatility).

Market Sentiment
Market Sentiment

We created the widget from a combination of quantitative systems that show edge when trading in the market. We use systems that capture different mechanics in the market so as to avoid Multicollinearity .

The first model is The Relative Strength model: 

Relative strength, like the name suggests, measures the return of one asset against another asset. This way we can “eliminate noise” and try to isolate the variable we are looking for. In his book – Technical Analysis: Power Tools for Active Investors – Gerald Appel suggest to look at small stocks (riskier stocks) performance v. larger cap stocks (less risky). We do it in order to gauge the market “appetite” to risk. If riskier stocks outperform less-risky stocks – the market is predicted to move higher as there is an appetite for risk, and vice versa – It is a bearish signal if less risky stocks are out-performing. Notice that comparing 2 assets allowss us to “cancel” the trend in order to “focus” on risk appetite.

In this model we use SPX (S&P 500) to measure the “safe” stocks and the Nasdaq (IXIC) to measure the riskier stocks. We calculate a ratio by dividing the Nasdaq / SP500 and create a 10-week average. If the current ratio is above the average – it’s a bullish sign, and under is a bearish sign.

The following chart describes the p/l of the Risk model v. Buy&Hold, from Jan 1994 till Dec 2014:

Risk Model VS. B&H - Option Samura
Risk Model VS. B&H

We can see over the last 21 years the model out-performed B&H with less time in the market + less volatility and draw down. However we can see a really long period (up to 2000) where the model under performed.

Risk model - Results table - Option Samurai
Risk model – Results table

In conclusion: This model can help us better assess the market sentiment and help us gain an edge trading in market. In the next parts, I will describe more quantitative logics that compile the market sentiment widget.

 

 

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