The final day of the Risk Management conference began with Gerry Fowler who is Global Head of Equity & Derivative Strategy for BNP Paribas delivering a presentation titled Love Panic. You know you are in for a treat when the title of a presentation sounds like a good name for a band.
Love panic is actually a sentiment indicator created by BNP. He notes that many entities have approached creating a sentiment indicator and that these usually focus on the equity market. These indicators are nice, but usually do not tell an investor what to do. The BNP Love-Panic indicator uses twelve factors including the small cap outperformance of large cap stocks, the CBOE put/call ratio, fund flow in to US equities, and State Street Investor Confidence. The result is a reading that depicts if the equity market is in a phase that is either love, neutral, and panic. This is a contrarian indicator so a high reading would be considered too much love which can signal a market high. The Love-Panic indicator has an inverse correlation with six month forward equity returns. Updated data is available each Monday on Bloomberg terminals. There are actually three versions – US markets (ILUVUS), European market (ILUVEUR) and Emerging Markets (ILUVEM). The chart below is of ILUVUS from Bloomberg –
After introducing Love-Panic he demonstrated combining a signal with option hedging strategies. The Love-Panic indicator was combined with either buying 6 month 95% put or putting on a 6 month 95%/105% collar. When the Love-Panic indicator moves above 25 this would trigger a hedge trade held for 6 months. Using a 95% 6 month put was a fairly expensive method of hedging a portfolio. At times combining this hedge based on the Love-Panic did help relative performance, but was also a drag on performance. A collar buying 6 month 95% puts and selling 6 month 105% calls when then indicator breaks above 25 was a much better method of combining the indicator with this hedging strategy.
Fowler then showed how to apply a Phase Attribution Model (PAM) to sector selection. He noted there are 10 sectors in the US and the model would rank them by expected performance. For application of the model he said telecom stocks were excluded due to low option market liquidity for the sector ETF. This could be a prohibitive factor for institutions that would take large positions. I found the method of applying the model very interesting. On a monthly basis the model would dictate buying the three sectors expected to perform well and taking a short position in the three sectors expected to underperform. With respect to the three ‘middle’ sectors he would sell straddles on those sectors. This straddle selling helps the performance in times of low volatility, but also results in a portfolio being short if the market moves up too quickly and long if the market drops quickly.