Love Panic. Gerry Fowler, CFA Head of Equity & Derivative Strategy. October 2013

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1 Love Panic Gerry Fowler, CFA Head of Equity & Derivative Strategy October 2013

2 Equity AND Derivative Strategy (BBG: BNPD <GO>) Macroeconomic/fundamental analysis and strategy leading to actionable trade ideas Close alignment between views and implementation as the team combines equity AND derivative strategy experts #1 for option trading ideas - Greenwich Survey of European Clients 2013 Global Research House of the Year Derivatives Intelligence 2013 Have a view Economics Fundamentals Market views Know the market Market pricing analysis/awareness Suggest actionable trades Equity & Derivative implementation strategies Market & Strategy Updates Volatility & Correlation Monitors Single Stock & Directional Focus Dividend Market Updates Daily Lens Dispersion Monitor Single Stock Weekly Focus Dividend Monitor Outlook and Outlook Updates Global Index Volatility Express Sniper Dividend Espresso 2

3 Contents Love-Panic 1. The importance of sentiment 2. Measuring sentiment 3. A hedging strategy for long only managers Phase Attribution Model 4. Quantitative long/short sector investing using sentiment phasing 5. An option strategy for long/short managers 3

4 1. The importance of sentiment 4

5 Topical But none calibrated to forward equity returns 5

6 ...insert loosely related, humorous cartoon here... 6

7 Sentiment does play a major role in equity movements Global equity performance attribution heavily influenced by the VALUATION investors place on earnings 60% 50% 40% Chg in Rating Growth Yield Performance 30% 20% 10% 0% -10% -20% -30% -40%

8 2. Measuring sentiment 8

9 Enter Love-Panic Popular sentiment indicators have short-comings Not calibrated to equity market performance (CESI) Sentiment isolation is ambiguous (VIX, market volumes, advance/decline indices) Single indicators measure factors that are often too specific (NYSE margin debt, AAII bull/bear surveys, fund flows etc) Combination indices use equal/constant weightings (CNN) Love-Panic solutions Calibrated to 6-mth forward equity market returns Combines multiple sentiment factors to remove idiosyncratic non-sentiment drivers Dynamically weights components according to their historical predictive strength Love-Panic construction Identify indices (that are subjectively related to sentiment) with 3-yr z-scores negatively correlated to 6-mth forward equity returns Weight their individual predictive success across three phases (love, panic and neutral levels). Combine their historical levels at the appropriate phased weighting into the Love-Panic index. Individual factor weightings vary from 0-11% of the total index calculation. Testing period 2005 to May

10 Individual indicators hit and miss Smallcap outperformance of largecaps CBOE put/call ratio Fund flow into US equities State Street Investor Confidence +12 other indicators 10

11 Combined indicator has high/consistent negative correlation Love-Panic US Index Love-Panic level 3-yr rolling correlation to market performance Conviction best at extremes 15% 10% 5% 0% -5% Average Return by Love-Panic level -10% below to to to 0 0 to 5 5 to to to 20 above 20 11

12 Out of sample performance and regimes in sentiment value Thoughts on out of sample periods Doesn t give false signals in periods when fundamentals consistently trump sentiment ( ) Fails when bullish momentum trumps contrarians (2003, 2013). These periods are usually associated with a rapid move to bullishness caused by genuine fundamental improvement. A useful addition to risk management toolkits 12

13 Latest Love-Panic US update and inputs (BBG: ILUVUS Index) 13

14 Latest Love-Panic EU update and inputs (BBG: ILUVEUR Index) 14

15 Latest Love-Panic EM update and inputs (BBG: ILUVEM Index) 15

16 3. A hedging strategy for long only managers 16

17 Our goal Improve long-only risk management Futures or full-delta hedging are too blunt in that the cost of being wrong is high. If only partially hedging, then you re only partially hedged! Option hedging is typically an expensive strategy when implemented passively. Implied volatility and skew usually overprice risks for long periods. Common single-factor conditional hedging strategies (volatility levels) improve decision making but suffer from the same idiosyncratic problems as using those factors for market timing. Calibrate Love Panic signals with option hedging strategies Using in and out of sample data, what: triggers, option strategies, strikes and maturities would have been optimal for adding alpha in the past 17

18 Our process Testing parameters Weekly data from ( is in-sample data) Black Scholes option calculation based on constant (non-listed) maturities Transaction costs of 10bps per option Implied volatilities taken from the surface at the time (includes skew and term structure impact) Dividend yield constant at 2% Rates assumed to be the 3-mth benchmark (for all maturities tested) Only one hedge strategy held at any time (regardless of additional triggers) Enter new hedge strategy if still triggered at expiry of initial hedge. Selected parameters for following case studies Trigger: Break up through Love-Panic of 25 triggers a hedge trade Maturity: 6-mths Strategies: 95% puts and 95/105% collars 18

19 Trade on periods for 6-mth maturity strategy 100 Love Trade On SPX 6m Forward Returns (RHS) Love Panic (LHS) 60% % Love Panic US % 0% -20% SPX 6m Forward Return Panic % -60% 19

20 3. A hedging strategy for long only managers Puts 20

21 Puts: Strategy premium history 17% 15% 13% Premium for 95 put 11% 9% 7% 5% 3%

22 Puts: Strategy P/L vs SPX 140 Portfolio SPX Hedging PnL SPX and Portfolio Perf. rebased to

23 Puts: Annual impact on portfolio return and volatility 25% 20% 15% Excess Return Volatility Impact on Portfolio 10% 5% 0% -5% -10% -15% -20% -25%

24 Puts Spread: Love-Panic trigger sensitivity Love Panic Level Total P/L Avg. P/L Nb. of Trade Average Excess Ann. Ret. Average Impact on Port. Vol 15-30% -2.0% % % -1.0% % % -1.1% % % -1.1% % % -0.8% % % -0.8% % % -1.3% % % 0.2% % % 0.2% % % 0.6% % % 1.6% % % 2.6% % % 3.6% % % 4.4% % % 2.3% % % 2.3% % % 2.9% % % 3.5% % % 6.8% 9 1.7% % 7.3% 9 1.9% % 6.9% 9 1.7%

25 Puts Spread: Maturity sensitivity Total P/L Avg. P/L Nb. of Trade Average Excess Ann. Ret. Average Impact on Port. Vol Maturity 1-12% -0.8% % % -1.0% % % 1.2% % % -0.3% % % 0.8% % % 1.6% %

26 Puts Spread: Strike sensitivity Long Put Strike Short Put Strike 1.6% % 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% % 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% % 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% % 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% % 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% % 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% % 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% 1.6% % 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% % 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% % 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% % 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% % 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% % 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% % 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% 1.4% % 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% % 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% 1.3% % 1.2% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2% % 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 26

27 3. A hedging strategy for long only managers Collars 27

28 Collars: Strategy premium history 1.7% 1.6% 1.5% Premium for 105 vs. 95 Collar 1.4% 1.3% 1.2% 1.1% 1.0%

29 Collars: Strategy P/L vs SPX 180 Portfolio SPX Hedging PnL SPX and Portfolio Perf. rebased to

30 Collars: Annual impact on portfolio return and volatility 30% 25% 20% Excess Return Volatility Impact on Portfolio 15% 10% 5% 0% -5% -10% -15% -20% -25%

31 Collars: Love-Panic trigger sensitivity Love Panic Level Total P/L Avg. P/L Nb. of Trade Average Excess Ann. Ret. Average Impact on Port. Vol 15-6% -0.4% % % 0.0% % % 0.5% % % 0.5% % % 0.9% % % 0.9% % % 0.3% % % 2.8% % % 3.6% % % 4.2% % % 5.0% % % 5.7% % % 6.0% % % 7.3% % % 4.8% % % 4.8% % % 5.4% % % 6.1% % % 9.8% 9 2.6% % 10.6% 9 2.9% % 10.2% 9 2.7%

32 Collars: Maturity sensitivity Total P/L Avg. P/L Nb. of Trade Average Excess Ann. Ret. Average Impact on Port. Vol Maturity 1 13% 0.8% % % 0.3% % % 3.4% % % 2.4% % % 3.7% % % 5.0% %

33 Collar: Strike sensitivity Put Strike Call Strike 5.0% % 6.6% 4.8% 4.3% 4.0% 3.6% 3.3% 3.0% % 8.3% 6.2% 5.7% 5.2% 4.8% 4.4% 4.1% % 10.0% 7.7% 7.1% 6.5% 6.0% 5.6% 5.2% % 10.0% 7.6% 7.0% 6.4% 5.9% 5.5% 5.1% % 10.1% 7.7% 7.0% 6.5% 5.9% 5.5% 5.1% % 10.1% 7.7% 7.0% 6.4% 5.9% 5.5% 5.1% % 10.1% 7.7% 7.0% 6.4% 5.9% 5.5% 5.1% % 10.0% 7.6% 6.9% 6.3% 5.8% 5.4% 5.0% % 9.9% 7.5% 6.9% 6.3% 5.8% 5.3% 5.0% % 9.9% 7.5% 6.8% 6.2% 5.7% 5.3% 4.9% % 9.8% 7.4% 6.7% 6.2% 5.6% 5.2% 4.9% % 9.7% 7.4% 6.7% 6.1% 5.6% 5.2% 4.8% % 9.8% 7.4% 6.7% 6.2% 5.7% 5.2% 4.9% % 9.7% 7.4% 6.7% 6.1% 5.6% 5.2% 4.8% % 9.7% 7.3% 6.7% 6.1% 5.6% 5.2% 4.8% % 9.6% 7.2% 6.6% 6.0% 5.5% 5.1% 4.7% % 9.4% 7.1% 6.5% 5.9% 5.4% 5.0% 4.6% % 9.3% 7.0% 6.3% 5.8% 5.3% 4.9% 4.5% 33

34 Further thoughts Potential improvements to use of sentiment in risk management Different triggers (break-up or break-down through trigger level) Different implementation timing (delay of a day/week) Add an overlay for ADDING risk when Panic trigger breached 34

35 4. Quantitative long/short sector investing using sentiment phasing 35

36 Cycles usually stylized 36

37 Calibrating to the business cycle is popular OECD lead indicator often used for phasing In this example there are four phases of a business cycle 37

38 Calibrating to a MARKET SENTIMENT cycle with Love-Panic Love-Panic identifies three market phases 38

39 Quant long/short analysis process improved by PAM The standard quant model Take a list of factors Measure their ability to predict 1-mth returns on each country/sector Create a combined score from the most predictive factors on each Buy the top quartile and sell the bottom quartile (but what about the middle!?) Phase Attribution Model (PAM) Assess the historical predictive power of each factor only in the same phases as defined by Love-Panic Use and weight the top 8 factors for selections This will introduce more fear/greed, beta and cyclicality to country and sector selections 39

40 PAM: Parameters included Vauation Trailing Earnings Yield Trailing Dividend Yield Trailing Free Cash Flow Yield Trailing Price to Book Earnings yield as a z-score of 7 year mean Dividend yield as a z-score of 7 year mean Price to book as a z-score of 7 year mean MSCI MSCI Factset MSCI MSCI MSCI MSCI Growth 3 year EPS change Datastream 7 year EPS change Datastream 1 month change in long term average growth Datastream 3 month change in long term average growth Datastream 6 month change in long term average growth Datastream Long term average growth Datastream Long term earnings standard deviation Datastream 12m forward YOY growth Datastream Profitability Return on Equity Return on Equity relative to historic average Return on Equity relative to its last trough Return on Equity relative to its last peak Return on Equity variance Net Margin Net Margin relative to history Factset Factset Factset Factset Factset Factset Factset Price Momentum 1 Month Price Momentum MSCI 3 Month Price Momentum MSCI 6 Month Price Momentum MSCI 12 Month Price Momentum MSCI Leverage Net Debt To Equity Net Debt To Equity relative to history Factset Factset Earnings Momentum 1 month expected earnings momentum IBES 3 month expected earnings momentum IBES 6 month expected earnings momentum IBES 12 month expected earnings momentum IBES Earnings Revision Index IBES 40

41 PAM performance across strategies US Sector Alpha European Sector Alpha Country Alpha out of sample *Includes 50bps transaction costs per month (on total 200% notional) 41

42 YTD PAM selections/performance of US sector strategy Perf Jan Perf Feb Perf Mar Perf Apr Perf May Perf Jun Perf Jul Perf Aug Perf Sep US Energy 7.8 Neg 0.0 Neg 1.9 Pos -0.9 Neg 1.9 Neg -2.0 Neg 5.1 Neg -2.0 Neg 3.0 Neg US Materials 3.7 Pos -1.4 Pos 2.0 Neg 0.9 Neg 1.6 Neg 5.4 Pos -0.2 Pos 4.9 Pos US Industrials 2.2 Pos -1.0 Neg 4.6 Neg -1.4 Neg 5.7 Pos 6.4 Pos US Cons. Disc. 5.7 Pos 0.5 Pos 4.6 Neg 5.3 Pos -2.8 Pos 5.0 Pos US Cons. Stap. 5.7 Pos US Health Care 6.2 Pos 3.2 Pos 1.5 Pos US Financials -1.9 Pos -5.2 Pos US IT 2.4 Neg -3.5 Neg 4.9 Neg US Telecoms 2.1 Neg 2.4 Neg 3.2 Pos 6.0 Pos -7.0 Pos 1.9 Pos -3.7 Neg 0.3 Neg US Utilities 4.8 Neg 1.6 Neg 5.7 Pos -9.4 Pos 0.7 Pos 4.1 Neg -5.5 Neg 1.7 Neg US Sectors Perf *Does NOT include transaction costs 42

43 5. An option strategy for US long/short managers 43

44 Long/short strategies High profits when correlation is low Low profits when volatility is low Correlation and volatility are highly correlated Long/short returns should have low/negative correlation to short volatility returns 44

45 Analysis parameters Process followed in ad hoc analysis of thesis Run US sector long/short strategy on top and bottom 3 sectors from PAM Overlay a short straddle strategy on the 3 middle sectors (exlcuded telco sector) 1-mth maturity options (not calibrated to expiry) 5bps transaction cost (per option) ATM strikes Long/short uses 1x leverage ($100 long and $100 short - $33 on each sector...) Straddle notional positions are $100 also ($17 notional on each put and each call on each sector) Introduces occasional delta (up to 100%) to the market-neutral long/short strategy but in exchange for premium 45

46 Performance of strategies PAM Straddle strategy PAM + straddle strategy *Includes transaction costs based on churn rate (Churn highest when Love-Panic phase changes) 46

47 Correlation between short vol and long/short strategies 80% 60% 12m Rolling Correlation VIX Index % 50 12m correlation 20% 0% -20% VIX -40% 20-60% 10-80%

48 Performance and volatility comparison Since 2007 Volatility Annualized Return Sharpe PAM Straddle strategy PAM + Straddle Strategy % % %

49 Conclusions Can sentiment indicators inform equity risk management strategies? Yes, at both the broad market and the sector level Sentiment alone should never be a black-box market timing signal It can help with timing option hedging strategies in a way that reduces hedging costs and maximises the effectiveness of a hedging program Could also be used in reverse to time option based long exposures to markets when panic is peaking A sentiment phase overlay to quantitative US sector long/short option strategies Selling volatility produces returns negatively correlated to long/short returns Selling volatility on the middle ranked elements of a quant model helps maximise the alpha of a successful model Our Phase Attribution Model (PAM) has a great track record in US sector selection and the Sharpe ratio is enhanced further with a short straddle strategy on the middle sectors. 49

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