FATALPHA - Summary

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  1. 1. Quantitative Methods + Qualitative Analysis = Superior Returns
  2. 2. Summary Philosophy Value Investing provides an investor the highest probability of success since historically it has provided the best returns. Almost any investment could be justified by an enthusiastic investor as value based on assumptions. Therefore the approach needs a method in order to focus a value investors effort. Quantitative value models achieves this. A similar approach as Asness AQR, Greenblatts Gotham Funds and Ibbotsons Zebra Capital. Black boxes are prone to failure, so fundamental (traditional bottoms-up, qualitative) analysis should be applied. This boosts returns and reduces risk. Method Proprietary quantitative models based on combining value with growth, dividends, momentum and on its own are used. 6-17 factors are used in each model. Each model and each individual factor were backtested with positive results. 65 data points are used. The models were based on combining factors that academics and other practitioners have also tested and displayed in books and papers. Fundamental analysis is done on the model output. Only the best opportunities are selected. Strategies Flagship FatAlpha Active Strategy. Gross exposure: 100%. Net exposure 90%+ during 2012-15. From 2012- 2015, the strategy returned 157% vs 60% for market. FatAlpha Market Neutral Strategy launched in September 2015 with a target gross exposure of 100% and net exposure of 0%. This strategy uses a short quantitative model designed in 2015. 2
  3. 3. Searching for the best strategy Market Facts: 1. Most Active Fund managers underperform 2. Market Gurus arent any better 3. Wall Street predictions are mostly wrong What approach is the most successful based on both theory and practice? Fact: Many successful fund managers are/were value investors: Warren Buffett (Berkshire Hathaway), Peter Lynch (Fidelity Magellan), Sir John Templeton (Franklin Templeton Investments), Joel Greenblatt (Gotham Capital), John Neff (Vanguard's Windsor Fund), Benjamin Graham, Al Frank, etc. Fact: Academic work has concluded that value investing results in significant alpha: Recent Nobel Prize Winner, Eugene Fama found that low price-to-book stocks outperfomed the market and was disappointed to see that this performance could not be explained by the Capital Asset Pricing Model (CAPM). Professors Lakonishok, Shleifer and Vishny published "Contrarian Investment, Extrapolation, and Risk" that showed that low P/E, P/Bk and P/CF stocks outperformed the high P/E, P/Bk, P/CF stocks and the market averages. Dreman and Lufkin looked at 27 years of annual data to come to the same conclusion when studying Price to Earnings, Price to Book, Price to Cash Flow and High Dividend Yielding stocks. According to Dremans work not only do low PE stocks do better than high PE stocks but this outperformance occurs also during both positive and negative earnings surprises. 3
  4. 4. How to best implement a value investment strategy? Quantitative Value Models have been backtested extensively with success and shows to have significant merit: OShaughnessy conducted an extensive in-depth study of market ratios and potential strategies in What Works on Wall Street. The conclusion was that buying stocks with low multiple or practically any combination of ratios led to market outperformance. He shows numerous multi- factors models that outperform the market. Survivorship bias and other potential statistical issues were accounted for. While at the University of Chicago, professor Piotroski wrote "Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers" In the paper Piotroski showed that by combining a 9-factor binary model to filter the low Price to Book companies, one could outperform the market. FatAlpha backtested this idea and confirmed Piotroski's conclusion to be true. S&P Analyst Richard Tortoriello wrote "Quantitative Strategies for Achieving Alpha". Tortiello tested numerous single, two-factor, and three-factor models while providing a framework and logic for creating multi-factor models. Tortoriello backtests show that valuation, more than anything, drives superior returns and is the strongest of the quantitative factors. He recommends using value factors as the base of any model. Fund manager and Columbia professor Joel Greenblatt in his NYT bestseller backtests his magic formula method over 17 years (1987-2004), and shows an average outperformance of about 10% per year vs the S&P 500. 4
  5. 5. Initial Backtests of Quantitative Methods FATALPHA conducted its own backtests of numerous value quantitative models. The models focused on company fundamentals and market ratios. The results confirmed previous studies. The backtested results of two such models are shown on the right and below. FATALPHA mainly uses such models to produce lists of potential investments. 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Return S&P 500* 20% 31% 27% 20% -10% -13% -23% 26% 9% 3% 14% 4% -38% 23% 13% 0% 5% Value Model 11% 49% 1% 7% 8% 40% -4% 55% 46% 15% 26% -21% -38% 132% 41% 5% 18% Multiple/Dividend Model 4% 33% 3% 1% 27% 21% -4% 26% 23% 14% 20% 11% -30% 52% 13% 6% 12% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% 20.0% S&P 500 Multiple/Dividend Model Value Model 5% 12% 18% 1996 2011 Annual Backtested Return * Based on Bloomberg ticker: SPX Index 5
  6. 6. Current Quantitative Models Used 1996 2013 Annual Backtested Return * Based on Bloomberg ticker: SPX Index 6% 16% 13% 16% 16% 14% 15% 17% 17% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Return Alpha Value 1Q 21% 32% -1% 11% 8% 39% -9% 58% 25% 8% 20% -11% -38% 96% 28% -4% 17% 51% 16% 10% Value 1 12% 42% -6% -2% 0% 25% -18% 48% 28% 8% 25% -9% -42% 100% 21% 3% 13% 63% 13% 7% Value 2 16% 31% 4% 14% 7% 27% -10% 39% 30% 16% 19% -10% -38% 100% 23% 5% 19% 53% 16% 10% T-Value 1 21% 27% 20% 8% 18% 15% 0% 36% 45% 18% 28% -6% -35% 47% 21% -2% 7% 49% 16% 9% T-Value 2 4% 32% 13% 7% 13% 18% -1% 34% 38% 23% 23% -11% -33% 43% 25% -2% 15% 49% 14% 8% Dividend 8% 28% 2% 11% 13% 23% -2% 44% 26% 18% 22% 6% -35% 59% 15% 5% 14% 43% 15% 8% Garp 1 26% 29% 2% 22% 13% 54% 3% 58% 21% 13% 12% 12% -37% 44% 27% -1% 13% 40% 17% 11% Garp 2 32% 31% -7% 28% 18% 47% 5% 51% 26% 13% 8% 6% -38% 63% 19% 0% 13% 44% 17% 11% S&P 500* 20% 31% 27% 20% -10% -13% -23% 26% 9% 3% 14% 4% -38% 23% 13% 0% 13% 30% 6% The backtested models have the following four themes: Value, Value with momentum, Value with growth, Value and dividends. Models contain between 6- 17 factors 6
  7. 7. Robustness of Quantitative Value Value 1Q model ranks all the stocks in the Russell 3000. To test the robustness of this model, the 3000 ranked stocks are broken into 10 groups of 300 (i.e. 1-300, 300-600, 600- 900, etc) and the returns of each have been calculated. As expected the top group outperforms, which each subsequent group registering a lower return. Decile 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Return 1 21% 32% -1% 11% 8% 39% -9% 58% 25% 8% 20% -11% -38% 96% 28% -4% 17% 51% 16.0% 2 21% 28% -2% 15% 17% 22% -11% 44% 22% 10% 15% -3% -42% 74% 28% -1% 17% 40% 13.7% 3 21% 32% 1% 4% 17% 19% -7% 45% 20% 7% 17% -6% -35% 64% 24% -2% 15% 41% 13.2% 4 18% 28% -2% 2% 11% 13% -10% 42% 19% 5% 17% -1% -38% 46% 26% -6% 19% 40% 10.8% 5 14% 26% -1% 6% 13% 6% -13% 54% 17% 4% 18% -5% -33% 50% 21% -7% 20% 38% 10.7% 6 20% 22% -3% 9% 13% 6% -20% 51% 17% 3% 17% -3% -34% 29% 20% -9% 14% 35% 8.6% 7 18% 18% 4% 5% 8% -3% -22% 57% 16% 5% 16% 3% -36% 27% 22% -6% 14% 33% 8.0% 8 15% 20% 3% 40% -8% -6% -31% 54% 13% 2% 9% 5% -38% 37% 24% -6% 18% 37% 7.9% 9 10% 11% 11% 42% -9% -17% -40% 52% 6% 4% 11% 2% -41% 26% 25% -6% 13% 43% 4.8% 10 6% -4% 15% 70% -20% -30% -45% 51% 1% -1% 5% 9% -44% 22% 28% -4% 12% 35% 1.7% 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 1 2 3 4 5 6 7 8 9 10 16.0% 13.7% 13.2% 10.8% 10.7% 8.6% 8.0% 7.9% 4.8% 1.7% 1996 2013 Annual Return 7
  8. 8. Growth of $100,000 invested in models $100,000 become $300,093 in the S&P 500 vs $1,747,632 in model. Portfolio rebalanced at year-end starting in 1995. Dividends not included. S&P 500 benchmark used is SPX Index from Bloomberg $1,436,330 $1,747,632 $1,360,729 $1,185,257 $300,093 $100,000 $300,000 $500,000 $700,000 $900,000 $1,100,000 $1,300,000 $1,500,000 $1,700,000 $1,900,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Value Value+Growth Value+Momentum Value+Dividends S&P 500 8
  9. 9. Value Proposition EDGE 1: Value Investing Strategy The most successful fund managers are value investors. Academics conclude that value investing results in significant alpha. EDGE 2: Quantitative Value Models Ideas sourced using proprietary quantitative value models Models backtested since 1996. Approach tested by academics and market practictioners (eg. Fund manager Joel Greenblatt and his magic formula method described in his New York Times best seller) EDGE 3: Qualitative Analysis Overlaying bottoms-up fundamental analysis enhances returns and reduces risk. Strategy beat stand-alone models in both 2013 (59% vs 51%) and 2014 (30% vs 4%) Value Investing Fundamental Analysis Quantitative Models 9
  10. 10. Qualitative Analysis After producing short lists of potential investment opportunities FATALPHA applies its qualitative overlay. During this process FATALPHA focuses on: Strong Fundamentals Preference for strong cash flow generators Low levels of debt (overleveraged companies are eliminated) Story Cheapness could be due to specific reasons. FATALPHA determines whether the market has exaggerated and if there is a opportunity for either a short-term rebound or medium-term reversal. Technic