Philosophy Statements

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1 August 23, st AFORES Conference Myron S. Scholes, Ph.D. Frank E. Buck Professor of Finance, Emeritus, Stanford University Chief Investment Strategist, Janus Capital Group The views presented are as of April They are for information purposes only and should not be used or construed as an offer to sell, a solicitation of an offer to buy, or a recommendation for any security or market sector. No forecasts can be guaranteed. The opinions and examples are meant as an illustration of broader themes, are not an indication of trading intent, and are subject to change at any time due to changes in market or economic conditions. There is no guarantee that the information supplied is accurate, complete, or timely, nor are there any warranties with regards to the results obtained from its use. It is not intended to indicate or imply in any manner that current or past results are indicative of future profitability or expectations. As with all investments, there are inherent risks to be considered. Janus Capital Group Inc. is a global asset manager offering individual investors and institutional clients complementary asset management disciplines. Janus Capital Management LLC serves as investment adviser. Janus is a registered trademark of Janus International Holding LLC. Janus International Holding LLC. C

2 About the Presenter Myron Scholes, Ph.D. Chief Investment Strategist, Janus Capital Awarded Nobel Memorial Prize in Economic Sciences, 1997 Received the Innovator of the Year award from the Chicago Mercantile Exchange and the Lifetime Achievement Award from the Derivatives Association Frank E. Buck Professor of Finance, Emeritus at the Stanford Graduate School of Business Director of Dimensional Fund Advisors mutual funds 1

3 Philosophy Statements Compound returns, not average returns or performance relative to a benchmark, should be our major focus. They are enhanced most by mitigation of tail losses and participation in tail gains, each period of time. The options market prices provide valuable information about the risk of each period s gains and losses. Myron Scholes, Nobel Prize recipient in Economics, 1997 Much of the real world is controlled as much by the tails of distributions as by means or averages: by the exceptional, not the mean; by the catastrophe, not the steady drip We need to free ourselves from average thinking. Philip Anderson, Nobel Prize recipient in Physics,

4 Merging Two Strands Relative Performance Measurement The costs and benefits of staying close to a benchmark Absolute Return Investing The future course: a concentration on absolute return investing. That is, Maximize terminal value compound returns not average returns Compound returns differ from average returns Worry about risk, in particular, risk of draw downs and shapes of distributions The distributions of returns are not constant. Expected returns and volatilities change for individual securities and most definitely for portfolios because asset compositions change and correlations change among securities; at times, diversification might be lost 3

5 Asset Pricing Tracking error converts investment from absolute to relative returns (Information Ratio or Sharpe ratio) Stay close to the herd Does not foster longer-term investing. Move to static cross-sectional investing (e.g. 60% equities/40% bonds) Does not allow for dynamic risk control 4

6 Constraints Affect Asset Prices Paper with Ash Alankar and Peter Blaustein Investors rationally constrain their active investment managers tracking-error constraint (herding), less costly for: Explicit monitoring costs are high Hard to evaluate performance: Carrot or stick, now third arrow measurement problems costly to overcome Investors own risk management purposes across multiple investments is difficult if their managers don t stay close to their designated benchmarks. Problem pushed up to asset allocation Managers want constraints as well to concentrate on skill and not on selecting risk allocations Attracting and keeping investors. Stay close to benchmark and don t worry about absolute risk. Paid for relative and not absolute performance Leads to implicit or explicit tracking-error constraints. Existed in one form or other for decades Explicit costs are traded off for implicit costs in lost returns? Why? Higher beta assets underperform; higher idiosyncratic risk assets underperform. Lost returns: explanation for low beta and idiosyncratic risk anomaly 5

7 U.S. and International Markets Low beta outperformance of high beta stocks same for global fixed income, commodities and currency markets across multiple asset classes and geographies Frazzini and Pedersen (2013) Underperformance of stocks with high idiosyncratic volatility extends to international markets Total volatility matters, idiosyncratic and systematic. Ang, Hodrick, Xing and Zhang (2009) 6

8 Our Major Findings With a tracking error constraint, the manager finances his active investments by using low volatility stocks. He holds his higher risk assets to control tracking error The portfolio is inefficient relative to an unconstrained portfolio. It is not on the efficient set The greater is the risk-to-reward ratio of his active portfolio, however, the more the manager wants to deviate from the benchmark and must finance more of the active position from lower risk assets while holding higher risk assets to mitigate the trackingerror constraint Our model has dynamics based on alpha beliefs and empirical evidence supports the model predictions 7

9 Three Ways to Earn Returns: Elephant Prediction of cash flows, growth rates, discount rates of companies or factors. (Turning over inventory, proactive) Alphas Most think of this as the way to enhance returns. Providing intermediation services to the market. Liquidity (risk transfer), differential skills to understand uncertainty. (Turning over inventory, reactive) Omega making markets work. But, time needed and illiquid. Core of the endowment model. And, market risk downplayed. E.g. banks, broker/dealers, hedge funds, PE firms, corporations Holding systematic exposures that are priced in the market. (Holding inventory, e.g. index funds => risk dynamics) Betas, (smart betas) but what are factors and their dynamics, (cluster dynamics)? Are these true factors? Why? Risk and expected returns of factors are not constant. Take account of or ignore dynamics. Correlations are not constant over time Time-series diversification should be focus of investment. Most importan 8

10 Factors that Affect Terminal Wealth: Systematic Components and Tracking Error Compound returns less than average returns even for normal distribution. Compound returns affect the level of terminal wealth Average returns (risks) are misleading. All of society uses them The urns are not constant, not normally distributed 1) Allowing risk to fluctuate around average (target) risk reduces compound returns Excess Volatility is a cost in lost returns 2) Fat-tailed events (tail gains and losses) biggest effect on compound returns. The tails are everything 3) Skewness. Volatility ignores skewness. Negative skew reduces compound returns Cross-section and Time-series diversification 9

11 What Determines Compound Returns? The distribution of future returns tells us everything we need to know, in particular the 1. Mean of the distribution of future returns: Average Return 2. Shape of the distribution of future returns: Risk of Returns A. Left tail risk B. Right tail risk C. Often much more important! Different Means Means Different Shapes Density % -60% -40% -20% 0% 20% 40% 60% 80% 100% 120% Annual Returns 0-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% Annual Returns 10

12 Time Diversification Investors ultimately care about terminal value -institutions typically have ten-year or longer time horizons Historically, terminal value impacted significantly due to lack of time diversification Consider two strategies: 1. Variable Risk Across Time: Fully invested in stock market 50% of the time 2. Constant Risk Across Time: Half invested in the stock market 100% of the time Growth of $100 $450 $400 $350 $300 $250 $200 $150 Both strategies have the same per-period expected return: 1/2 the return of overall stock market Strategy 2 is diversified across time -it has a volatility of 50% of the market vs. 71% for strategy 1 Growth of $100 invested in the S&P 500 Index Varying Risk Across Time Constant Risk Across Time 5.4% 4.7% $100 Jan-88 Jan-92 Jan-96 Jan-00 Jan-04 Jan-08 Jan-12 The compound return is greater under the Constant Risk Strategy 11 The hypothetical example does not represent the returns of any particular investment. Source: Bloomberg

13 Compound Return Facts: Tail Risk Dominates! The risk of returns is much more important than the average return, with tail risk playing the dominant role. Example: The realized return of U.S. equities can be explained by the extreme tail gains or extreme tail losses. Take out the extreme tail gains, realized return fell to almost zero Take out the extreme tail losses, realized return nearly double Hypothetical Growth of $1 Invested in U.S. Large Cap Equities (1/1/ /31/2015) 10,000,000 1,000,000 U.S. Large Cap Equity Miss Extreme Gains 9% annual return ~ $1,200, ,000 Avoid Extreme Losses ($) 10,000 1,000 5% annual return ~ $3, % annual return ~ $ Compound return is won by getting the few large moves correct; the many small moves are uneventful! 12 Source: Ibbotson as of 12/31/15. Based on monthly returns. Past performance is no guarantee of future results. Assumes reinvestment of income and no transaction costs or taxes. This data is for illustrative purposes only and not indicative of any investment. An investment cannot be made directly in an index. An extreme tail gain or loss is described as any monthly period whose performance is 2 standard deviations above or below the average monthly return for the entire period. From the period 1/1/ /31/1925, individual security returns were gathered from U.S. financial periodicals on a monthly basis, beginning with the official list of the New York Stock Exchange during that time period. From the period 1/1/1926 monthly returns data on the S&P 500 was collected. FOR INSTITUTIONAL INVESTOR USE ONLY / NOT FOR PUBLIC VIEWING OR DISTRIBUTION

14 Most Asset Classes Exhibit Fat Tails MSCI EAFE: Jan 1970 Apr 2013 Commodities: Jan 1991 Apr 2013 Global High Yield: Jan 1990 Apr Frequency Frequency Frequency (26) (14) (2) (25) (15) (5) (21) (15) (9) (3) Monthly Return (Percent) Monthly Return (Percent) Monthly Return (%) 13 Past performance does not guarantee future results. Commodities represented by DJ UBS Commodities Index; global high yield by Barclays Global High Yield Index. Source: Barclays, DJ UBS, MSCI

15 Reducing the Size of Tail Losses Can Have Material Impact (focus) Even a so-called balanced account has experienced tail events Reducing the realized left tail losses by 1/2 or 1/3 would have dramatically improved outcomes Global 60/40: January 1930 December 2012* Growth of $1: January 1930 December 2012 Frequency (24) (14) (4) 6 16 Monthly Return (Percent) (24) (18) (12) (6) Thousands Reduce Left Tail by ⅓** $12,931 Reduce Left Tail by ½** $4,904 60/40* $680 *The Global 60/40 is allocated 60% to the S&P 500 and 40% to Ibbotson fixed-income from 1930 through 1989, and 60% to the MSCI World and 40% to the Barclays Global Aggregate thereafter, with currency risk hedged to USD. **Left tail is defined as the returns worse than the 10th-percentile outcome of the global 60/40 portfolio from 1930 through 2012, which was (3.06)%. The series that reduces left tail by 1/3 assumes that all returns that were worse than (3.06)% were reduced by 33%; the series that reduces left tail by 1/2 assumes that all returns worse than (3.06)% were reduced by 50% 14 This hypothetical example does not represent the returns of any investment. Source: Barclays, Ibbotson Associates, MSCI, S&P

16 Investment Strategies Asset Allocation Static Asset Allocation Strategies Common Approach Benchmark and Tracking Error E.g. 40% bonds and 60% stock. Based on static weights that, on average, provide a risk profile. Buckets are filled within the bond/stock allocations. Dynamics only through readjustment to static weights (60/40) Mean reversion (static rules to rebalance.) Not static risk, however. Other methods: life-path products, index fund products, or factor products (smart betas) (small vs. big, etc.) also ignore risk dynamics Dynamic Asset Allocation Strategies Risk Management. Reallocate to asset classes based on changing risk and return characteristics Risk-parity strategies. Keeping risk constant? But, only measure based on history and assume normal distributions and constant correlations. Concentrate on downside risk to protect against draw downs. Dynamic adjustments as risks change 15

17 How to Enhance Compound Returns? Historically, forecasting returns with accuracy has shown to be futile.a more productive path is managing the risk to improve compound returns. Target the following outcomes to improve the shape of the distribution. 1. Reduce risk of drawdown (left tails) 2. Participate in rising markets (right tails) Our Adaptive Multi-Asset Allocation product is designed to achieve exactly this.excess returns through dynamic tail risk management we are not managing expected returns, but rather managing risk Year Compound Return (Multi-Asset Simulation vs. Global 60/40)* Power of targeted outcomes over time Year Compound Return (Multi-Asset Simulation vs. Global 60/40)* Density Compound Return (%) Annualized Compound Return (%) Multi-Asset Simulation Global Balanced 60/40* 16 This page contains hypothetical performance. Please see disclosure on pages 20 and 21. *Global 60/40: 60% MSCI All Country World Index/40% Barclays Global Aggregate Bond Index

18 Measuring Tail Risk Using Options Prices Prices of put options are like insurance - Predictive Markets: Poon and Granger (2003) Three general types of options 1. Out-of-the-money put options protect you against very large losses. The higher their price the large the left tail 2. ATM options benefit from both large and small moves. Their price can tell you about the distribution of small moves (volatility) 3. Out-of-the-money call options are lotto tickets on the large gains. The higher their price the larger the right tails 1) Left Tail 2) Volatility 3) Right Tail Hypothetical Outcome -55% -48% -40% -32% -24% -16% -8% 0% 8% 16% 24% 17 ATM stands for at-the-money.

19 Risk Targeted & Constant Risk (ACWI): Simulation YR ACWI Index Adaptive Benchmark Constrained α Adaptive Constant Risk Target of 15% % 11.3% -0.1% 17.0% 5.6% % 22.8% 1.3% 26.0% 4.5% % 12.5% 0.3% 11.8% -0.4% % -35.8% 6.1% -32.1% 9.8% % 38.2% 2.8% 34.9% -0.5% % 17.5% 4.3% 16.9% 3.7% % -1.4% 5.5% -4.3% 2.6% % 16.9% 0.1% 16.2% -0.6% % 22.7% -0.7% 26.8% 3.4% % 8.5% 3.8% 9.0% 4.3% % -0.1% 1.7% -3.7% -1.9% ALL 5.8% 8.5% 2.7% 9.1% 3.3% α Simulated Simulated Cumulative Cumulative Log Log Performance Performance Stdev 16.3% 15.0% 8.2% 14.2% 11.9% Max DD -55% -48% N/A -41% N/A Beta 100% 91% N/A 85% N/A Dec-04 Dec-06 Dec-08 Dec-10 Dec-12 Dec-14 This contains simulated performance. Please see the disclosures on page 20. Transaction cost assumption for equities, 40 bps. 18 Adaptive Benchmark Constrained (MSCI ACWI) Adaptive Constant Risk Target of 15% (MSCI ACWI) MSCI ACWI Index

20 Objective Based (ACWI): Simulation 4 Cumulative Performance ACWI 30% max 12M DD Budget 20% max 12M DD Budget 10% max 12M DD Budget Dec-03 Dec-05 Dec-07 Dec-09 Dec-11 Dec-13 Simulated Portfolio Measure 30% Drawdown Control 20% Drawdown Control 10% Drawdown Control MSCI ACWI Compound Return 10.8% 9.5% 5.6% 7.5% Average Return 10.6% 9.3% 5.6% 8.5% Convexity Cost (Compound - Average Ret) 0.2% 0.2% 0.1% -1.0% Volatility 7.8% 6.0% 3.2% 15.4% 3M ETL -4.6% -3.0% -1.7% -16.1% 3M ETG 8.0% 6.5% 3.3% 14.4% Sharpe ETG/ETL Max DD -17.0% -9.4% -3.5% -55.3% 12M Max DD -17.0% -9.4% -3.5% -50.6% This contains simulated performance from December 2003 October Please see disclosure on page 20.

21 Objective Based (ACWI): Simulation Risk Postures and Impact of: 1. Losing less in Declining Markets AND 2. Participating in Rising Markets Distribution Distribution of 1 of Year 1 YR Compound Returns (Simulated) (simulated) Distribution Distibution of 5 of Year 5 YR Annualized Compound Returns Returns (Simulated) (simulated) Density Power of Tails over Time % -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% 1 YR Compound Return 0-8% -6% -4% -2% 0% 2% 4% 6% 8% 10% 12% 14% 16% 5 YR Annualized Compound Return ACWI max 12M DD budget = 10% max 12M DD budget = 20% max 12M DD budget = 30% ACWI max 12M DD budget = 10% max 12M DD budget = 20% max 12M DD budget = 30% 20 This contains simulated performance from December 2003 October Please see disclosure on page 20.

22 Objective Based 20% (ACWI): Simulation Positive Convexity Beta Adjusted Residual of 20% max DD Strategy vs. ACWI Beta Adjusted Residual of 20% Max Drawdown Strategy vs. MSCI ACWI (monthly returns) (Monthly Returns) 5% 4% 3% Outperform in down markets Participate in up markets Beta-Adjusted Residual 2% 1% 0% -1% -2% -3% -4% -5% -25% -20% -15% -10% -5% 0% 5% 10% 15% ACWI Returns This contains simulated performance from December 2003 October Please see disclosure on page 20. Residual defined as beta-adjusted difference between 20% target strategy and the MSCI ACWI Index. 21

23 Simulations Disclosures The Objective-Based simulation (the Model ) represents the hypothetical returns of a strategy that invests in the S&P 500 Index targeting a maximum realized drawdown objective over a rolling 12-month period, and is not based on the actual performance of any fund, strategy, index or portfolio. The hypothetical performance and exposures of the Model represents the returns of a hypothetical allocation on (Feb 1996 to March 2015) to a long only portfolio of U.S. equities and cash on the basis of expected tail losses as estimated using options prices, and historic realized drawdown. The data assumes that assets are allocated based on daily expected tail loss and expected tail gains, and current drawdown. The portfolio optimization process seeks to target a specific realized maximum drawdown over a 12-month rolling period, while also accounting for upside attractiveness. While the Model evaluates tail risks daily, it assumes trades are made less frequently on a varying basis, using current models and a model portfolio that is updated daily. It is implemented via futures contracts, and assumes no transaction costs. The occurrence of transaction costs and other additional costs would have the effect of decreasing performance results. The Expectations-based Multi-Asset simulation (the EBMA Model ) represents the hypothetical returns of a strategy that balances tail risks across growth, capital preservation and inflation assets, allocating to assets with the greatest expected tail gains relative to expected tail losses, and is not based on the actual performance of any fund, strategy, index or portfolio. The hypothetical performance and exposures of the EBMA Model represents the returns of a hypothetical allocation on (July 2007 to April 2015) to a long only portfolio that allocations to growth, capital preservation and inflation-sensitive assets on the bases of expected tail losses as estimated using options prices. In the simulation, growth assets are proxied by the S&P 500 Index, capital preservation assets are proxied by U.S. treasuries and inflation assets are proxied by a broad commodity based and U.S. breakevens (long Treasury Inflation Protection Securities and short Treasuries). The portfolio optimization process seeks to balance tail risk across asset groups, and increase allocations to those assets with expected tail gains. While the EBMA Model evaluates tail risks daily, it assumes trades are made less frequently on a varying basis, using current models and a model portfolio that is updated daily. The EBMA model assumes transaction costs across all assets (futures, equities, bonds) average 15 basis points. The occurrence of additional costs would have the effect of decreasing performance results. The Model was initially developed by Janus Capital Group, in It is predicated on the concept that time diversification can reduce portfolio convexity costs, thereby increasing long-term compound returns. The Risk Targeted simulation represents the hypothetical returns of a strategy that invested in S&P 500 stocks from February 1998 to July 2015, targeting individual stocks and sectors with attractive tail gains/tail loss expectations. It not based on the actual performance of any fund, strategy, index or portfolio. For purposes of the simulation, transaction costs assumption is 40 basis points. Constant risk is a term used to imply constant expected tail loss (ETL). As an example, if the portfolio s ETL objective over two months is 10%, and current ETL estimate for the portfolio is 14%, the portfolio manager would de-lever the portfolio. Similarly, if the portfolio s ETL objective over two months is 10%, and current ETL estimate for the portfolio is 6%, the portfolio manager would lever the portfolio accordingly. Though Janus may manage portfolios that seek to replicate the Model described above, there is no guarantee that they will do so. Janus may use other financial instruments and investment activities to replicate the Model. The performance of the portfolios will be affected by the cost of futures transactions, any other transactions, the fact that actual futures prices obtained may differ from closing futures prices on any day, and other fees and taxes. Information on Adaptive Solutions is being provided for discussion purposes only and may not be copied, reproduced or otherwise redistributed to anyone. Simulated performance results shown herein were prepared by Janus and were achieved through the retroactive application of models construed on the basis of historical data and designed with the benefit of hindsight. In particular, the simulations do not reflect actual trading in an account, so there is no guarantee that an actual account would have achieved similar results. In fact, there may be differences between simulations and actual results subsequently achieved. No assumption should be made that future performance will be equal to the simulation. In no circumstances should simulated results be regarded as a representation, warranty, or prediction that investors will achieve or are likely to achieve results equal to the simulations, or that losses can be avoided. The simulated results do not represent the investment performance or the actual accounts of any investors. The securities in the hypothetical simulations were selected with the full benefit of hindsight, after their performance over the period shown was known. It is not likely that similar results could be achieved in the future. 22

24 Presentation Disclosures Note to All Readers: FOR INFORMATIONAL PURPOSES ONLY. This document does not constitute and should not be construed as investment, legal or tax advice or a recommendation, solicitation or opinion regarding the merits of any investments. Nothing in the document shall be deemed to be a provision of investment management services by Janus Capital Group and its subsidiaries ( Janus ). Anything non-factual in nature is an opinion of the authors, and opinions are meant as an illustration of broader themes, are not an indication of trading intent, and are subject to change at any time due to changes in market or economic conditions. Investing involves risk, including the possible loss of principal and fluctuation of value. There is no assurance that the investment process will consistently lead to successful investing. Statements in this piece that reflect projections or expectations of future financial or economic performance of the markets in general are forward-looking statements. Actual results or events may differ materially from those projected, estimated, assumed or anticipated in any such forward-looking statements. Important factors that could result in such differences, in addition to the other factors noted with such forward-looking statements, include general economic conditions such as inflation, recession and interest rates. Janus is not responsible for any unlawful distribution of this document to any third parties, in whole or in part, or for information reconstructed from this document and do not guarantee that the information supplied is accurate, complete, or timely, or make any warranties with regards to the results obtained from its use. It is not intended to indicate or imply that current or past results are indicative of future profitability or expectations. 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