Tag Archives: lightbox

Dumb Alpha: The Drawbacks Of Compound Interest

By Joachim Klement, CFA The second installment of this series presented evidence that a simple random walk forecast typically performs better than the amassed expertise of professional forecasters for short-term forecasts of about 12 months. In this post, I argue that estimation uncertainty is not reduced for long-term forecasts either, because mean reversion cannot overcome the effects of compound interest. Luckily, there is a range of techniques, from simple to sophisticated, that can help long-term investors with this challenge. The “Muffin Top” Problem As most middle-aged people can confirm, age inexorably leads to a slowing metabolism. If you don’t change your diet, your waistline expands quite generously. In my case, I refused to notice these changes until I grew an undeniable “muffin top” of belly fat above my belt line. Chagrined, I changed my diet and stepped up my exercise, but so far — muffin doin’. This little anecdote is a rather fitting (if unappealing) metaphor for long-term investing. What I tried to force my body to do was to revert back to its original state (the mean), but the forces of mean reversion were not strong enough to do so. This scenario can happen in the world of investing as well. Imagine someone who wants to invest for the next 10 years and who is thus not interested in short-term forecasts so much as the long-term average expected returns of assets. Common wisdom states that, while return forecasts can be widely off the mark in any given year, in the long run, returns should converge towards a rather stable long-term mean. Because of mean reversion, it should be easier to forecast long-term returns than short-term returns. Compound Interest Ruins the Day In an important article in the Journal of Finance , however, University of Chicago economists Lubos Pastor and Robert Stambaugh showed that, in the presence of estimation uncertainty, mean reversion is not strong enough to reduce the volatility and uncertainty of long-term stock market returns. The main reason is that an estimation error in the first year will propagate and compound over the subsequent nine years, an estimation error in the second year will compound over the subsequent eight years, etc. Take, for example, an investment you know will average an annual return of 10% per year over the next 10 years. If in the first year the return is -10%, the average return over the subsequent nine years needs to be about 12.48% per year to make up for this shortfall. In other words, a 20% estimation error in the first year requires a relative increase in annual returns over the next nine years of 24.8%. If, on the other hand, the asset in the first year has a return of 0%, the average return over the subsequent nine years needs to be about 11.17% to make up for the shortfall. So a 10% estimation error in the first year requires a relative increase in annual returns of 11.7%. Half the estimation error requires less than half the relative return increase to make up for the shortfall. The investment results of the first few years have an oversized influence on the long-term investment returns — something that retirement professionals know as “sequence risk.” If you start saving for retirement and experience a major bear market in the first few years, you are much less likely to achieve your long-term financial goals than if you experience a rather benign environment at first and a bear market later. While the research by Pastor and Stambaugh is theoretical in nature, there is empirical evidence that long-term return forecasts are, in fact, just as uncertain and “inaccurate” as short-term forecasts. Ivo Welch and Amit Goyal have looked at the predictive power of many different variables that are commonly used to forecast equity market returns. They find that the forecast error does not materially change for forecast horizons between one month and 10 years. In other words, despite the existence of mean reversion, the uncertainty about future equity returns does not decrease in the long run. Facing the Challenge If long-term return forecasts are just as difficult to make as short-term forecasts, what can long-term investors do to create robust long-term portfolios? After all, we know that traditional Markowitz mean-variance optimization is about 10 times more sensitive to return forecast errors than to forecast errors in variances . There are in my view several possibilities, increasing from least to most in degree of sophistication: The equal weight asset allocation discussed in the first part of this series does not rely on forecasts, and thus is a simple and effective way to create robust long-term portfolios. Minimum variance portfolios and risk parity portfolios do not require any return forecasts and, if done properly, can outperform traditional portfolios by a wide margin. More sophisticated methods like resampled efficient frontier methodologies or Bayesian estimators can include estimation errors into the portfolio construction process and thus create portfolios that are more immune to unexpected events. Whatever technique one favors, there are ways to deal with forecast errors. Most critically, it is time investors take estimation uncertainty more seriously for the benefit of their clients and the long-term success of their portfolios. If you liked this post, don’t forget to subscribe to the Enterprising Investor . All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer. Disclaimer: Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.

Go On The Offensive To Preserve And Build Wealth

By Carl Delfeld I’ll never forget the advice given to me by the CEO of UBS Wealth Management. “Carl, remember one thing. Before you go out and tell your clients all the ways you’re going to make them money, try your best not to lose any.” And it’s certainly great advice for all of us, as we move into 2016 amid a high degree of uncertainty. Preserving capital should always be the top priority. Unfortunately, the traditional defensive strategy recommended to achieve this goal – high-quality bonds and blue-chip U.S. stocks – is obsolete. Rising interest rates could crush many bond portfolios, and you only have to look back at the global financial crisis to see how many blue-chip stocks lost almost half of their value in the blink of an eye. Let me share with you two potential ways to protect wealth that you won’t hear about anywhere else. They’re also a low-risk method to build wealth. One: Don’t Become Too Cautious and Defensive First, as any coach or smart sports fan will tell you, the surest way to lose a game when you’re ahead is to become too cautious and defensive. Why? You tend to become tentative, miss opportunities, and lose the initiative that built your success in the first place. It’s far better to intelligently stay on offense, pushing ahead while trying not to take any undue risks. Above all, this is never the time to try the same old tired plays. Two: Stay Open-Minded and Flexible This brings me to the second way to protect wealth, the need to be open-minded and flexible. For the past four or five years, a portfolio of U.S. stocks has performed pretty well, with the flat 2015 being an exception. Part of this is due to the Fed pumping in massive amounts of liquidity, no competition from zero rate Treasuries, and to the strong U.S. dollar attracting capital and hurting international stock returns. It certainly appears to me that at least some of these trends are reversing. This means you need to adjust by taking off the blinders and searching for new opportunities. And to do this you need to fight “home bias.” Going on the Offensive Home bias is the tendency to invest too much in your home country, thereby neglecting overseas opportunities. It’s very comfortable to lean towards the home stock market, but it is contrary to a cardinal rule of investing – not putting all your eggs in one basket. It’s also at odds with common sense since no one country – even America – has a monopoly on growth, value, and progress. When you really think about it, this home bias is puzzling. Why should the world’s best companies with the best growth prospects just happen to be in America or wherever your home country happens to be? I say this even though I’m a huge believer in America’s future, if we pursue the right policies and reforms. This is why I authored a book outlining what needs to be done: Red, White & Bold: The New American Century. In short, where a company is based means less and less; and what it does, and how well it performs, means more and more. And this is especially true for emerging markets, as the value of their stock markets plays catch-up with their contribution to global economic growth and share of the global economy. Just take a glance at these two great charts by JP Morgan. U.S. stock markets still dominate, accounting for 46% of the value of all listed companies in the world, while Japan’s share has dropped sharply from its peak in 1989 at over 30% to just 8% today. The share going to emerging markets has grown sharply, but still sits at around 13%, even though emerging markets represent 83% of the world’s population and half of global growth and output. This big gap between the value of all emerging-market-publicly traded companies and their contribution to global growth and the world’s economy will narrow – and this is your opportunity to cash in. The key trend to watch is the direction of the U.S. dollar. If the greenback levels out or begins to pull back in early 2016, undervalued emerging markets will really take off. The strategy you use to take advantage of this mismatch is critical. Stick with high quality companies showing good growth and strong balance sheets. Diversify across many countries, while favoring those that respect private capital, rule of law, a free press, and open markets. Most importantly, always use a trailing stop-loss to minimize risk. It’s the best hedge to protect your portfolio from a weaker U.S. dollar, and it could really supercharge your returns in 2016. Original Post

Incorporating The Volatility Risk Premium Into Portfolios

By Wei Ge, Ph.D., CFA, Senior Researcher, Parametric This is the third paper in a series that Parametric has published on the VRP. The first paper discusses the sources of the volatility risk premium, explaining that the VRP may come from three distinct sources: behavioral biases, economic factors, and structural constraints faced by investors (Ge [2014]). The second paper compares the three most common ways to monetize the VRP, all of which utilize financial derivatives: option strategies, variance swaps, and VIX Index futures (Ge [2015]). It concludes that investors may seek to harvest the volatility risk premium with either a dedicated capital allocation or an overlay strategy and suggests that using equity index options may be the best approach for investors to monetize the VRP, closely followed by utilizing VIX Index futures. The focus of this paper is to discuss how investors can incorporate the VRP into a balanced portfolio (60% equity/40% fixed income portfolio) or an equal-weight multi-asset diversified portfolio. Both dedicated VRP allocations and overlay VRP constructs are used to harvest the VRP, as suggested by the prior study. The paper concludes that both methods can potentially enhance investors’ portfolio returns as the VRP is an attractive and traditionally untapped source of returns that has low correlations with traditional risk premiums. Both approaches are shown to provide return enhancement without having to materially alter the risk profile of an investor’s portfolio; though the overlay approach potentially does so in a more capital-efficient fashion. Therefore, when permissible, investors may want to consider utilizing an overlay strategy as a potential method to pursue improved portfolio returns. Otherwise, investors may still enhance their portfolios’ returns by incorporating the VRP via a dedicated vehicle. DATA AND METHODOLOGY This study analyzes the VRP in the context of two scenarios typical of investors’ portfolios. The first scenario is a balanced portfolio consisting of 60% equity allocation and 40% fixed income allocation (the 60/40 Portfolio). Investors usually invest in a mix of equity and fixed income to balance two major objectives of their portfolios, to match liability requirements and to grow assets. The 60/40 Portfolio is the perennial model portfolio for analyzing an investor’s asset allocation as it should offer investors the growth prospects of equities and the stability of fixed income assets (Ambachtsheer [1987], Bernstein [2002]). Some have further suggested using a more dynamic allocation approach (Inker and Tarlie [2014]; Xiong et al. [2013]) to enhance the basic 60/40 portfolio. The 60/40 portfolio is considered by most practitioners a solid model portfolio representing the basic structure of portfolios. In this study, the 60% equity allocation is represented by the S&P® 500 Index and the 40% fixed income allocation is represented by the Barclays Capital Aggregate Index. Twenty-five years of annual return data is used in this scenario (Jan 1990 to Dec 2014). The second scenario is an equal-weight multi-asset portfolio ( the Diversified Portfolio), consisting of five sleeves with equal 20% capital allocations in five different asset classes, including: Equity – S&P 500 Index Treasury – Barclays US Total Treasury Index Credit – Barclays Capital Credit Index Commodities – Bloomberg Commodity Index Real Estate – FTSE EPRA/NAREIT Real Estate Index This scenario is intended to model generic diversified portfolios of investors that are less focused on equity. The Diversified Portfolio includes five different asset classes: equity, treasury, credit, commodities, and real estate 1 . Twenty-three years of annual return data is used for this scenario (Jan 1992 to Dec 2014), with the shorter date range due to lack of data for some of the constituent asset classes (e.g. commodities). In this study, a static asset value-based allocation is used to make the scenarios simple. The conclusions and lessons drawn from this study can be readily applicable to more sophisticated portfolios with more asset classes, such as absolute return assets, hedge funds, private equity, foreign allocations, or portfolios constructed using algorithm-driven methods. As suggested by our prior study, option-based strategies can be used to add exposure to the VRP for the model portfolios. Both the overlay strategy and the dedicated allocation approach are tested in this study and they are constructed as follows: The Dedicated VRP Construct – 50% S&P 500 Index exposure, 50% Treasury Bills, and a short strangle consisting of shorting S&P 500 Index puts and calls layered on top of the base assets. Both short positions of put and call options are explicitly and fully collateralized by the underlying S&P 500 Index and Treasury Bills and have notional values equal to the S&P 500 Index or Treasury Bills allocations. When incorporated into a portfolio, assets must be reallocated from other assets to the Dedicated VRP Construct. The Overlay VRP Construct – a short strangle based on shorting S&P 500 Index options with equal notional values 2 . Unlike the Dedicated VRP Construct, the short puts and calls are implicitly collateralized by the equity and fixed income allocations in the base portfolio. The Overlay VRP Construct does not require capital allocation and can be added on top of existing portfolios. In this study, 10% or 20% of the Dedicated VRP Construct allocation is used for both the 60/40 and Diversified portfolios. The equivalent level of the Overlay VRP Construct, short strangles consisting of 5% and 10% of call and put selling (totaling 10% and 20%) are added to both scenarios to test the overlay strategy. The weights of the Overlay VRP Construct are designed to compare with the Dedicated VRP Construct allocations based on equal footing. The results with dedicated allocations or overlay strategies are compared with the original scenarios both visually and quantitatively. RESULTS Figure 1 examines the performance statistics of the variations of the 60/40 balanced portfolio. From Jan 1990 to Dec 2014, the S&P 500 Index delivered an average annual return of 9.62% with a standard deviation of 14.64%, translating into a 0.45 Sharpe Ratio. The Barclays Capital Aggregate Index delivered an average return of 6.49% with a standard deviation of 3.67%, translating into a 0.95 Sharpe Ratio. The 60/40 Portfolio delivered an average annual net of fee return of 8.29% with a standard deviation of 9.08%, translating into a 0.62 Sharpe Ratio. It has a maximum drawdown of 32.53%. The performance of the 60/40 Portfolio falls between the two constituent series. The variations with the Dedicated VRP Construct allocations are named the “55/35/10” and “50/30/20” portfolios, respectively, with 55% or 50% S&P 500 Index, 35% or 30% Barclays Capital Aggregate Index, and 10% or 20% allocations to the Dedicated VRP Construct. The 55/35/10 portfolio delivered an average annual net of fee return of 8.52% with a standard deviation of 9.06%, translating into a 0.64 Sharpe Ratio. The 50/30/20 portfolio delivered an average annual net of fee return of 8.74% with a standard deviation of 9.06%, translating into a 0.67 Sharpe Ratio. The Overlay VRP portfolios, with the same 60% S&P 500 allocation and 40% Barclays Capital Aggregate allocation as the base, and the Overlay VRP Constructs consisting of 5% or 10% call selling and put selling (totaling 10% and 20% and named “Overlay 10” and “Overlay 20,” respectively), have similar levels of risk and maximum drawdown, but the annual net of fee returns improved to 8.69% and 9.10%, translating into Sharpe Ratios of 0.66 and 0.70, respectively. Figure 1: Adding VRP to the Simulated Balanced Portfolio (Jan 1990 – Dec 2014) Click to enlarge Source: Parametric, 9/10/2015. STD is the annual standard deviation of the returns; SR is the Sharpe Ratio; Max DD is the maximum drawdown based on monthly returns; Beta is the regression beta against the S&P 500 Index. Net of fee returns have a 35 bps management fee and are net of expected transaction costs. Simulated performance is for illustrative purposes only, does not represent actual returns of any investor, and may not be relied upon for investment decisions. Actual client returns will vary. All investments are subject to loss. You cannot invest directly into indexes. Please refer to the Disclosures included at the end of this material for additional important information. The simulated performance statistics indicate that adding the VRP into a balanced portfolio indeed improves the back-tested annual net of fee returns, from 23 bps and 45 bps for the Dedicated VRP Construct allocations to 40 and 81 bps for the two Overlay VRP Construct examples. The risks of the VRP variation portfolios remain similar to the base 60/40 Portfolio, in both return standard deviations and maximum drawdowns. Figure 2 plots the growth of the wealth of the five portfolios (60/40 Portfolio, two Dedicated VRP variations, and two Overlay VRP variation portfolios). The results confirm the improved wealth-generation capabilities of the VRP-based portfolios, with the VRP overlay strategies delivering better return enhancements. Figure 2: Simulated Growth of Wealth – Balanced Portfolio Without or With the IRP (Jan 1990 – Dec 2014) Click to enlarge Source: Parametric,9/10/2015. Net of fee returns have a 35 bps management fee and are net of expected transaction costs. Simulated performance is for illustrative purposes only, does not represent actual returns of any investor, and may not be relied upon for investment decisions. Actual client returns will vary. All investments are subject to loss. May not invest directly into indexes. Please refer to the Disclosures included at the end of this material for additional important information. Figure 3 examines the simulated performance of the variations of the Diversified Portfolio. From Jan 1992 to Dec 2014, the Diversified Portfolio, consisting of 20% allocations to five asset classes, delivered an annualized net of fee return of 7.09% with a standard deviation of 8.16%, translating into a 0.58 Sharpe Ratio. It has a maximum drawdown of 34.80%. The first variation of the Diversified Portfolio with the Dedicated VRP Construct allocation (Diversified + 10% Dedicated VRP Construct) consists of an 18% allocation to each of the five base asset classes plus a 10% allocation to the Dedicated VRP Construct. It delivered an annual net of fee return of 7.39% with a standard deviation of 7.97%, translating into a 0.63 Sharpe Ratio. The second variation with the Dedicated VRP Construct allocation (Diversified + 20% Dedicated VRP Construct) consists of a 16% allocation to each of the five base asset classes plus a 20% allocation to the Dedicated VRP Construct. It delivered an average annual net of fee return of 7.70% with a standard deviation of 7.81%, translating into a 0.68 Sharpe Ratio. The two variations with Overlay VRP Constructs (Overlay 10 and 20 Portfolios) have similar levels of risk and maximum drawdowns, but the annual net of fee returns improved to 7.50% and 7.91%, translating into Sharpe Ratios of 0.62 and 0.67, respectively. Note again that the exposure to short call and put options is covered implicitly by the allocations to equity and treasury assets. Figure 3. Adding VRP to the Simulated Diversified Portfolio (Jan 1992 – Dec 2014) (click to enlarge) Click to enlarge Source: Parametric, Credit Suisse Hedge Fund IndexesSM, 9/10/2015. STD is the annual standard deviation of the returns; SR is the Sharpe Ratio; Max DD is the maximum drawdown based on monthly returns; Beta is the regression beta against the S&P 500 Index. Net of fee returns have a 35 bps management fee and are net of expected transaction costs. Simulated performance is for illustrative purposes only, does not represent actual products or returns of any investor, and may not be relied upon for investment decisions. Actual client returns will vary. All investments are subject to loss. May not invest directly into indexes. Please refer to the Disclosures included at the end of this material for additional important information. The simulated performance statistics indicate that, again, adding the VRP into a multi-asset diversified portfolio improves the expected overall performance. The improvement net of fees is 30 bps or 61 bps annually with the Dedicated VRP Construct allocations, and 41 bps or 82 bps net of fees with the two Overlay VRP Constructs. The risks of the VRP variation (Overlay and Dedicated) portfolios, again, remain similar to the baseline multi-asset diversified portfolio or even more subdued, in terms of both return standard deviations and maximum drawdowns. Figure 4 plots the wealth index of the five portfolios (the Diversified Portfolio, two Dedicated VRP variations, and two Overlay VRP variations). The performance improvement is most significant for the Overlay 20 portfolio, followed by the Diversified + 20% Dedicated VRP portfolio, Overlay 10 portfolio, and the Diversified + 10% Dedicated VRP portfolio. Figure 4: Growth of Wealth – Simulated Diversified Portfolio Without or With the VRP (Jan 1994 – Dec 2014) Click to enlarge Source: Parametric, 9/10/2015. Net of fee returns have a 35 bps management fee and are net of expected transaction costs. Simulated performance is for illustrative purposes only, does not represent actual products or returns of any investor, and may not be relied upon for investment decisions. Actual client returns will vary. All investments are subject to loss. Please refer to the Disclosures included at the end of this material for additional important information. Simulated statistics of the two scenarios indicate that all the VRP variation portfolios improved the performance of the base portfolios and all have similar levels of risk as the base portfolios, in terms of returns volatility and maximum drawdowns. The return improvements are higher for the Overlay VRP variations than with the Dedicated VRP allocations. Implicit leverage may be a part of the reason behind the superior returns of the overlay approaches. When an overlay VRP construct with zero weight is added on top of a portfolio, an implicit leverage is introduced; it may add extra returns, but may also suffer losses. The addition of the short index option strangle layer on the base portfolio, however, introduces minimal changes to the risk profile of the portfolio, probably due to two reasons. First, the Overlay VRP Construct is implicitly fully collateralized by the equity or treasury allocations in the underlying balanced or diversified portfolios, reducing risks of exacerbated drawdowns. Second, the Overlay VRP Construct with the short index option strangle has an equity beta close to zero and a negative returns correlation with the equity market (Ge [2015]). The portfolio’s overall risk characteristics does not change significantly with the addition of the Overlay VRP Construct, exemplified by the similar standard deviations and maximum drawdown attributes of the variations. Therefore, the overlay approach of incorporating VRP into an investor’s portfolio should be considered the preferred approach if there are no constraints on leverage in investors’ mandates. The addition of the Dedicated VRP Construct allocations does not change the risk characteristics of the base portfolios for similar reasons. The overall performance enhancement, however, is slightly less significant due to the need to accommodate the Dedicated VRP Construct by reducing other assets in the portfolio. This method of incorporating the VRP may be utilized when the client is more risk averse or the overlay approach cannot be used due to legal constraints or mandate requirements. CONCLUSION This the third paper in the series that examines the equity insurance risk premium. This piece focuses on how to incorporate the IRP into investors’ portfolios. It builds on the conclusions of the two previous studies and explores the best method of incorporating the IRP into generic portfolios. Based on the conclusion of the previous study on the best way to monetize the IRP, this study analyzes the use of index option strategies, i.e. shorting S&P 500 Index option strangles, as either a collateralized Dedicated IRP Construct, or as an Overlay IRP Construct. The application of the IRP enhancement is analyzed based on two generic scenarios, one is the typical 60/40 balanced portfolio representative of many investors’ portfolios, and the other is an equal-weight multi-asset diversified portfolio consisting of five asset classes, representing typical portfolios of investors that are less focused on equity. The IRP can be beneficial to investors’ portfolios and the IRP addition in both formats can provide both return enhancement and diversification. The Dedicated IRP allocations improves both scenarios on a net of fees by a roughly annualized 2 to 61 bps while reducing the overall risk levels slightly. The Overlay 10 portfolio improves both scenarios on a net of fees by approximately 40 bps on an annualized basis, utilizing the same short strangle construct as the Dedicated IRP Construct. The Overlay 20 portfolio improves the net of fee return by approximately 80 bps. Neither overlay variation alters the risk profile of the portfolio significantly. The final conclusion is that investors may utilize the overlay approach to incorporate the IRP into their portfolios if legally permissible, by taking advantage of the implicit leverage embedded in the overlay approach, which should deliver elevated returns without significantly changing their risk profile. On the other hand, if the investors are risk averse or are constrained by legal restrictions or mandate requirements from utilizing the overlay approach, the incorporation of the IRP as a dedicated allocation may also be a good choice as it is capable of delivering potentially better returns without introducing significant extra risks. REFERENCES Ambachtsheer K. P. “Pension Fund Asset Allocation: In Defense of a 60/40 Equity/Debt Asset Mix.” Financial Analysts Journal, 43 (1987), pp 14-24. Bernstein P. L. “The 60/40 Solution.” Bloomberg Personal Finance, 2002. Ge W. “A Survey of Three Derivative-Based Methods to Harvest the Volatility Premium in Equity Markets.” Parametric White Paper, Parametric Portfolio Associates LLC, 2015. Ge W. “Understanding the Sources of the Insurance Risk Premium.” Parametric White Paper, Parametric Portfolio Associates LLC, 2014. Inker B, Tarlie M. “Investing for Retirement: The Defined Contribution Challenge.” GMO White Paper, GMO LLC, 2014. Malkiel B. G., Saha A. “Hedge Funds: Risk and Return” Financial Analysts Journal, 61 (2005), pp 80-88. Strohmaier J. “Low Volatility Investing: Expectations & Implementation Implications.” Parametric White Paper, Parametric Portfolio Associates LLC, 2015. Xiong J.X., Sullivan R. N., Wang P. “Liquidity-Driven Dynamic Asset Allocation.” Journal of Portfolio Management, 39 (2013), pp 102-111.