Tag Archives: management

Ivy Portfolio January Update

The Ivy Portfolio spreadsheet track the 10 month moving average signals for two portfolios listed in Mebane Faber’s book The Ivy Portfolio: How to Invest Like the Top Endowments and Avoid Bear Markets . Faber discusses 5, 10, and 20 security portfolios that have trading signals based on long-term moving averages. The Ivy Portfolio spreadsheet tracks both the 5 and 10 ETF Portfolios listed in Faber’s book. When a security is trading below its 10 month simple moving average, the position is listed as “Cash”. When the security is trading above its 10 month simple moving average the positions is listed as “Invested”. The spreadsheet’s signals update once daily (typically in the late evening) using dividend/split adjusted closing price from Yahoo Finance. The 10 month simple moving average is based on the most recent 10 months including the current month’s most recent daily closing price. Even though the signals update daily, it is not an endorsement to check signals daily or trade based on daily updates. It simply gives the spreadsheet more versatility for users to check at his or her convenience. The page also displays the percentage each ETF within the Ivy 10 and Ivy 5 Portfolio is above or below the current 10 month simple moving average, using both adjusted and unadjusted data. If an ETF has paid a dividend or split within the past 10 months, then when comparing the adjusted/unadjusted data you will see differences in the percent an ETF is above/below the 10 month SMA. This could also potentially impact whether an ETF is above or below its 10 month SMA. Regardless of whether you prefer the adjusted or unadjusted data, it is important to remain consistent in your approach. My preference is to use adjusted data when evaluating signals. The current signals based on December 31st’s adjusted closing prices are below. This month (NYSEARCA: VNQ ) is above its moving average and the balance of the ETFs are below their 10 month moving average. The spreadsheet also provides quarterly, half year, and yearly return data courtesy of Finviz. The return data is useful for those interested in overlaying a momentum strategy with the 10 month SMA strategy: (click to enlarge) I also provide a “Commission-Free” Ivy Portfolio spreadsheet as an added bonus. This document tracks the 10 month moving averages for four different portfolios designed for TD Ameritrade, Fidelity, Charles Schwab, and Vanguard commission-free ETF offers. Not all ETFs in each portfolio are commission free, as each broker limits the selection of commission-free ETFs and viable ETFs may not exist in each asset class. Other restrictions and limitations may apply depending on each broker. Below are the 10 month moving average signals (using adjusted price data) for the commission-free portfolios: (click to enlarge) (click to enlarge) Disclosure: None

Evaluating Actively Managed Stock Funds With iM’s Terminal Value Rating System

This rating system identifies funds which may provide better returns than a benchmark index-fund by measuring fund performance from the perspective of savers who make regular monthly contributions to funds. It compares the terminal value from periodic $1.00 monthly contributions to a fund with the terminal value from the same contributions to a benchmark index-fund over the same time period. Specifically, the system calculates 1-year and 5-year rolling terminal values from $1.00 monthly contributions to the fund and the benchmark index-fund. Predictive information comes from the relationship between the fund and the benchmark rolling terminal values, allowing an estimate of future fund performance relative to the benchmark index-fund. The Terminal Value Rating System Since most investors aim to save an adequate amount for retirement, it is appropriate to calculate the terminal value from monthly contributions to a particular fund and compare this to the terminal value if the same contributions had been made to a benchmark index-fund instead. (In this analysis SPY, the ETF tracking the S&P 500, is used as the benchmark index-fund, hereinafter referred to as benchmark.) This method provides a better picture of fund performance for savers as it measures the end value from periodic investments to a fund, rather than performance over standard fixed time periods. (Fund prices adjusted for dividends are mainly from Yahoo Finance. Also, the system only applies to funds with no front load fees.) An evaluation of the relationship between the 1-year and 5-year rolling terminal values for investments in a fund, and the corresponding rolling terminal values for investments in the benchmark, can provide a good estimate of future fund performance relative to the benchmark. The relationship is termed “Rolling Performance” and is defined in the Appendix. The ratings derived from this analysis range from a grossly underperforming ‘E’ to a good outperforming ‘A’. In the charts, the ratings are based on the most recent past 1-year and 5-year Rolling Performances, shown as “iM RATING: 1yr(5yr)”. Desirable funds should have a 1-year rating of ‘C’ or better, and a 5-year rating of ‘B’ or better. (See the Appendix for Rating Criteria) Over and above the simple 5-bin rating, charts are produced to visualize, and substantiate, the fund’s rating. How to interpret the charts The upper two graphs in the charts show the actual terminal values obtained from investing $1.00 every month in the fund and the benchmark. These are the sums of all contributions including all gains and losses to the end of November 2015, and indicate the total savings over time. A desirable fund would continuously have had higher terminal values than those for the benchmark. The lower two graphs in the charts are the 1-year and 5-year Rolling Performances. The 5-year Rolling Performance should preferably be continuously positive, which would indicate that an investor would always have done better investing in the fund than in the benchmark over a five year period. For future fund performance to be better than the benchmark would require the 1-year and 5-year Rolling Performance graphs near the end to be positive and to have upward (positive) slopes as well. Positive 1-year and 5-year Rolling Performances show that a fund performed better than the benchmark over the last year and the last five years, respectively. Upward slopes of the Rolling Performance graphs would indicate that fund performance had constantly gained over the benchmark while the slopes were positive and should also signal further excess gains for the fund over the benchmark in the near-term future. Example of a fund likely to outperform SPY An example of a fund that should continue to provide better performance than the benchmark is T. Rowe Price Growth Stock (MUTF: PRGFX ) with an iM-Rating of B(A). Had one invested $1.00 each month starting on the last day of February 1993, one would have contributed a total of $274 including the last contribution at the end of November 2015. The terminal value, that is the sum of all contributions including all gains and losses to November 2015, would have been $893. Had one made the same contributions to SPY, then the terminal value would have been $746. A saver would have had 19.7% more money at the end from investing in PRGFX than from investing in SPY. The upper pair of graphs in the chart which are plotted to a semi-log scale shows the performances over time. (click to enlarge) (click to enlarge) The terminal value rating system is especially useful to determine the likely future performance trend for a fund. The 1-year and 5-year Rolling Performances are shown by the green and purple graphs, respectively, at the bottom of the chart. One can see that since May-2000 for most of the time PRGFX provided better returns over five years for savers than SPY. As of 11/30/2015, the value of the 5-year Rolling Performance is +7.6%, and the 1-year Rolling Performance is +2.8%. This indicates that over the last five years and one year a $1.00 per month investor would have had, respectively, 7.6% and 2.8% more savings from PRGFX than from SPY. Both Rolling Performance values are positive and the slope of the 5-year Rolling Performance graph since Aug. 2014 is also positive, which is a good indication that performance of this fund relative to SPY should be higher also for the near-term future. Example of a fund likely to underperform SPY An example of a fund that will likely continue to underperform the index-fund is the CREF Stock Account (QCSTRX) with an iM-Rating of D(E). This is one of the oldest and largest actively managed stock funds in the U.S. with about $117-billion in assets, representing about 13.5% of total assets under management at TIAA-CREF. Had one invested $1.00 each month starting on the last day of February 1993, one would have contributed a total of $274 including the last payment at the end of November 2015. The terminal value, that is the sum of all contributions including all gains and losses to November 2015, would have been $651. Had one made the same contributions to SPY then the terminal value would have been $746. A saver would have had 14.6% more money at the end from investing in SPY than investing in the CREF Stock Account. The upper pair of graphs in the chart which are plotted to a semi-log scale depicts the performances over time. (click to enlarge) (click to enlarge) The fund versus benchmark 1-year and 5-year Rolling Performances are shown by the green and purple graphs, respectively, at the bottom of the chart. One can see that QCSTRX provided worse 5-year returns for savers than SPY from 1998 to 2002 and then again from 2011 to 2015. The latest value of the 5-year Rolling Performance for QCSTRX is -8.2%, meaning that over the last five years a $1.00 per month investor would have had 8.2% less savings from the CREF Stock Account than from SPY. Similarly, the 1-year Rolling Performance for QCSTRX is -2.2%, meaning that over the last year a $1.00 per month investor would have had 2.2% less savings from the CREF Stock Account than from SPY. Both Rolling Performance values are negative, and at the end the trajectories of both Rolling Performance graphs also point lower. This indicates that this fund is likely to provide lower returns for investors than SPY in the foreseeable future as well. Conclusion Of the many actively managed stock funds we investigated only a few funds have produced better returns than the benchmark SPY, and are likely to continue to outperform SPY. These funds are characterized with 1-year and 5-year ratings better than ‘C’, and would have had positive 5-year Rolling Performance over longer periods. The charts and iM-Ratings for three such large-cap stock funds, JGASX, FDGRX and GTLLX are provided in the Appendix. Appendix Special terms and abbreviations Terminal Value (TV): The sum of all contributions including all gains or losses from a specified starting date to the present or to a specified past date. PRGFX(+1/mo), QCSTRX(+1/mo), SPY(+1/mo): Terminal values from all past consecutive monthly $1.00 contributions made in PRGFX, QCSTRX, and SPY. 1-year Rolling Performance: The percentage difference between the terminal values from the past 12 consecutive rolling monthly $1.00 investments made in a fund and the benchmark, calculated as (TV12 (fund) – TV12 (bench) ) / TV12 (bench). 5-year Rolling Performance: The percentage difference between the terminal values from the past 60 consecutive rolling monthly $1.00 investments made in a fund and the benchmark, calculated as (TV60 (fund) – TV60 (bench) ) / TV60 (bench). iM-Rating Criteria The Rating criteria are based on the most recent past 1-year and 5-year Rolling Performances with the thresholds as listed below. Performance Rating Thresholds Rating Thresholds A above 6% B 2% to 6% C -1% to 2% D -5% to -1% E below -5% Other Funds likely to outperform SPY JPMorgan Growth Advantage Sel (MUTF: JGASX ) with iM-Rating C(A). The funds VHIAX, JGACX, JGVRX, JGVVX are of the same class family and all enjoy the same rating. (click to enlarge) (click to enlarge) Fidelity Growth Company (MUTF: FDGRX ) and FDEBX of the same class, both have an iM-Rating of C(A) (click to enlarge) (click to enlarge) Glenmede Large Cap Growth (MUTF: GTLLX ) with IM-Rating C(A) (click to enlarge) (click to enlarge) No Recession Is Signaled By iM’s Business Cycle Index: Update December 31, 2015

U.S. Small Caps: Smoke And Mirrors

Summary The aim of this quick study is to check whether the well-known outperformance of US small caps over US large caps: Is true? Is persistent with respect to market timing? Is persistent with respect to internal selectivity within the index? Every investor – rookie or experience – already would have heard about the well-known, small caps’ outperformance. The topic is not as simple as it seems to be. It has to be followed very cautiously. This article is an attempt to give readers some major keys, enabling them to avoid expensive mistakes. This study relies on two indices: – S&P 500 Total Return – Russell 2000 Total Return Database stands between December 31, 1998 and December 22, 2015. Persistent with Market Timing? We can notice that an investor who checked their performance at the end of each year, and who had kept their equity position until December 22, 2015 would have noticed an outperformance of S&P 500 versus Russell 2000 no matter they had invested at the end of 2004, 2005, 2006, 2007…or 2014. This outperformance varies between 1.7% (investment at the end of 2007) and 24.9% (investment at the end of 2010). Therefore, the post 2008 rally in equities was clearly driven by large caps (here through S&P 500) over small caps (here through Russell 2000). In the table below, the outperformance of large caps is exhibited in the bottom right. Everywhere else in the table, and whatever be the holding period, the Russell 2000 has posted a better performance than the S&P 500. The only period in which we notice a similar outperformance by the S&P 500 was during the equity market crash in 2007-2008 as large caps were being considered safer than small caps – a case of clear defensive reaction. The rally that followed enabled the US equity markets to rise by 162.3% for the S&P 500 since December 31, 2008 and by 150.5% for Russell 2000 since December 31, 2008. Please note that between December 31, 2010 and December 22, 2015, the S&P 500 rose by 80.2% whereas Russell 2000 posted ‘only’ a 55.3% growth. There is one explanation for this: the market has changed, with the increase in ETF investing, smart-beta and systematic strategies. (click to enlarge) Source: Author’s own The 15.9% number in the table shows the difference between S&P 500 Total Return and Russell 2000 Total Return between December 31, 2010 and December 22, 2014. From the table we can infer that until 2010, the Russell 2000 has been outperforming the S&P 500 regularly, except in 2007-2008, where the ‘washout’ was much more important for small caps than for large caps. It seems that since 2010, investor behavior has changed with a big shift towards ETFs and smart-beta, risk premia solutions, focusing on large caps and low-volatility assets (Minimum Variance method, Equal Risk Contribution). Persistent with Internal Selectivity Within the Index – Actuarial and Total Return We check the composition of each index at the last day of year Y-1, and assume the composition remains stable over year Y. Given the huge rotation of US indices, it is a way to minimize the error due to index reshuffle and to birth and death sample bias. Source: Author’s own Look at the 1999 table. The Russell 2000 posted a 21.3% performance, with an average performance of the components of 25.6%. The median is -7.6%! almost 30 points low. Except in 2002, the median performance of the Russell 2000 components has been always below the average performance, or below the performance of the Index. Two explanations: – The median performance of the components is lower than the average performance. This means that the distribution exhibits excessively large returns on the positive side, dramatically shifting the average return on the upside. – The average performance of the components is lower than the index performance. This means that these indices, being capitalization-weighted, give more weight to large capitalizations. Therefore, large capitalizations tend to outperform small, even within the Russell 2000 Index. Shown below is the distribution of the annual performances of the components from S&P 500 and Russell 2000. Source: Author’s own These distributions are very interesting, especially focusing on the extreme left tail, the right hand part of the body and the extreme upper side of the distribution. Without any surprise, tails are a lot thicker for Russell 2000 than for S&P 500. Moreover, on Russell 2000, best annual performances exceed 1000%. Question is: Given the well-known investor asymmetry between gain and loss, do you think that a stock which is up 100% YTD will be kept in the portfolio by the asset manager? Don’t you think that he would cut the position in order to ‘take his profit’? Therefore, in a stock-picker paradigm, and given the behavioral and cognitive biases, it can be considered as very difficult to keep a large (> 100%) winning position. Thus, the contribution of positive extremes to the Russell 2000 cannot be taken into account in a stock-picking framework. Using medians in order to measure each stock performance seems then a much more reasonable assumption (look below). (click to enlarge) Source: Author’s own This table shows the difference between the median of S&P 500 and the median of Russell 2000. Since 2004, the median of S&P 500 outperforms regularly the median of Russell 2000. In other words, if your stock-picking is not able to catch the extreme positive returns on Russell 2000, then you should shift to stock-picking within S&P 500, as the best proxy of your expected return (the median) is by far higher on the latter index. On the other hand, should you be interested in investing through ETFs, then you can choose to invest in Russell 2000 ETFs rather than in S&P 500 ETFs as you get the performance of the index. Until 2010, the Russell 2000 Index used to outperform S&P 500 regularly. Within the Russell 2000, may we exhibit any pattern? In the image below, colors are important – the more positive, the greener, the more negative, the redder. Rows stand for capitalization quartiles, from the smallest (top) to the largest (bottom). Columns stand for volatilities quartiles from the smallest (LHS) to the largest (RHS). Source: Author’s own Looking at the performance (capitalization (row); volatilities (column)), we can notice that although over the period, the performance of the index is largely positive (+249% total return between December 31, 1998 and November 11, 2015) – meaning it was a bull market on average 7.7% per year, the red cells are much more represented on the right column of the table. This happens when the index performance is negative, of course (2002, 2008), but it also happens when the index performance is flat or mildly positive (2000, 2001, 2004, 2011, 2012, 2014, 2015). On the other hand, these high volatility stocks strongly outperform the universe in two periods out of seventeen: 1999 and 2003, with respective total return performance of the Russell 2000 of +21%, +47%. This means that the outperformance of volatile small caps is very hard to capture because over the long run it may be easy to experience huge drawdowns with difficulties to recover. Keep in mind that when a stock drops by 50%, it needs to increase by 100% to come back to the initial level. Regarding capitalization effect, things seem to be more difficult to explain. As a summary for this part, should you want a smooth pattern, focusing on the low-volatility stocks in N-1 is worth in order to succeed in such a challenge, whereas dealing with historically high-volatility stocks may suffer from huge drawdowns (2002, 2008), and only rare astonishing performances, which may struggle in erasing the previous underperformance. The issue is always the same: what is your investment timeframe? For more information: Why US investing differs a lot from European investing Conclusion Due to the weight of extreme returns, the performance of Russell 2000 is pulled up dramatically. Russell 2000 is a non-representative index of small caps given that the small caps universe can be summarized as “many are called, but few are chosen,” but the ones which are chosen exhibit amazing performances (more than +1000% per year) hiding the many which are not chosen and post performances close to -100%. The asymmetry of actuarial returns (compared to logarithmic returns) then emphasizes these extreme positive returns whose upper limit is + infinity, whereas a stock price cannot go below 0, flooring the extreme bad performance to -100%. Second, given the asymmetry of the investor with gain and loss, these extreme positive returns are not sustainable in a stock-picking framework, as everybody knows that investors are likely to take profit on a largely winning position, meaning that it is very unlikely that they keep an equity position whose performance already equals +100% per year. Therefore, studying the small cap universe through the mean does not seem to take this behavioral bias into account. Using the median seems more relevant. In addition to the data explained, investing in US small caps by picking stocks from the Russell 2000 means struggling with scarce liquidity. In a nutshell, should you want to invest in small caps, do it through a Russell ETF; should you want to pick up stocks, you should rather choose an S&P 500-equivalent universe, as the left tail of the distribution of S&P 500 is a lot thinner than the one of Russell 2000. The development of ETFs and the increasing flows on these strategies and smart-beta and risk premia are likely to increase the pattern we exhibit in this paper. So from now, when speaking about the outperformance of small caps, you can say, “Small caps are smoke and mirrors. Should you want to outperform the S&P 500, you have to be good at picking the stocks (the famous 2% positive extremes), AND you have to be good at timing the market ” Companies whose aim is to pick up US Small Caps almost always underperform the Russell 2000 (Median Performance of the Members < Index Performance). Now you are able to understand why. Would you rationally invest in such a strategy? (Too?) many people are convinced that they have the skills to pick up the famous 2% stocks that post astonishing performances. Be careful as too much self-confidence is likely to turn into overconfidence and a long-term underperformance.