Tag Archives: lightbox

The Best And Worst Of February: Long/Short Equity

Long/short equity mutual funds and ETFs suffered another month of losses in February, falling 0.33% in the aggregate versus a drop in the S&P 500 Index of 0.13%. Of the universe of 179 funds with a full month of performance in February, only 65 managed to post monthly gains, but there were some particularly strong standouts. Nevertheless, the category saw total outflows of $399 million over the month and more than $3.9 billion for the year ending February 29, 2015. Will long/short equity funds be able to rebound and stem the outflows, or will the category continue to lose assets in March? Time will tell. Best Performers in February The three best-performing long/short equity funds in February were: QuantShares Hedged Dividend Income Fund (NYSEARCA: DIVA ) Gotham Absolute 500 Fund (MUTF: GFIVX ) Gotham Total Return Fund (MUTF: GTRFX ) The 1-year old QuantShares fund, with $3.6 million in assets, was February’s top performer, returning an astounding 9.26% for the month. For the year ending February 29, however, the fund was down 1.97%, but this was surprisingly good enough to rank in the top decile of the category. The fund’s one-year beta, relative to the S&P 500, was 0.45, but its alpha of -0.21% resulted in a Sharpe ratio of -0.35. Still, given the category’s substandard performance overall, investors invested a net $1.23 million in the fund for the year ending on Leap Day. Gotham’s pair of funds – GFIVX and GTRFX – ranked #2 and 3, respectively. The former returned +5.66% in February, giving it one-year returns of -2.83% through the end of the month, handily beating the S&P 500 Index, which fell 6.19% over the same period; while the latter returned +5.08% for the month, and didn’t have one-year returns since it launched on March 31, 2015. GFIVX, the more mature fund, had a 0.72 beta, alpha of 1.77% and volatility of 11.55% for the year ending February 29. This resulted in a one-year Sharpe ratio of -0.20, compared to that of the Index of -0.45. Worst Performers in February The three worst-performing long/short equity funds in February were: Neuberger Berman Global Long Short Fund (MUTF: NGBAX ) Catalyst Insider Long/Short Fund (MUTF: CIAAX ) Caldwell & Orkin Market Opportunity Fund (MUTF: COAGX ) The Neuberger Berman Global Long Short Fund was February’s worst-performing long/short equity fund, losing a stunning 8.59% for the month. This dropped its one-year return to -14.46% through the end of February, ranking in the bottom 10% of its category. Surprisingly, the fund enjoyed positive net flows for the year ending February 29, with investors putting $4.1 million more into the fund than they withdrew. Perhaps they’re attracted to the fund’s -0.06 beta coefficient, which is about as close to “uncorrelated” as you can get. But with a -15.40% one-year alpha, the fund’s low correlation hasn’t helped its investors much. The Catalyst Insider Long/Short Fund suffered monthly losses of 5.62% in February, which brought its one-year return through the end of the month to -3.21%. This was good enough to rank in the top quarter of the category, but not good enough to convince investors to stick with the fund – it suffered outflows of more than $7 million for the year. Perhaps investors looked past its attractive 1.47% alpha to its 17.33% annualized volatility, which ranked fourth out of the category’s 142 funds with one-year track records. Finally, the Caldwell & Orkin fund was the month’s third-worst performer with losses of 4.88%, but the fund ranked in the top 7% of its category based on its one-year returns of +1.38%. This was undoubtedly one of the reasons it received a whopping $87.47 million in net inflows for the year. Its one-year beta (-0.07), alpha (+1.26%), Sharpe ratio (0.19), and volatility (8.75%) were all attractive relative to the category averages, too. Past performance does not necessarily predict future results. Jason Seagraves contributed to this article. MPT statistics (alpha and beta) are relative to the S&P 500 Index.

Simulated Backtests May Not Be Realistic For Volatility ETNs

The volatility ETNs VelocityShares Daily Inverse VIX Short-Term ETN (NASDAQ: XIV ) and iPath S&P 500 VIX ST Futures ETN (NYSEARCA: VXX ) have attracted much interest. Since these products provide the greatest value when used in conjunction with trading strategies that seek to avoid large drawdowns, numerous strategies have been developed. However, both ETNs were brought into existence after the most recent major crisis in 2009. That means they’ve only been through part of the full volatility cycle that runs through boom, bust, and recovery. It’s therefore desirable to backtest these trading strategies against longer time periods to include a diversity of conditions. Two data sources are available for simulating ETN performance beyond their lifetimes: 1) the index these ETNs track , which goes back to 1/31/2006, and 2) the VIX futures on which this index is based and for which there is data going back to their introduction in 2004. It’s become common practice for anyone presenting a trading strategy based on these ETNs to demonstrate and compare their strategy against other strategies using one or both of these data sources. The indexes are generally considered a safe substitute for these ETNs when backtesting and comparing trading strategies because 1) the ETN managers have an obligation to track these indexes and have set up safeguards to correct tracking errors, and 2) various people have found that the ETNs track their indexes fairly well (within a few percent) year over year. Figures 1 and 2 show the ETNs over their full lifetime with their indexes. VXX looks pretty good. XIV obviously has some drift. Figure 1. VXX tracking its index. Figure 2. XIV tracking its index. Despite its longterm drift, XIV tracks reasonably well on a yearly basis, generally slipping 0.5 to 3%, although sometimes slipping 4-6%. Monthly, XIV does better yet, slipping less than 4%. Weekly slippage for XIV is also below 4%. VXX, as you might guess, does better on a yearly and monthly basis. However, on a week-to-week basis, VXX actually sometimes sees wider swings than XIV, slipping as much as 5-6%. However, where things get really interesting is with the daily slippages for XIV and VXX. Figures 3 and 4 show what these look like. Click to enlarge Figure 3. VXX daily slippage by percent index change. Click to enlarge Figure 4. XIV daily slippage by percent index change. As you can see in Figures 3 and 4, there’s a distinctive non-linearity when the index change is large in magnitude. Above 7.5% and below -7.5%, the ETNs tend to compress their index. The other thing that happens is the range of slippage becomes generally wider at these extremes. The range of slippage is quite wide in several other bins as well. This wide range of scatter has the potential to be a significant problem when backtesting with simulated data. Imagine if 10% increases were handed out from time to time to some strategies and not to others. That would clearly skew the results! However, we also see that the distributions are fairly well balanced. While there’s a lot of scatter, it’s spread around in both negative and positive directions. So is this a problem or not? We can’t answer that from just these charts. It looks like there’s a possibility of problems, but there’s also a re-assuring symmetry in these tracking errors that might cancel out. The only way to find out is to test. I did that by running backtests of several common trading strategies on both the ETNs and their index within the time since the ETNs began. Let me note up front that there are some peculiarities to backtesting with the index. It has no open and close prices, just a daily number from settle to settle. To match that as closely as possible using the ETNs, I buy and sell only the close. I also use that day’s trading signal as the decision rule for opening and closing positions that same day. This roughly simulates buying and selling at the close based on a signal that fires just before the close. In this way, I use end-of-day data for both the ETN and the index, which should help make the tests more comparable. The trading strategies I backtested are Vratio Vratio10 CB_10_9_-8_-7 CB_5_2_-8_-7 CB_5_2_-5_-4 Vratio and Vratio10 are from Tony Cooper’s Easy Volatility Investing . The CB strategies are contango-backwardation with four thresholds: XIV-Buy, XIV-Sell, VXX-Buy, and VXX-sell. I picked these strategies because I think they are widely used. The first CB strategy is what got me started investigating slippage. A commenter asked about using a high threshold for contango as a conservative buy indicator. He proposed 10%, but didn’t supply any further thresholds, so I picked three more in the same conservative spirit and ran a backtest. It gave a decent return. I thought it would be a good idea to backtest over a longer timeframe, so I set up to test with the index, checked my setup by backtesting with the index over the same timeframe as the ETN…and well, you’ll see what happened! The third CB strategy uses thresholds that I already knew would probably do fairly well, and the one between is a hybrid. All backtests ran from 03-Jan-2011 to 12-Feb-2016. Let’s get straight to the results: Strategy ETN Gain Index Gain Net Slippage Backtesting with ETN v with Index Vratio 259.1% 238.6% 6.1% Vratio10 312.3% 299.3% 3.3% CB_10_9_-8_-7 170.1% 64.2% 64% CB_5_2_-8_-7 227.8% 84.2% 78% CB_5_2_-5_-4 328.8% 162.0% 64% With the two Vratio tests, we see a small amount of slippage, consistent with the expectation that positive and negative slippages would likely cancel out. But the three contango-backwardation backtests had extremely large net slippages. So much so that while its index performance puts CB_5_2_-5_-4 in the middle of the pack for net gains, tracking errors moved it to first place in the ETN results! If these results are correct (and I’d encourage readers to check me on this since it’s quite surprising), we must accept that backtesting with index data is not a good proxy for ETN performance — at least for some trading strategies. Is there anything we can do to get around this problem? One possibility is to check each strategy for excessive slippage in the overlapped period when both ETN and index are available. If slippage is mild, that suggests the strategy is evenly distributing the conditions that give positive and negative tracking errors, and may be more reliably backtested over a longer period. With strategies that do show bias, a deeper analysis may make it possible to adjust for that bias. On a related note, if short-lived tracking errors can make this much difference, it would be helpful to occasionally re-evaluate the slippage effects of strategies one uses, to see if they’ve changed. Finally, it’s my opinion that futures data prior to 2006Q3 is not valid for backtesting these ETNs in any case. The reason is that M1 and M2 are not consistently present in the futures prior to that time. Since, even with M1 and M2 data, it’s questionable whether we can do a meaningful backtest, the substantial additional uncertainty of missing futures data is surely over-reach. Notes Definition of “slippage” as used in this article: I define a change in the ETN as the product of the change in the index and the change due to slippage: (1+P) = (1+I)*(1+s) Where P is the gain/loss rate of the ETN over some time T, I is the gain/loss rate of the Index over time T, and s is the slippage factor over time T. Rearranging this definition, slippage is calculated as s = ((1+P)/(1+I))-1. Does the ProShares Short VIX Short-Term Futures ETF ( SVXY) have less slippage than XIV? While I did not backtest with SVXY, I did plot its daily slippages, and they’re very similar to XIV. Disclosure: I/we have no positions in any stocks mentioned, but may initiate a long position in EITHER XIV OR VXX over the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

Cheap Funds Dupe Investors – Q1 2016

Fund holdings affect fund performance more than fees or past performance. A cheap fund is not necessarily a good fund. A fund that has done well in the past is not likely to do well in the future ( e.g. 5-star kiss of death and active management has long history of underperformance ). Yet, traditional fund research focuses only on low fees and past performance. Our research on holdings enables investors to find funds with high quality holdings – AND – low fees. Investors are good at picking cheap funds. We want them to be better at picking funds with good stocks. Both are required to maximize success. We make this easy with our predictive fund ratings. A fund’s predictive rating is based on its holdings, its total costs, and how it ranks when compared to the rest of the 7000+ ETFs and mutual funds we cover. Figure 1 shows that 70% of fund assets are in ETFs and mutual funds with low costs but only 1% of assets are in ETFs and mutual funds with Attractive holdings. This discrepancy is astounding. Figure 1: Allocation of Fund Assets By Holdings Quality and By Costs Sources: New Constructs, LLC and company filings Two key shortcomings in the ETF and mutual fund industry cause this large discrepancy: A lack of research into the quality of holdings. A lack of high-quality holdings or good stocks. With about twice as many funds as stocks in the market, there simply are not enough good stocks to fill all the funds. These shortcomings are related. If investors had more insight into the quality of funds’ holdings, we think they would allocate a lot less money to funds with poor quality holdings. Many funds would cease to exist. Investors deserve research on the quality of stocks held by ETFs and mutual funds. Quality of holdings is the single most important factor in determining an ETF or mutual fund’s future performance. No matter how low the costs, if the ETF or mutual fund holds bad stocks, performance will be poor. Costs are easier to find but research on the quality of holdings is almost non-existent. Figure 2 shows investors are not putting enough money into ETFs and mutual funds with high-quality holdings. Only 78 out of 7421 (1% of assets) ETFs and mutual funds allocate a significant amount of value to quality holdings. 99% of assets are in funds that do not justify their costs and over charge investors for poor portfolio management. Figure 2: Distribution of ETFs & Mutual Funds (Count & Assets) By Portfolio Management Rating Click to enlarge Source: New Constructs, LLC and company filings Figure 3 shows that Investors successfully find low-cost funds. 70% of assets are held in ETFs and mutual funds that have Attractive-or-better rated total annual costs , our apples-to-apples measure of the all-in cost of investing in any given fund. Out of the 7421 ETFs and mutual funds we cover, 1664 (70%) earn an Attractive-or-better total annual costs rating. Clearly, ETF and mutual funds investors are smart shoppers when it comes to finding cheap investments. But cheap is not necessarily good. The Nationwide Portfolio Completion Fund (MUTF: NAAIX ) gets an overall predictive rating of Very Dangerous because no matter how low its fees (0.62%), we expect it to underperform because it holds too many Dangerous-or-worse rated stocks. Low fees cannot boost fund performance. Only good stocks can boost performance. Figure 3: Distribution of ETFs & Mutual Funds (Count & Assets) By Total Annual Costs Ratings Click to enlarge Source: New Constructs, LLC and company filings Investors should allocate their capital to funds with both high-quality holdings and low costs because those are the funds that offer investors the best performance potential. But they do not. Not even close. Figure 4 shows that less than half (49%) of ETF and mutual fund assets are allocated to funds with low costs and high-quality holdings according to our predictive fund ratings, which are based on the quality of holdings and the all-in costs to investors. Figure 4: Distribution of ETFs & Mutual Funds (Count & Assets) By Predictive Ratings Click to enlarge Source: New Constructs, LLC and company filings Investors deserve forward-looking ETF and mutual fund research that assesses both costs and quality of holdings. For example, the Market Vectors Semiconductor ETF (NYSEARCA: SMH ) has both low costs and quality holdings. Why is the most popular fund rating system based on backward-looking past performance? We do not know, but we do know that the transparency into the quality of portfolio management provides cover for the ETF and mutual fund industry to continue to over charge investors for poor portfolio management. How else could they get away with selling so many Dangerous-or-worse ETFs and mutual funds? John Bogle is correct – investors should not pay high fees for active portfolio management. His index funds have provided investors with many low-cost alternatives to actively managed funds. However, by focusing entirely on costs, he overlooks the primary driver of fund performance: the stocks held by funds. Investors also need to beware certain Index Label Myths . Research on the quality of portfolio management of funds empowers investors to make better investment decisions. Investors should no longer pay for poor portfolio management. D isclosure: David Trainer and Kyle Guske II receive no compensation to write about any specific stock, sector or theme. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.