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UnitedHealth Solid Q1 Earnings Put These ETFs In Focus

The largest U.S. health insurer, UnitedHealth Group (NYSE: UNH ), reported solid first-quarter 2016 results. The company continued its long streak of earnings beats. Earnings per share came in at $1.81, surpassing the Zacks Consensus Estimate by 9 cents and the year-ago earnings by 17%. Revenues rose 25% year over year to $44.5 billion, broadly in line with the Zacks Consensus Estimate of $44.7 billion. The company reported medical care ratio of 81.7%, up 30 basis points year over year, thanks to the extra calendar day of service in the quarter. Growth was broad based, with a 54% increase in revenues for Optum, the health services business (see all the Healthcare ETFs here ). Based on solid first-quarter results and business trends, UnitedHealth raised its earnings guidance to $7.75-7.90 per share for 2016 from $7.60-7.80 per share projected earlier. The Zacks Consensus Estimate of $7.85 per share is within the guided range. The company expects revenues to be approximately $182 billion in 2016, which is in line with the current Zacks Consensus Estimate. As a result, the stock jumped 4.8% in the last two trading days (as of April 20, 2016), following the earnings announcement. The stock currently has a Zacks Rank #2 (Buy) with a Value Style Score of “A”. This underscores its potential to outperform in the weeks ahead. In its conference call, UnitedHealth stated that it would pull out of the majority of public exchanges owing to smaller overall market size and a higher risk profile within this market segment. Next year, the company plans to remain in only a few of the states and will not carry any financial exposure from the exchanges into 2017. ETFs in Focus Investors may want to take a closer look at the ETFs having the largest allocation to this health insurance giant, as UNH has shown encouraging trading following its earnings. For those, the iShares U.S. Healthcare Providers ETF (NYSEARCA: IHF ) could especially be on their radar, as UNH takes the top spot in the fund’s portfolio at 12.9% share. IHF This ETF provides exposure to 49 companies offering health insurance, diagnostics and specialized treatment by tracking the Dow Jones U.S. Select Healthcare Providers Index. About 45% of the portfolio is dominated by managed care firms, while healthcare services (26.5%) and healthcare facilities (23.3%) round off the top three. The fund has amassed $709.6 million in its asset base, while volume is good at about 112,000 shares per day, on average. It charges 44 bps in annual fees and expenses, and added 1.9% in the last two trading days following the UNH earnings release (as of April 20, 2016). The product has a Zacks ETF Rank of 1 or “Strong Buy” rating with a Medium risk outlook. Other ETFs Other healthcare ETFs, like the Health Care Select Sector SPDR ETF (NYSEARCA: XLV ) – 4.6%, the iShares U.S. Healthcare ETF (NYSEARCA: IYH ) – 4.3%, the PowerShares DWA Healthcare Momentum Portfolio ETF (NYSEARCA: PTH ) – 3.8%, the Fidelity MSCI Health Care Index ETF (NYSEARCA: FHLC ) – 3.9% and the Vanguard Health Care ETF (NYSEARCA: VHT ) – 4.1%, also have a decent exposure to UnitedHealth. Apart from the healthcare space, UNH is among the top 10 holdings in some large cap ETFs, such as the SPDR Dow Jones Industrial Average ETF (NYSEARCA: DIA ) and the PowerShares Dynamic Large Cap Growth Portfolio ETF (NYSEARCA: PWB ), with exposure of 4.9% and 3.4%, respectively. However, these products will be less impacted by the movement of UNH share price. Original Post

A Better Way To Run Bootstrap Return Tests: Block Resampling

Developing confidence about a portfolio strategy’s track record (or throwing it onto the garbage heap), whether it’s your own design or a third party’s model, is a tricky but essential chore. There’s no single solution, but a critical piece of the analysis for estimating return and risk, including the potential for drawdowns and fat tails , is generating synthetic performance histories with a process called bootstrapping. The idea is based on simulating returns by drawing on actual results to see thousands of alternative histories to consider how the future may unfold. The dirty little secret in this corner of Monte Carlo analysis is that there’s more than one way to execute bootstrapping tests. To cut to the chase, block bootstrapping is a superior methodology for asset pricing because it factors in the reality that market returns exhibit autocorrelation. The bias for momentum – positive and negative – in the short run, in other words, can’t be ignored, as it is in standard bootstrapping. There’s a tendency for gains and losses to persist – bear and bull markets are the obvious examples, although shorter, less extreme runs of persistence also mark the historical record as well. Conventional bootstrapping ignores this fact by effectively assuming that returns are independently distributed. They’re not, which is old news. The empirical literature demonstrates rather convincingly a strong bias for autocorrelation in asset returns. Designing a robust bootstrapping test on historical performance demands that we integrate autocorrelation into the number crunching to minimize the potential for generating misleading results. The key point is recognizing that sampling historical returns for analysis should focus on multiple periods. Let’s assume that we’re looking at monthly performance data. A standard bootstrap would reshuffle the sequence of actual results and generate alternative return histories – randomly, based on monthly returns in isolation from one another. That would be fine if asset returns weren’t highly correlated in the short run. But as we know, positive and negative returns tend to persist for a stretch, sometimes in the extreme. The solution is sampling actual histories in blocks of time (in this case several months) to preserve the autocorrelation bias. The question is how to choose the length for the blocks, along with some other parameters. Much depends on the historical record, the frequency of the data, and the mandate for the analysis. There’s a fair amount of nuance here. Fortunately, R offers several practical solutions, including the meboot package (“Maximum Entropy Bootstrap for Time Series”). As an illustration, let’s use a couple of graphics to compare a standard bootstrap to a block bootstrap, based on monthly returns for the US stock market (S&P 500). To make this illustration clear in the charts, we’ll ignore the basic rules of bootstrapping and focus on a ridiculously short period: the 12 months through March 2016. If this was an actual test, I’d crunch the numbers as far back as history allows, which runs across decades. I’m also generating only ten synthetic return histories; in practice, it’s prudent to create thousands of data sets. But let’s dispense with common sense in exchange for an illustrative example. The first graph below reflects a standard bootstrap – resampling the historical record with replacement. The actual monthly returns for the S&P (red line) are shown in context with the resampled returns (light blue lines). As you can see, the resampled performances represent a random mix of results via reshuffling the sequence of actual monthly returns. The problem is that the tendency for autocorrelation is severed in this methodology. In other words, the bootstrap sample is too random – the returns are independent from one another. In reality, that’s not an accurate description of market behavior. The bottom line: modeling history through this lens could, and probably will, lead us astray as to what could happen in the future. Let’s now turn to block bootstrapping for a more realistic profile of market history. Note that the meboot package does most of the hard work here in choosing the length of the blocks. The details on the algorithm are outlined in the vignette. For now, let’s just look at the results. As you can see in the second chart below, the resampled returns resemble the actual performance history. It’s obvious that the synthetic performances aren’t perfectly random. Depending on the market under scrutiny and the goal of the analytics, we can adjust the degree of randomness. The key point is that we have a set of synthetic returns that are similar to, but don’t quite match, the actual data set. Note that no amount of financial engineering can completely wipe away uncertainty. The future can and probably will deliver surprises, for good and ill, no matter how clever our analytics. Nonetheless, bootstrapping historical data (or in-sample returns via backtests) can help separate the wheat from the chaff when looking into the rearview mirror as a preview of what lies ahead. But the details on how you run a bootstrap test are critical for developing comparatively high-confidence test results. In short, we can’t ignore a simple fact: market returns have an annoying habit of exhibiting non-random behavior.

Lack Of Earnings Quality And Debt Downgrades Limit S&P 500’s Upside

Four in a row. That’s how many consecutive 3-point baskets Andre Iguodala scored against the Houston Rockets in last night’s playoff game. There has also been a “4 for 4″ in the financial markets. One after another, major banks have lowered their year-end targets for the S&P 500. Most recently, the global equity team at HSBC shaved its year-end target to 2,050 from 2,100. On the surface, HSBC’s cut is less severe than other bank revisions to S&P 500 estimates. That said, J.P Morgan pulled its projection all the way down from 2200 to 2000. Credit Suisse? Down to 2,050 from 2,200. And Morgan Stanley slashed its year-end projection from 2175 to 2050. So what’s going on? We had four influential banks expressing confidence in the popular benchmark a few months earlier. Their analysts originally projected total returns with reinvested dividends between 5%-10% in the present 12-month period. Now, however, with the S&P 500 only expected to finish between 2000-2050, these banks see the index offering a paltry 0%-2%. Another way some have phrased it? Excluding dividends, there is “zero upside.” Here is yet another “4 for 4” that makes a number of analysts uncomfortable. Year-over-year quarterly earnings have fallen four consecutive times. That has not happened since the Great Recession. And revenue? Corporations have put forward year-over-year declines in sales growth for five consecutive quarters. That hasn’t happened since the Great Recession either. The bullish investor case is that the trend is going to start reversing itself in the 2nd half of 2016. However, forward estimates of earnings growth and revenue growth are routinely lowered so that two-thirds or more companies can surpass “expectations.” And it is not unusual for estimates to be lowered by 10%. Take Q1. Shortly before the start of the year, Q1 estimates had been forecast to come in at a mild gain. Today? We’re looking at -9% or worse for Q1. Over the previous five years, Forward P/Es averaged 14.5. They now average 16.5 on earning estimates that will never be realized. In essence, S&P 500 stock prices are sitting a softball’s throw away from an all-time record (2130), while the forward P/E valuations sit at bull market extremes that do not justify additional appreciation in price. And what about earnings quality? Wall Street typically presents two kinds: Generally Accepted Accounting Principles (GAAP) earnings and non-GAAP earnings that excludes special items, non-recurring expenses and a wide variety on “one-time charges.” The foolishness of non-GAAP presentations notwithstanding, one might disregard the manipulation when non-GAAP and GAAP are within the usual 10% range. This was more or less the case between 2009 and 2013. By 2014, however, the gap between the two different earnings per share reports began to widen. By 2015, “manipulated” pro forma ex-items earnings exceeded actual earnings per share by roughly $250 billion, or 32%. Can you spell c-h-i-c-a-n-e-r-y? Of particular interest, there was a similar disconnect between GAAP and non-GAAP in 2007. Non-GAAP in the year when the last bear market began (10/07) was 24% higher than GAAP earnings per share. It follows that the discrepancy today in earnings quality is even wider than it was prior to the stock market collapse. “But Gary,” you protest. “As long as the Federal Reserve and central banks are exceptionally accommodating, stocks should excel.” In truth, however, the long-term relationship between the SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) and the Vanguard Total Bond Market ETF (NYSEARCA: BND ) demonstrate that the bond component of one’s portfolio has been more productive over the last 12 months than the stock component. Bulls can point to the market’s eventual ability to shake off the euro-zone crisis of 2011. That was the last time that the SPY:BND price ratio struggled for an extended length of time. Back then, however, the Federal Reserve offered two aggressive easing policies – “Operation Twist” and “QE 3.” Today? Stocks are not only extremely overvalued on most historical measures, but the Fed has only lowered its tightening guidance from four hikes down to two hikes. Is that really enough ammunition to power stocks to remarkable new heights? “Okay,” you acknowledge. “But rates are so low, they are even lower than they were in 2013. And that means, going forward, there is no alternative to stocks.” Not only does history dispel the myth that there are no alternatives to stocks , but many corporations that have been buying back their stocks at attractive borrowing costs are now at risk of debt downgrades, higher interest expenses and even default. For example, the moving 12-month sum of Moody’s debt downgrades hopped from 32 a year ago to 61 in March of 2016. Meanwhile, the longer-term trend for the widening of credit spreads between investment grade treasuries in the iShares 7-10 Year Treasury Bond ETF (NYSEARCA: IEF ) and high yield bonds in the iShares iBoxx $ High Yield Corporate Bond ETF (NYSEARCA: HYG ) suggest that the corporate debt binge may soon come to an ignominious end. Foreign stocks, emerging market stocks as well as high yield bonds all hit their cyclical tops in mid-2014, when the credit spreads were remarkably narrow. The IEF:HYG price ratio spikes and breakdowns notwithstanding, the general trend for 18-plus months has been less favorable to lower-rated corporate borrowers. The implication? With corporate credit conditions worsening at the fastest pace since the financial crisis , companies may be forced to slow or abandon stock share buybacks. What group of buyers will pick up the slack when valuation extremes meet fewer stock buybacks? Click here for Gary’s latest podcast. Disclosure: Gary Gordon, MS, CFP is the president of Pacific Park Financial, Inc., a Registered Investment Adviser with the SEC. Gary Gordon, Pacific Park Financial, Inc, and/or its clients may hold positions in the ETFs, mutual funds, and/or any investment asset mentioned above. The commentary does not constitute individualized investment advice. The opinions offered herein are not personalized recommendations to buy, sell or hold securities. At times, issuers of exchange-traded products compensate Pacific Park Financial, Inc. or its subsidiaries for advertising at the ETF Expert web site. ETF Expert content is created independently of any advertising relationships.