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3 Healthcare Funds To Buy On Biotech Rebound

After being beaten down during the first three months of the year, biotech stocks made a remarkable rebound over the past few days. Though the iShares Nasdaq Biotechnology ETF (NASDAQ: IBB ) is still down 17% in the year-to-date frame, it posted an increase of 5.9% on Wednesday, witnessing the best percentage gain since March 12, 2009. In fact, the 7.9% rise in IBB over the past one-month period also propelled healthcare mutual funds, which gained 2.9% during the same period. Mutual funds from this category may be profitable for investors, who are looking to gain from this encouraging trend. Reasons for the Recent Surge Strong gains of 5% and 3.5% respectively in Pfizer Inc. (NYSE: PFE ) and Allergan plc (NYSE: AGN ) played an important role in lifting biotech stocks on Wednesday. The increase was prompted when the companies mutually called off their merger after tougher tax inversion rules were imposed by the U.S. Treasury Department and Internal Revenue Service. Leaving the deal behind, Allergan CEO Brent Saunders said that the company, “could act immediately if” it gets “the right opportunity with the right growth profile and the right strategic logic.” Meanwhile, it is now speculated that names of other UK-based firms like GlaxoSmithKline plc (NYSE: GSK ) are on Pfizer’s radar. Moreover, a surge of nearly 17% in shares of Edwards Lifesciences Corp. (NYSE: EW ) gave a boost to this sector. According to the company, data from the trial revealed that a procedure which uses its SAPIEN 3 valve shows better results than open heart procedures for certain patients. What’s Ahead? In spite of the recent surge, some of the concerns that affected the performance of biotech stocks at the start of 2016 may continue to impact the sector in the near future. Calls for reducing the prices of several drugs had played an important role in dragging down the sector. Hillary Clinton’s comments on the prohibitive pricing of certain medications drew much attention last year, weighing down on the sector’s stocks. Moreover, the U.S. Treasury Department’s adaptation of new rules to contain inversion-related deals may lower the volume of overseas merger and acquisition deals in the near term. Moreover, mixed earnings results during the fourth quarter affected the sector to quite an extent. Also, continued decline in the first-quarter earnings forecast is likely to hurt the sector’s performance in the days ahead. First-quarter earnings from the healthcare sector are anticipated to grow only 0.6% from the year-ago level compared with 9.3% growth witnessed in the previous quarter. Moreover, the year-on-year revenue growth rate is projected to decline to 8.8%, lower than the fourth quarter’s growth pace of 9.7%. However, an innovative product pipeline, product approvals and impressive performances by key products may act as growth catalysts and help the sector to overcome the above-mentioned concerns. Moreover, favorable valuation can make smaller companies within the sector attractive bets for acquisition. Separately, positive results from clinical trials also lift the sector’s stocks. They are difficult to predict, but come as welcome surprises for investors. 3 Healthcare Funds Picks Given this strong recovery, we have highlighted three healthcare mutual funds that either have a Zacks Mutual Fund Rank #1 (Strong Buy) or #2 (Buy). We expect these funds to outperform their peers in the future. Remember, the goal of the Zacks Mutual Fund Rank is to guide investors to identify potential winners and losers. Unlike most of the fund-rating systems, the Zacks Mutual Fund Rank is not just focused on past performance, but also on the likely future success of the fund. These funds have encouraging one-month and three-year annualized returns. The minimum initial investment is within $5000. Also, these funds have a low expense ratio and no sales load. Delaware Healthcare Fund I (MUTF: DLHIX ) invests a large chunk of its assets in equity securities of companies that are engaged in operations such as production, development and of products and services related to healthcare sector. DLHIX is a non-diversified fund. Along with a Zacks Mutual Fund Rank #1, DLHIX has one-month and three-year annualized returns of 5.9% and 16.8%, respectively. Annual expense ratio of 1.11% is lower than the category average of 1.35%. Fidelity Select Biotechnology Portfolio (MUTF: FBIOX ) seeks growth of capital. FBIOX invests the lion’s share of its assets in companies primarily involved in the research, development, manufacture, and distribution of various biotechnological products. The fund invests in securities of companies throughout the globe. Along with a Zacks Mutual Fund Rank #2, FBIOX has one-month and three-year annualized returns of 5.1% and 16.8%, respectively. Annual expense ratio of 0.72% is lower than the category average of 1.35%. Live Oak Health Sciences Fund (MUTF: LOGSX ) invests the majority of its assets in common stocks of healthcare companies or those related to medicine and life sciences. Though LOGSX primarily focuses on acquiring domestic securities, it may allocate a small portion of its assets in securities of foreign firms and ADRs. Along with a Zacks Mutual Fund Rank #2, LOGSX has one-month and three-year annualized returns of 3.9% and 15.9%, respectively. Annual expense ratio of 1.08% is lower than the category average of 1.35%. Link to the original post on Zacks.com

Testing Asset Allocation Results With Random Market Selection

Skill is a slippery concept in finance, courtesy of the shady influence of chance in asset pricing. It’s also an awkward topic in just about every corner of money management because discussing it in detail invariably raises serious doubts about our ability to engineer investment results that are satisfactory much less stellar. But ignored or not, randomness is a factor and perhaps a far more powerful one than generally assumed. In recent posts I’ve explored several facets of how random market behavior can influence portfolio results. In the first installment on the topic we focused on random rebalancing dates. Then we moved on to the results via randomly changing asset weights in asset allocation. Let’s push this testing a step further and build portfolios by randomly selecting asset classes. As before, I’ll use the same 11-fund portfolio that’s globally diversified across key asset classes with a starting date of Dec. 31, 2003. The benchmark strategy is rebalancing the portfolio at the end of each year back to the initial weights, as defined in the table below. Let’s call this our “reasonable” attempt at building an informed asset allocation strategy. For comparison with the element of chance in market pricing, this time the test consists of randomly selecting combinations of asset classes with equal weighting that rebalances the mix back to equal weights every Dec. 31. Note that there are 11 funds in the table above. To test for randomness I’ll use R’s number-crunching prowess to select 1,000 different asset allocation mixes. For instance, one randomly selected portfolio may hold US stocks, US REITs, and commodities and ignore everything else. Another portfolio may hold everything with the lone exception of US junk bonds. (For those who’re interested in the details, I’m selecting time series data via the sample() command with no replacement.) All random portfolios are created as equal weight strategies (if there’s more than one fund) using a start date of Dec. 31, 2003, with results running through yesterday’s close (Apr. 6). The chart below compares the benchmark portfolio (red line) with 1,000 random portfolios as defined above. As you can see, there’s a wide range of outcomes relative to the benchmark portfolio, which increased from 100 to roughly 211 over the test period–i.e., the portfolio more than doubled. By contrast, the best-performing random portfolio surged to more than 300 while the worst performer collapsed to just under 50. Most of the random portfolios, however, dispensed moderately superior or inferior results relative to the benchmark. Let’s review the same data from another perspective by comparing the ending value of the benchmark portfolio (red line) for the sample period with the distribution of ending values for the 1,000 randomly generated strategies (black line). Note that the median outcome for the random portfolios is also included in the chart below (blue line). This is only a toy example, of course, but the results imply that we should be cautious in assigning skill as a key factor for the results of the benchmark portfolio. Dumb luck seems to have played a role too. But let’s not beat ourselves up too much. We can almost certainly avoid the fate of the worst performer among the random strategies by holding a broad set of asset classes. The probability is quite low that everything will fail at the same time, although the events of 2008 pushed that notion to the limit and left more than a few investors with doubts. In any case, the main takeaway is that randomness in market behavior is a factor, and perhaps a dominant one, when it comes to risk and return in the context of portfolio design. That doesn’t mean we should throw up our hands and assume that we have no control over investment outcomes. Rather, the lesson is that a fair amount of what appears to be skill may be something else. In other words, our wetware has a tendency to be confused by randomness–a confusion that we’re all too often eager to facilitate, perhaps unconsciously. Chance can’t be engineered out of the investing process, at least not entirely, but that’s only a minor issue if we’re prepared to deal with this gremlin. The intelligent response is to understand how randomness can influence risk and return and factor that aspect of market behavior into asset allocation analysis and design. Yes, many are fooled by randomness, but that doesn’t have to be every investor’s fate.

Policy Divergence And Investor Implications

By Mark Harrison, CFA The world’s central banks and treasuries are no longer simply balancing the levers of growth and inflation through a succession of cycles with varying degrees of poise. Karin Kimbrough, a macro-economist at Bank of America Merrill Lynch, explores a world where all the old symmetries of monetary and fiscal policy have evaporated – that era might as well be 100 years ago. Instead, according to Kimbrough, who spoke at the CFA Institute Fixed-Income Managed Conference in Boston, in this new era, central banks are far from scoring top grades. In the United States, the US Federal Reserve’s quantitative easing (QE) trade is beginning to unwind, but QE policies are still underway in other developed economies. There is monetary and fiscal policy divergence, which, together with demographic distinctions among advanced economies, has important implications for interest rates and fixed-income markets. In this question & answer session following Kimbrough’s presentation, concerns are raised about this policy divergence, difficulties controlling inflation, portfolio risk concentrations from investor yield-chasing, and perceived foreign threats. A full version of this presentation is also available in the CFA Institute Conference Proceedings Quarterly . Audience member: What do you think about the concept of good versus bad inflation? Karin Kimbrough: I personally do not ascribe too much value to the good versus bad inflation concept. I think that the good versus bad inflation argument really just reflects where we see growth and demand more tightly. We are making more advances on the consumer side. Growth is looking okay, and services are definitely stronger than manufacturing, so we are seeing more inflation in services. Any sort of price pressure from abroad is just completely disinflationary given the strengthened dollar and the many downward pressures abroad in commodities and import prices. You mention that fiscal policy is not living up to its end of the bargain. What are some policies that you would espouse to help bridge the gap? I am a Keynesian at heart, in the sense that Keynesian is shorthand for correcting deficient demand. I believe that, in the presence of a deep lack of aggregate demand, the government should step in and support it. So, as a Keynesian economist, I would have supported some kind of new deal deploying people who are still unemployed to work on a major infrastructure project. It might be a redo of some of our major highways or getting high-speed internet into more rural areas – some long-term infrastructure investment that would actually pay off in dividends in the long term for the United States in terms of productivity, either through transportation or communication. So, I would have liked to have seen highway bills and infrastructure bills or, as a New Yorker, another tunnel between New Jersey and New York. All of that got delayed because it was deemed too expensive, but I cannot think of a better time to do it than when rates are low and there is a lot of labor to deploy. Yes, it is expensive, but it also puts people back to work. When people are back at work, they are paying taxes, paying their mortgage, and shopping, and businesses make plans and invest. When you grade inflation a D+, you grade it against the Fed’s target of 2%. How do we know 2% is the right number? If 2% is not the right number, what might the right number be, and how would it affect your grade for inflation? I graded it based on the test, and the test was 2%. Should the test be different – say, 1.5%? Maybe. I think of it this way: 2% provides a nice, comfortable margin such that the Fed is not setting a target that is so low that it is constantly flirting with deflation, which is generally a nightmare for central banks. No central bank wants to be constantly resorting to QE and asset purchases. The Fed wants to be able to toggle the pace of our economy using rates, which is hard to do when everything is sitting so close to the zero lower bound. A 1% inflation target would mean that, over the medium term, actual inflation is oscillating at a very low level, which is problematic. The Fed is trying to set inflation expectations that give the central bank ample room to respond without constantly facing a threat of either destabilizing high inflation or managing problematic deflation. No one is quantitatively arguing the Fed get to 2% more robustly than historical behavior. If 2% is just a random number and is not achievable, does it force the Fed to implement policies that might create other risks, such as the systemic risks that come out of an attempt to create something that is not possible? Central banks look at a variety of measures when deciding on policy, and of course, not all measures point in the same direction. For example, the personal consumption expenditure (PCE) index presents a more negative case right now compared with consumer price index (CPI) inflation, which is sitting at 1.7-1.8% – a lot closer to 2%. It depends on how something is measured, and of course, governments and central banks are guilty of occasionally changing their standards. They will give good reasons for changes – for example, they might say, “We think we need to reweight medical costs or housing costs differently” – and they will come up with a different measure of inflation. If 2% is indeed unachievable and we are constantly trying to drive ourselves there, then perhaps we are doing it at the expense of inflating asset price bubbles by keeping rates unusually low. I think about it from a central bank perspective: There are risks of financial instability resulting from inflated asset prices. These risks are worsened when leverage is added into the mix. Right now, I do not think we are at a particularly over-levered position relative to a decade ago. So, the Fed might be willing to tolerate some degree of overvaluation in certain markets, because the leverage does not look like it is there. That said, if leverage were building up, I would be a lot more worried about trying to achieve an unachievable target of 2%. 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.