Author Archives: Scalper1

FBT Was +47.55% In 2014 And +10.00% YTD. Will The Returns Continue In 2016?

Summary This established Biotech ETF has an interesting structure but also is quite volatile. With $3.28BLN in assets, will the institutions continue to invest in 2016? We answer these questions and provide our recommendation on this top performer. The First Trust NYSE Arca Biotechnology Index ETF (NYSEARCA: FBT ) is an equal weighted passively managed fund with an established track record, (inception 06/19/2006). The fund seeks to replicate as close possible, before fees and expenses, the price and yield of the NYSE Arca Biotechnology Index, (previously the Amex Biotechnology Index). The interesting structure of the ETF is the 30 components, (previously 20 components prior to October 20, 2014). What is challenging for shareholders is the quarterly rebalancings that occur in late January, April, July and October. Due to the equal weighting objective of the Fund and the underlying Index and the general small to mid-cap nature of the sector, these rebalancings and the ETF, in general, can be volatile. We will analyze the structure of the ETF, its holdings, performance and fees and provide our recommendation. 100% of the ETF is in common equity holdings. Our Market Cap is quite simple, with most of our sources agreeing: FBT Market Capitalization Market Cap Weight Mid cap 34.98% Small cap 33.10% Large cap 31.98% These numbers were courtesy of Fidelity, with xtf.com extremely close in agreement. Morningstar, as we previously noted uses a slightly difference nomenclature. Their breakdown is: Medium at 40.56%, Small at 27.19%, Large at 23.12%, and Giant at 9.12%. Categorically we can state that the majority of the firms in this ETF are small to mid-cap firms with limited products presently, if any, in the marketplace. In terms of the style of the underlying components, it is quite clear to investors who have participated in this space. FBT Ownership Style Style Weight Growth 59.80% Pure Growth 30.00% Blend 7.10% Value 3.10% Without a doubt this ETF is a growth vehicle and not intended for those seeking value investments. Morningstar states that the ownership style is mid or medium and is considered high growth. In terms of currency and countries of the holdings it is somewhat interesting. FBT Country and Currency Exposure Country Weight Currency Weight United States 89.90% United States dollar 89.90% Ireland 3.68% Euro 10.10% Spain 3.23% NA NA Netherlands 3.19% NA NA Our country and currency exposure here is clearly US geographically focused with some Eurozone exposure as noted. The 10.10% euro weighting will not adversely impact this ETF even with the euro possibly moving below dollar-euro parity. As such, we have no issues with the underlying geographical or currency weightings. It is quite clear that the overall sector is 100% healthcare in FPT. The industry exposure is informative. Industry Weight Biotechnology 79.27% Life Sciences Tools & Services 16.29% Pharmaceuticals 4.47% While this is in no way diversified, it does show that there are companies within the ETF which are not pure biotech, but are grouped within the fund. Some of them we do recognize from previous research and there is one firm that we previously analyzed and recommended. We will discuss this firm when we review the holdings. In terms of the holdings, as usual we will analyze the top 15 components, their symbols, ratings, (Moody’s and S&P), if any, and their weight within the ETF and the underlying index {BTK}. In this fund’s case we will also show their individual year to date and 12 month performance. FBT top 15 holdings Name/Symbol YTD perf/ 12 month Ratings, (Moody’s/S&P) Weight-BTF Weight- Index, {BTK} Nektar Therapeutics (NASDAQ: NKTR ) 0.71%/-3.27% NR/NR 4.47% 3.33% Dyax Corp. (NASDAQ: DYAX ) 165.50%/168.18% NR/NR 4.10% 3.33% Isis Pharmaceuticals, Inc. (NASDAQ: ISIS ) -7.92%/-0.25% NR/NR 4.09% 3.33% Alnylam Pharmaceuticals, Inc. (NASDAQ: ALNY ) 0.74%/-8.18% NR/NR 3.93% 3.33% United Therapeutics Corp. (NASDAQ: UTHR ) 20.08%/16.21% NR/NR 3.78% 3.33% Celldex Therapeutics Inc. (NASDAQ: CLDX ) -16.33%/-17.19% NR/NR 3.70% 3.33% Alkermes, PLC (NASDAQ: ALKS ) 22.49%/22.62% Ba3/BB 3.68% 3.33% Illumina, Inc. (NASDAQ: ILMN ) -4.60%/-7.27% NR/BBB 3.64% 3.33% Charles River Laboratories International, Inc. (NYSE: CRL ) 18.73%/18.41% Ba2/BBB- 3.57% 3.33% Novavax, Inc. (NASDAQ: NVAX ) 36.09%/46.99% NR/NR 3.48% 3.33% Alexion Pharmaceuticals, Inc. (NASDAQ: ALXN ) -3.18%/-9.12% NR/NR 3.35% 3.33% Myriad Genetics, Inc. (NASDAQ: MYGN ) 25.54%/21.89% NR/NR 3.35% 3.33% Vertex Pharmaceuticals Inc. (NASDAQ: VRTX ) 2.52%/2.02% NR/NR 3.35% 3.33% Regeneron Pharmaceuticals, Inc. (NASDAQ: REGN ) 33.24%/25.67% Baa1/NR 3.32% 3.33% Amgen Inc. (NASDAQ: AMGN ) -0.62%/-5.91% Baa1/A 3.24% 3.33% The top 15 holdings represent 55.05% with an average of 3.67%, with the bottom 15 totaling 44.96%. This was expected with the equal weighting of the ETF. Unlike the index which is even at 3.33% or 1/30 for each holding, the ETF is adjusted for share price and an equal value. Based upon the individual performance of the top 15 holdings, it is fairly obvious that returns are not reasonably predictable without extensive analysis of each company, their future products and FDA approval developments. The equal weightings here do provide an opportunity of participating in one of the top performers, such as Dyax Corporation with a 165.50% return YTD. Obviously, the return on Dyax far outweighs the negative return of a firm such as Celldex Therapeutics at -16.33% YTD. The benefit of the ETF allows participation in a sector where returns can be quite diverse from one firm to another. In terms of credit ratings, only 14.13% (S&P) of the top 15 have ratings and only 25.66% of these 15 holdings. It is quite apparent that with the rapid growth and negative balance sheets of these firms, the majority of the firms are mostly lower grade credits, if rated at all. Only Illumina, Inc., Charles River Laboratories International, Inc and the well known Amgen Inc. are investment grade, as per S&P. One of the firms in the ETF with a weighting of 2.91% is our personal favorite, Quintiles Transnational Holdings, Inc (NYSE: Q ), a company we had previously analyzed and recommended. Quintiles is the leader in {CRO} services or a Contract Research Organization. The company basically performs many of the services that large pharmaceuticals and Biotech firms require to bring their product to market and to continue to develop new and existing products. This would include Consulting Services, Portfolio and Strategy Planning, Clinical Trial Execution, Laboratories, Real-World and Late Stage Trials, Technology Solutions, Patient and Provider Engagement, Product Marketing and Sales. We are a little surprised to see it in this ETF. It is a profitable and quite a large capitalized firm, yet it will continue to grow and profit as long as there is a need for their services from the healthcare sector. As such, we think it is a great way to participate in the overall growth of the firm (14.51% YTD/18.26% 12 month) balanced with the performance of the other holdings in the ETF. Based upon the components and structure we analyzed the overall performance of the ETF and the index. FBT’s Performance, Fees and Recommendation Category FBR {ETF} BTK {Index} Net Expense Ratio .58% NA Turnover Ratio 58.00% NA YTD Return 9.94% (11/30/15) 5.99% (12/07/15) 10.66% (11/30/15) 5.48% (12/07/15) 1-Year Total Return 10.08% (11/30/15) 5.56% (12/07/15) 10.79% (11/30/15) 5.58% (12/07/15) Dividend Yield/SEC Yield 0.17%/-0.43% NA Beta (Shares/Holdings) 1.13/.70 NA P/E Ratio FY1/current 29.60/26.93 NA Price/Book Ratio FY1/current 8.00/7.06 NA Our expense ratio is in-line with the asset class median of 0.53% and is quite acceptable. Our turnover ratio is only slightly surprising here. With an asset class median or 18.00%, we expected much higher. One of the reasons is the general nature of the sector and the rules of the ETF and the underlying index that cause firms to be replaced. According to the NYSE Arca: Components will be removed from the index during the quarterly review if they fail any of the criteria below: (1) Current Market Capitalization is lower than $900 million (2) The Average Daily Traded Value for the past 3 Months is lower than $900,000 (3) The Current Last Traded Price is less than $1.00 In addition, various corporate actions may cause the stocks in the index to be substituted. As there has been M&A activity and various other corporate actions in the sector over the past year, the high turnover ratio is to be expected here. In terms of the ownership of the ETF, it is readily apparent that institutions and funds hold large holdings. While Wells Fargo (NYSE: WFC ) holds 6.31% and Morgan Stanley Smith Barney LLC (NYSE: MS ) holds 8.81%, the big surprise holding is another ETF that we previously analyzed and recommended. The First Trust Dorsey Wright Focus 5 ETF (NASDAQ: FV ), holds 33.99% of the total shares in its ETF or 24.20% of the total assets. The ETF has performed well due to its allocation in FBT, among others. FBT will continue to attract institutional shareholders and advisory clientele who seek allocation to the Biotech sector, regardless of economic conditions. In terms of economic conditions, many consider Biotech as being within the Pharmaceutical and medical space and defensive. We tend to agree, yet the cost of capital for the industry is always a concern. With interest rates set to rise this may be an issue for those firms which tend to borrow heavily to fund R&D. As such, though we are impressed with the performance over the past year the ETF is not for the squeamish. It is noted above that the YTD performance has dropped 4.00% since the end of November. The sector and its holdings are not for investors who are looking for the short term. A dollar cost strategy may be appropriate for investors who are familiar (or not familiar) with the frequent market routs. In terms of FBT the year high on July 20,2015 was $132.21 representing at that time a 28.96% YTD return, while the year low of $64.08 set on August 24,2015, after the Asia sell off, represented a loss of -37.49% YTD at that time. Overall, the volatility of the sector has not dissuaded institutional investors, (or speculators for that matter) from participating in this ETF or the sector. As the second largest biotech ETF, after the iShares Nasdaq Biotechnology ETF (NASDAQ: IBB ) it continues to represent an attractive vehicle to participate in a sector that will continue to produce new drugs and redevelop existing treatments. We are a strong buy on this ETF into 2016 and beyond.

Smart Beta Vs. Ben Graham

Summary “Smart Beta” and systematic investing strategies have become wildly popular in recent years. The trend has largely been driven by technological improvements and positive feedback loops. There are risks to systematic investing that must be acknowledged. Most importantly, systematic investors must acknowledge that stocks are not pieces of data, probabilities, or bets. They are legally, tangibly, and truly ownership interests in businesses. The Rise of Systematic Strategies According to Investopedia , “smart beta” was the most searched for financial term of 2015. Smart beta funds and ETFs are popping up all over the place. According to CNBC (emphasis mine) : As of June [2015], there were 444 strategic/smart beta ETFs in the market managing about $450 billion , according to Morningstar data. That’s up from 213 funds managing $132.5 billion in assets in 2009. They now account for 21 percent of all exchange-traded products and about 31 percent of all cash currently flowing into the industry . Anecdotally, fund companies like Gerstein Fisher (MUTF: GFMGX ) that have employed smart beta-like strategies for decades have suddenly seen a pouring in of assets. Before we go any further, what is smart beta? Investopedia defines it as follows: Smart beta defines a set of investment strategies that emphasize the use of alternative index construction rules to traditional market capitalization based indices. Smart beta emphasizes capturing investment factors or market inefficiencies in a rules-based and transparent way. The increased popularity of smart beta is linked to a desire for portfolio risk management and diversification along factor dimensions as well as seeking to enhance risk-adjusted returns above cap-weighted indices. It is a very general marketing term to describe (1) passive strategies (no active individual security selection) that (2) construct portfolios using weighting methods and metrics other than market capitalization weighting. Traditional indices are market weighted, and this has been observed to be detrimental to performance compared to equal weighting or fundamental-based weighting. By weighting, I mean the size of each position in the portfolio. An example of smart beta would be taking the 500 stocks in the S&P 500 (NYSEARCA: SPY ), but instead of assigning weights based on the market capitalizations (ex: Apple (NASDAQ: AAPL ) would be ~3% of the portfolio), you could weight the portfolio by LTM net income. For the purposes of this article, I’m more interested in smart beta for the general strategy and secular shift it represents – a shift toward systematic investment strategies that aren’t indexing, but aren’t individual security selection either. Outside of “smart beta” specifically, systematic strategies in general have become very popular. The success of Michael Covel’s Trend Following products and books, Tobias Carlisle’s The Acquirer’s Multiple product and books, Wesley Gray’s Alpha Architect , Joel Greenblatt’s Magic Formula and Gotham Funds (MUTF: GARIX ), etc. are evidence of this. What about the most popular investing blogs? Abnormal Returns , Pragmatic Capitalism , A Wealth of Common Sense . All these blogs have a systematic/passive bent. It seems to me that in the last year or two, systematic, rules-based strategies have become enormously popular. Maybe I didn’t have my eyes open before then, but now I can’t seem to avoid this stuff. Why? Technology. The rise is largely the result of technological improvements. Systematic strategies are fundamentally empirical. They require historical data and a backtest to answer the question “What’s worked in the past?” Technological improvements have made this possible. It’s now very easy to run a backtest on a Bloomberg terminal. More serious backtesters can use the extensive databases of Compustat and UChicago’s Center for Research in Security Prices (CRSP). And this feeds on itself, because people who do the research and backtests often publish their results, which are then used by other investors. So, more and more people have various answers to the question mentioned above and, naturally, more desire to do something with it. The other question: Is there a way we can run this strategy without human interference – fully automated? This is important because it’s difficult to manually follow a systematic strategy that involves purchasing hundreds of securities at potentially very short intervals, calculating weights, etc. It’s just not that feasible to do it manually. Technological improvements have made this feasible as well. I’m not so sure I understand the specifics of how this is done, but clearly, if hundreds of firms are doing it at much lower expense ratios than traditional actively-managed funds, there is automation involved. And this feeds on itself too. Once a fund/ETF has figured out how to do it, other investors can just buy into that ETF to participate in systematic investing. Personal Reflection This all is reflected in my recent articles and the evolution of my investment strategy. Being exposed to all of this has deeply influenced me. I also think, as I mentioned in a prior article, beginning to playing poker (a deeply probabilistic game) has had a significant impact. I’ve begun sourcing stocks using screens filtered by metrics that outperform, like EV/EBIT. I’ve begun taking small starter positions or bets, and looking at aggregate performance instead of performance by position. Put simply, I’ve begun to think of investments as bets and the future probabilistically. I’ve become empirical. Risks There is a lot of good in this transition, but I’m realizing now that it is dangerous if taken too far. Historical data is great, but there are risks to it. One is data mining, which I discussed in my article on stock screening. It’s worth googling “Butter in Bangladesh.” Then, there’s execution risk. What if your technology is flawed? What if there’s a power outage or you experience a data breach a la Target (NYSE: TGT )? What if you override the system at all? Joel Greenblatt points out that when he and his colleagues tried to source from Magic Formula without buying all the stocks on the list, the performance of the stocks they picked actually underperformed the market despite MF in aggregate outperforming, because they tended to avoid the biggest outperformers. They were the hairiest, and that’s why they performed so well. What if the markets change? The predictive power of metrics like P/B, which Fama and French articulated decades ago, has greatly diminished since. Past performance does not predict future performance. This is particularly important given the shift toward systematic strategies. The more popular these strategies get, the quicker the excess returns will be arbitraged away. Don’t assume you can stick to it either. It’s great looking at 50 years of data and seeing that over that period, the strategy has substantially outperformed the S&P, but that doesn’t mean there weren’t extended periods of substantial underperformance. In fact, most studies point out these spots of underperformance. One of my favorite quotes by Ben Carlson is this: The advice is to think and act for the long-term, which sounds great on paper, but the problem is that life isn’t lived in the long-term, it’s lived in the short-term… The problem is not the knowledge, it’s the behavior. Quitting smoking is not hard because people don’t know it’s bad for them, it’s hard because it’s habitual and it’s hard to change those bad habits. If you employ a systematic strategy, it’s because you think it will perform better than something else (most likely S&P 500) in terms of return, drawdown, etc. Naturally, you’ll be prone to comparing the performance of the strategy to that benchmark fairly frequently, and it will be difficult to see that it is performing worse over an extended period and still stick to it. To make this point more tangible, let’s use an example. You implement traditional Magic Formula (30 stocks, equal weight, annual rebalancing) with the expectation that your annual returns will substantially exceed the S&P 500. 4 years into implementation, you’ve underperformed in every single year (very possible) and cumulatively, the S&P 500 is up 15% annually and you are only up 8% annually. Unlike a fundamental research-driven active investor, you can’t explain this away with mistakes (“My current investment strategy works, I’ve just made mistakes and bad decisions along the way. My strategy is improved now and I’m more knowledgeable and experienced. I’ll do better going forward.”) The only thing you can do is question whether the selection criteria you are using still work. You only have four more years of data – data that disproves your initial hypothesis. That’s it. On top of that, clients and peers are badgering you about it. Surely, it’s difficult to stick to the strategy. Moreover, even if you want to stick to the strategy, there’s a good chance your clients don’t and they pull their money. At this point, you’ve stopped using the strategy at the worst time possible and managed to achieve underperformance with a strategy that has outperformed in the past and will likely outperform in the future. Stocks are Ownership Interests in Businesses I don’t mean to say that the empirical evidence is not compelling. It is. Some of these backtests encompass many decades and market cycles. Carlisle and Gray’s backtests in Quantitative Value are over 50 years. I also don’t mean to say that completely systematic strategies can’t work in practice. They can. The best example is probably Jim Simons’ Renaissance Technologies. The flagship Medallion fund did 72% annual returns before fees over a 20-year period from 1994 to 2014! What I am saying is that I don’t think a completely empirical approach to investing is sound, at least for me. There are too many things that can go wrong if we just leave it at this. Ultimately, stocks are ownership interests in businesses, not probabilities or bets. Maybe stocks can be thought of as probabilistic bets as a working assumption for a strategy, but that’s not what they actually are. A stock is legally, tangibly, and truly an ownership interest in a business. Ben Graham said this decades ago, and Buffett has singled it out as one of the 2-3 most important concepts to be learned from Graham. I think a much more sound approach to investing for empirically-driven, systematic investors is an upfront acknowledgement that goes something like this: Stocks are ownership interests in businesses. Stocks increase in price when the value of the underlying business increases or when there is a gap between the price of the stock and the value of the business and that gap closes. That is what is actually happening. As an investor, I have the opportunity to look at individual stocks and try to buy those whose prices do not fully reflect what the value of the underlying business is or will be. However, there is a wealth of data from historical markets that can be used to systematically identify these types of attractive situations. I feel, for various reasons, that these historical relationships are compelling and will continue to be. I also feel that I will be more successful as an investor using these systematic shortcuts than I would be if I tried to identify individual cases of undervaluation manually. The bottom line is that no matter who you are or how you invest, you need to acknowledge stocks for what they really are: ownership interests in businesses.

Neuroeconomics And Volatility

Summary Discussion on the summer spike in volatility in relation to the three areas of neuroeconomics. How your brain and emotions affect volatility decision making. I have preached patience and the science agrees. First, thank you for reading my articles. I have great readers, as shown by the comment sections of each article and I really appreciate all of you. If you enjoy my work, please follow me on Seeking Alpha and feel free to link to or share this article. In this piece, we will look into some very interesting research in economics and how that relates to volatility. Long-Term Volatility Trends I have always asserted that the VIX is driven long term by actual and predicted economic growth and short term by a variety of factors. If you look at the long-term chart below showing the VIX Index, you will see a slight correlation to the level of volatility and the performance of the general economy that generally agrees with this theory with a couple of exceptions. (click to enlarge) (click to enlarge) Let’s state the obvious here: if the economy is doing well or expected to be doing well, then volatility will tend to be lower and vice versa. This is a longer-term view of overall volatility. However, many other short-term events will produce better opportunities to profit from spikes in volatility when using VIX futures ETFs. Neuroeconomics This is something we haven’t discussed before in regards to volatility. This field of study seeks to explain human decision making, the ability to process multiple alternatives, and to follow a course of action. Neuroeconomics textbook definition fits very well into volatility trading. To compare volatility trading to neuroeconomics, we will use Jason Zweig’s book Your Money & Your Brain as a resource. Our First Lesson Monetary losses and gains are not just pure financial and psychological outcomes. These gains and losses create a biological change which has substantial effects on the brain and body. When trading volatility, it is important to understand and plan for the potential gains and losses of a given scenario. I am sure many people reading this article had been in a trade before and wondered things such as: how the heck can this be, this is out of control, the market is dumb, people are idiots, and then why did I even make that decision. On a daily basis, I see comments on social media that lend more to the premise of impulsive gambling rather than strategic investments in volatility. Areas of the brain linked to excitement and anxiety influence our financial decision making. Those decisions can be rational or irrational in nature. The nucleus accumbens is an area of the brain that activates when we expect a reward, such as a profitable volatility trade. Financial reward will most often cause traders to make decisions based on emotions and potential outcomes rather than the evidence at hand. According to Stanford University , the nucleus accumbens is located in an area of the brain rich in dopamine which has been linked to addiction. If you are only focusing on the reward of your volatility trade, you are leaving out 75% of the equation. How can you make a successful financial decision while encouraging your brain to release dopamine? Loss Aversion Loss aversion is the theory that individuals will exhibit greater sensitivity to losses than to an equivalent gain. I recommend reading The Neural Basis of Loss Aversion in Decision-Making Under Risk. In the past several years, investors have enjoyed above-average gains for an extended period of time. This pushed inverse volatility products such as the VelocityShares Daily Inverse VIX ST ETN (NASDAQ: XIV ) to new highs and leveraged long volatility products such as the ProShares Ultra VIX Short-Term Futures (NYSEARCA: UVXY ) to new lows. It also created pockets of writers who openly touted inverse volatility products as the best trading vehicles ever (more on those results later). Let’s view a market chart and the performance of XIV from 2011 to mid-2014. It is important to note the Y axis in this chart and that the gains in XIV would have been 10x the amount of the S&P over this period of time. Graph mainly for illustration purposes of increasing gains. You can see that a clear upward trend was in place until July of 2014. Beyond that point, the market, although making new highs, began to get choppy and growth fears began to emerge exponentially in the media. See below for July 2014 to present including the VIX Index. This chart shows the percentage of change and is separated by equity to give you a clearer picture of each instrument. VIX Spike Why would the VIX Index, and subsequently the VIX futures which affect volatility ETFs, spike to a level not seen since 2008 despite the lack of an actual recession? The answer is loss aversion. Investors were less willing to lose $5 than they were to potentially gain $5 after so many years of steady gains. Hitting the sell button is easy when you are up substantially on your original position or you fall into a growing category of investors that have never experienced a market correction. There was also no shortage of dire news stories about the economy and slow global growth, further supporting the neurological decision to avoid risk. We have previously discussed how UVXY operates and its tracking of the VIX futures. You can read more about UVXY and other volatility products in the ETF Guide . When the VIX futures were spiking this past summer, UVXY went on a tear and produced incredible gains in a short period of time. See below: (click to enlarge) During this time period, you had incredible interest in UVXY mainly coming from news features and a huge spike in social media volume. Bandwagoners looking to make a quick buck were sucked in. Some got out ahead, and others didn’t. By the time some traders realized they had made a mistake, the natural dopamine had long worn off and reality started to set in. Although unfortunate for them, these traders are an essential part of the volatility food chain in which the patient and well positioned survive. Conclusion I hope you have enjoyed this first lesson on volatility trading in relation to neuroeconomics. I look forward to bringing you more lessons as my schedule permits. To recap, we discussed how chemical and physical changes in the brain due to gains and losses on your investments influence the decision-making process. As volatility traders, we can take advantage of this information by clearly seeing through the market turmoil and making decisions based on evidence (past and present) rather than emotion. By understanding the parameters that volatility futures will trade in, the usual highs and usual lows based on the current scenario and historical figures, you can plan out your trade to encompass the three areas of neuroeconomics. By weighing all possible scenarios, you can be better prepared to follow through with your trade and increase the chances of profitability. As we have discussed, our natural instinct is to sell and save rather than to wait and gain. If I could pick the most common word out of my volatility articles here on Seeking Alpha, it would be patience and the science behind your decision making agrees. For more information on volatility trading and its related ETFs along with strategies and educational series, please check out my library here on Seeking Alpha. As always, thank you for reading!