Author Archives: Scalper1

Summary Behavioral Analysis of the players moving big blocks of securities in and out of $-Billion portfolios provides insights into their expectations for price changes in coming months. Portfolio Managers have delved deeply into the fundamentals urging shifts in capital allocations; now they take actions on their private, unpublished conclusions. These block transactions reveal why. Multi-$Million trades strain market capacity, require temporary capital liquidity facilitation and negotiating help, but are necessary to accomplish significant asset reallocations in big-$ funds. Market-making firms provide that assistance, but only when they can sidestep risks involved by hedge deals intricately designed to transfer exposures to willing (at a price) speculators. Analysis of the prices paid and deal structures involved tell how far coming securities prices are likely to range. Those prospects, good and bad, can be directly compared. This is a Behavioral Analysis of Informed Expectations It follows a rational examination of what experienced, well-informed, highly-motivated professionals normally do, acting in their own best interests. It pits knowledgeable judgments of probable risks during bounded time periods against likely rewards of price changes, both up and down. It involves the skillful arbitrage of contracts demanding specific performances under defined circumstances. Ones traded in regulated markets for derivative securities, usually involving operational and/or financial leverage. The skill sets required for successful practice of these arts are not quickly or easily learned. The conduct of required practices are not widely allowed or casually granted. It makes good economic sense to contract-out the capabilities involved to those high up on the learning curve and reliability scale. It requires, from all parties involved, trust, but verification. What results is a communal judgment about the likely boundaries of price change during defined periods of future time. Those judgments get hammered out in markets between buyers and sellers of risk and of reward. The questions being answered are no longer “Why” buy or sell the subject, but “What Price” makes sense to pay or receive. All involved have their views; the associated hedge agreements translate possibilities into enforceable realities. We simply translate the realities into specific price ranges. Then the risk and benefit possibilities can be compared on common footings. A history of what has followed prior similar implied forecasts may provide further qualitative flavor to belief and influence of the forecasts. Certainty is a rare outcome. Subjects of this analysis We look to some 40 ETFs with holdings concentrated in stocks of narrow industry focus. They provide a wide array of interests and an opportunity to see comparisons being made of expectations for price change on common footings. Please see Figure 1. Figure 1 (click to enlarge) source: Yahoo Finance Market liquidity is addressed in the first four columns of Figure 1. What leaps out is the wide variation in the 5th column calculation of how many market days of trading at the average volume of the last 3 months it would take to provide an exit for all of the present holders in each ETF. Very liquid ETFs have a complete turnover potential in less than two weeks, or 10 days. Sometimes this is due to relatively small investor interest in the ETF’s focus, like the iShares PHLX SOX Semiconductor Sector Index ETF (NASDAQ: SOXX ), where less than a half $ billion of capital has been committed. Despite current disenchantment with its holdings of semiconductor stocks, speculative interest remains high enough to keep daily trading activity at ¾ of a million ETF shares, so a 6-day turnover exists. More frequently active investor commitments parallel the trading traffic, like in the iShares U.S. Real Estate ETF ( IYR) or the Market Vectors Gold Miners ETF ( GDX). They each provide the ease of exit present in SOXX. The potential problem for some ETFs is the roach motel syndrome, where it is easy to get in but may be costly to get out under time pressure. This seems to be a prevalent problem for most of the Power Shares narrow industry focus ETFs where triple-digit days to turnover are common. A normal trading year contains 252 days. The trade-spread cost to trade these ETFs is typically in single basis points of hundredths of a percent. That is in the same region of a $7 commission on a $10,000 trade ticket. Price-earnings ratios for these subjects range from 11 times earnings to 35-38-41 times. Coal’s economic problem of being between the rock of vast quantities of cheap to extract natural gas, and the hard place of ecological purgatory has put the Market Vectors Coal ETF ( KOL) in the bad-boy corner. At the other extreme, REITS and Internet system operation have drawn investor attention, sometimes with disregard for fundamental earnings support. Dividend yield attraction exists for income investors in some of these ETFs. Alerian MLP sources provide two with yields of 7-8%. A number of commodity/materials ETFs tempt the either desperate or unwary with yields of 3-6% recent payments most likely not to have a continuing future. Should that happen, the market may make it apparent that the “dividends” were really an advance form of return of capital. Where behavioral analysis contributes Investor preferences among these ETFs during the past year are indicated in the last two data columns of Figure 1. They are calculated from their price range experiences in that period, shown in the prior two columns. The PowerShares DB Oil Fund (NYSEARCA: DBO ), fluctuated the most, by 134% low to high, but the travel was from High to Low. That path also accompanied the Global X Uranium ETF ( URA), KOL, the SPDR S&P Oil & Gas Equipment & Services ETF ( XES), the Market Vectors Steel ETF ( SLX), and the Market Vectors Gold Miners ETF ( GDX). In the opposite direction we see the First Trust Internet ETF (NYSEARCA: FDN ) and the PowerShares NASDAQ Internet Shares (NASDAQ: PNQI ), both near the top of a YTD double. From a portfolio management viewpoint, what matters more is where holdings are priced now, compared with where their prices may go in coming months. Prices are, after all, what determine the progress of wealth-building, and are what can be a source of expenditure provision as an alternative to interest or dividend income. Ultimately price changes are the principal portfolio performance score-keeping agent. Where prices are now, in comparison to where they have been provides perspective as to what may be coming next. If prices are high in their past year’s range, for them to go higher means that their surroundings must also increase. If price is low relative to prior year scope, a price increase represents recovery, when and if it happens. As you think about the security’s environment, does it seem likely in coming months to be one of stability, of increase, or of possible decline? How would such change be likely to impact the security under consideration? First there is a need to be aware of what has recently been going on. The measure for that is the 52-week Range Index. The 52 week RI tells what proportion of the price range of the last 52 weeks is below the present price. A strong, rising investment likely will have a large part of its past-year price range under where it is now. Something above 50, the mid-point of the range is likely, all the way up into the 90’s. At the top of its year’s experience the 52wRI will be 100. At the bottom the 52wRI will be zero. For the materials ETFs mentioned above [DBO, URA, KOL, XES, SLX, and GDX] the big question is whether 2016 will see a turnaround, just continued limbo, or even worse news. The YTD winners’ questions are “will they continue to outclass their peers, forging ahead?” or “have they finally over-done it and are due for profit-taking-induced retrenchments?”. All the 52wRI can do is provide perspective. A look to the future requires a forecast. With a forecast, expressed in terms of prospective price changes, both up and down, a forecast Range Index, 4cRI or just RI, gives a sense of the balance between upcoming reward and risk. The historical 52wRI can’t do much more than frame the past, a reference that may produce poor guidance. Knowledgeable forecasting is what behavioral analysis of the actions of large investment organizations, dealing with the professional market-making community, can do. The process of making possible changes of focus for sizable chunks of capital produces the careful thinking of likely coming prices that lies behind such forecasts. Hedging-implied price range forecasts Figure 2 tells what the professional hedging activities of the market-makers imply for price range extremes of the symbols of Figure 1, but in a different row sequence (explanation to follow). Columns 2 through 5 are forecast or current data, the remaining columns are historical records of market behavior subsequent to prior instances of RI forecasts like those of the present. Figure 2 (click to enlarge) A lot of information is contained here, much of potential importance. Some study is deserved. Exactly the same evaluation process is used to derive the price range forecasts in columns 2 and 3 for all the Indexes and ETFs, regardless of leverage or inversion. Column 7’s values are what determine the specifics of columns 6 and 8-15. Each security’s row may present quite different prior conditions from other rows, but that is what is needed in order to make meaningful comparisons between the ETFs today for their appropriate potential future actions. Column 7 tells what balance exists between the prospects for upside price change and downside price change in the forecasts of columns 2 and 3 relative to column 4. The Range Index numbers in column 7 tells of the whole price range between each row of columns 2 and 3, what percentage lies between column 3 and 4. What part of the forecast price range is below the current market quote. That proportion is used to identify similar prior forecasts made in the past 5 years’ market days, counted in column 12. Those prior forecasts produce the histories displayed in the remaining columns. Of most basic interest to all investment considerations is the tradeoff between RISK and REWARD. Column 5 calculates the reward prospect as the upside percentage price change limit of column 2 above column 4. Proper appraisal of RISK requires recognition that it is not a static condition, but is of variable threat, depending on its surroundings. When the risk tree falls in an empty forest of a portfolio not containing that holding, you have no hearing of it, no concern. It is only the period when the subject security is in the portfolio that there is a risk exposure. So we look at each subject security’s price drawdown experiences during prior periods of similar Range Index holdings. And we look for the worst (most extreme) drawdowns, because that is when investors are most likely to accept a loss by selling out, rather than holding on for a recovery and for the higher price objective that induced the investment originally. Columns 5 and 6 are side by side not of an accident. While not the only consideration in investing, this is an important place to start when making comparisons between alternative investment choices. To that end, a picture comparison of these Index and ETF current Risk~Reward tradeoffs is instructive. Please see Figure 3. Figure 3 (used with permission) In this map the dotted diagonal line marks the points where upside price change Prospect (green horizontal scale) equals typical maximum price drawdown Experiences (red vertical scale). Of considerable interest is that the subjects all tend to cluster loosely about that watershed. This strongly suggests that the overall market environment is neither dangerously overpriced or strongly depressed in price. If SPDR S&P 500 Trust ETF (NYSEARCA: SPY ) were on this map, it would be just to the right of and adjacent to [11]. In general, up and to the left are bad risk~return tradeoffs, and down and to the right are the more attractive ones. ETFs in the green area have reward-to-risk ratios of at least 5:1. The poorest Return~Risk tradeoffs are in [16] the SPDR S&P Capital Markets ETF ( KCE), and [24] the SPDR S&P Bank ETF ( KBE). The better ones are [2] the iPath DJ-UBS Livestock Total Return Sub-Index ETN ( COW), [8] the ALPS Alerian MLP ETF ( AMLP), and [18] the iShares U.S. Home Construction ETF ( ITB), the JPMorgan Alerian MLP Index ETN ( AMJ), and the SPDR Biotech ETF ( XBI). Still, there are other considerations that may, or should, influence investors’ preferences in adjusting portfolio holdings. Looking back at figure 2, there are conditions that may disrupt the organized notions drawn from Figure 3. Column 8 tells what proportion of the prior similar forecasts persevered in recovering from those worst-case drawdowns, and for the resolute holder turned into profitable outcomes, often reaching their targeted price objectives. Batting averages or ODDS of 7 out of 8 and 9 out of 10 are quite possible to accomplish by active investors. Column 10 tells how large the PAYOFFS were, not only of the recoveries, but including the losses. And those gains, in comparison with the forecast promises of column 5 offer a measure of the credibility of the forecast. There will be circumstances where credibility will be low and recovery odds worse than 50-50. When such conditions appear pervasive, cash is a low-risk temporary investment, sometimes the treasured resource. Most of the time there are prospective investment candidates that have odds of profitable outcomes of at least 6 or 7 out of 8 (above 75%) over a forecast horizon of 3 months. Several of these will have attractive combinations of prospective payoffs and credible ratios based on achieved payoffs. To sort these out Figure 2 has segregated its content rows by the Win Odds column into greater than and less than 75% and provided a blue subtotal of the 19 passing that screen. Those 19 have been further ranked by a figure of merit shown in column 15 that considers odds, payoffs, credibility and frequency of presence. Beyond Risk and Reward, Odds and Payoffs are critical considerations in the timely selection of portfolio asset adjustments. For these 37 industry-focus ETF candidates Figure 4 provides a comparative map. Its dimensions follow the same desirability parameters as in the Reward~Risk map of Figure 3, up and left is poorer, down and right is better. Figure 4 (used with permission) If Figure 4 leaves you with the impression that this may not be an exciting time to invest in ETFs with a narrow industry focus, you would be right. Maybe not a bad time, but perhaps a time to look elsewhere to see if these choices are the best available now. To have a different set of alternatives to consider, we offer up today’s list of the top 20 equities we evaluate daily from the 2,500+ issues that provide a sufficient source of information to produce price range forecasts. Compare Figure 5 with figure 4: Figure 5 (used with permission) The ETFs in Figure 4 have histories of price recovery from drawdowns that span the horizontal Win Odds scale from 80 of 100 to 100 of 100, and extend into the left space of 75 of 100 that is the top of the vertical Payoffs scale. In that space, [12] of Figure 4 is SPY, as a market-average reference comparison. It has Win odds of only 71 of 100, so is artificially bounded on the left, and has achieved payoffs of +2.2%, so it is positioned about as far up the payoff scale as is possible. Few of the industry-focus ETFs have achieved payoffs at their present Range Index forecasts of much above +5%, suggesting modest attraction at this point in time, despite their high win odds in several cases. But are there any better alternatives? That is why we included Figure 5, the Odds & Payoffs map of today’s top20 analysis list. A number of specific single stocks and one ETF have produced gains in excess of +10%, with Win Odds comparable to those in Figure 4. So there are alternatives to narrow-focus ETFs. The other blue comparison rows of Figure 2 provide perspectives in terms of an average of all the 37 narrow-industry focus ETFs above. An average of the day’s 20 best-ranked stocks and ETFs, from an overall population of over 2,500 securities, using an odds-weighted Risk~Reward scale, are also presented. This kind of comparing between alternative investments is what often distinguishes the experienced investor from the neophyte. There are so many intriguing possible stories of investment bonanzas that it may be difficult to keep focus. And for the newbie investor it may be a daunting challenge to decide on what combination of attributes may be most important. An advantage of the behavioral analysis approach is that price prospects suggested by fundamental and competitive analysis are being vetted by experienced, well-informed market professionals on both sides of the trade. Conclusion At present there is no outstanding sector ETF choice for asset allocation emphasis or the commitment of new capital. Neither is there grave concern for dangerous outcome from present sector positions. The Biotech Industry is well represented by ETFs including the XBI, the Market Vectors Biotech ETF (NYSEARCA: BBH ), and the First Trust NYSE Arca Biotech ETF (NYSEARCA: FBT ). But of these, at current forecast levels, a +5% to +7% achievement is what may be expected. Prior forecast gestation periods of 5-7 weeks imply annual compounding of 9 or 10 times to generate CAGRs of +50% to +80%, which are far from revolting. Still, among specific equities there are many that have produced better than the blue-row 20 best-odds average CAGR of +97%. So while ETFs suggest a more protected reward~risk tradeoff via the diversity of a fund, there are at least 20 alternative stocks averaging ratios of 1.9 times as much prospective return as their prior-forecast actual experienced price drawdown risks. The same measure for the 19 current best ranked industry ETFs is only 1.5 to 1 (Figure 2, column 14, blue summary row). The Win Odds recovery rate from their -4.3% typical maximum drawdowns at 85 is almost competitive with the 20 individual equities 88 Win Odds recoveries from a modestly higher -5.8% bad experience average, but their +11% achieved payoffs are triple those of the ETFs. It all depends on which dimensions of the investing challenge are most important to the investor. At present it appears from a Behavioral Analysis comparison that there are favorable choices that can be made. Even the market proxy alternative SPY does not display reason for serious defensive concerns.