Tag Archives: blue

Summary Once again, stock prices seem headed down. How far? How long? Why? A competent answer to these questions calls for perspective as to where we are now, and where we have been, both recently and at prior extremes. Who can provide that perspective of the past? Our best candidate(s) are folks who bet big money, frequently and constantly, on the near future. Who can answer the questions of the future? Our best suggestion is: “No one, definitively, because surrounding circumstances keep changing.”. But continual monitoring of the near future prospects compared to similar data at prior extremes may be a help. Folks who bet big money constantly on future stock prices They are the market-making [MM] community, acting in the opportunity for their own profit by servicing the intentions of clients managing billion-$ equity investment portfolios. What makes that community different from their clients, besides their forecast time horizon, is that as a group they bet directly against one another at the present moment, and the market for that activity presents useful expectations information. The clients, meanwhile are making bets against one another, but with ill-defined forecast time horizons, in markets not addressed to anything but immediate price discovery – that price which will provide a supply~demand clearing transaction of the moment. One that will simply queue up the next transaction challenge immediately following. Expectations of the transactors are not revealed except as to their preference for cash in comparison to the transaction subject. Where the transactors’ cash has come from, or is going to is an un-answered question. The lack of an answer prevents any further analysis or clues from this line of pursuit. In contrast, it is almost perfectly known where the market-makers cash has come from and is going back to. It is from their own capital (and funding) resources, to be used in providing market liquidity time and again, as the opportunity for them to profit presents itself. It needs to be kept liquid, as unencumbered as possible so it repeatedly can be put to work. Market liquidity is provided both by the MM firms’ block trade desks temporarily positioning (owning, net long or short) the momentary imbalance between buyers and sellers, and by other MM speculators (proprietary trading desks) willing to protect the MM positioners by selling them price-change protection insurance in a hedging deal. The cost of the price-change protection is a market-liquidity cost that is borne by the MM client-fund stock transactors. It is wrapped into in the bid-offer spread required by the to-be-consummated block trade. Both buyers and sellers in the negotiated transaction are impacted by their acquiescence to the transaction. The size of that cost, and the way the hedge deal is structured tells the story of what expectations the market-making community holds about what the clients are likely to do next with the subject stock. They are in communication with their clients constantly during every trading day, as they usually have been on several fronts for many years. The MMs have a pretty good idea of client intentions and action targets, despite client attempts to be obscure. The MM community augments that understanding with the instantaneous communications from a world-wide, local people-supported, 24×7 information-gathering system designed to keep them a step ahead, or at least not materially behind, the clients. We systematically translate the MM hedging actions into near-term price range forecasts. Forecasts with time horizons of the periods required to unwind the several types of derivative security contracts that may be involved in the hedge transactions, often no more than two to three months. Those price range forecasts have the great benefit of simple comparability. The extremes of the forecasts, in conjunction with the current market price, define upside and downside price change prospect limits. The balance between those, as portions of the whole range, are useful indications of near-term future price changes for each subject. Our common denominator for that we label the Range Index [RI]. The RI numeric is the percentage of that subject’s current forecast range between the current price and its lower extreme. RIs can span from over 100 (above the top future forecast) to negative numbers (below the lowest likely price forecast) although such extremes are not common. The smaller the RI, the larger is its upside proportion. For that subject a low RI implies the stock is cheaply priced at this point in time. Let’s check out to what extent there may be some forecast ability in the RI for a given security. We choose as a good example the ProShares UltraPro DOW30 (NYSEARCA: UDOW ), because as an ETF tracking the Dow Jones 30 index it is based on stocks actively being traded by major investment funds. Because the ETF is highly (3x) leveraged, its price changes through time are accentuated and easy to recognize. We will take every market day of the last 5 years, and from each starting point measure by how much UDOW’s price changed progressively, week by week, 5 market days at a time, out to nearly 4 months – 16 weeks, or 80 market days. Those results will be shown in a table with a blue central row that is the average price change trend for the ETF over the last 1261 market days – 5 years. To make comparisons easier between time periods of different lengths, all of the averages will be stated in annual compound growth rates, or CAGRs. Then to see what effect might be provided by knowing what the current-day RI was, we will exclude the likely most frequent RIs, the ones where the upside to downside price change proportions on cheaper days are between 1:1 and 2:1, and for the more expensive forecast days are 1:2. Corresponding RIs would be 33 to 50, and 50 to 66. In our table of price change calculations we will aggregate all the price changes in days with forecast RIs of 33 or lower into a row just above the blue average row. For all the days having RIs above 66 we will create a row of average price changes just below the blue average of all days. Please see Figure 1. Figure 1 (click to enlarge) By continuing this process we can fill out our table of annual rates of price changes at different levels of beginning forecast RIs from zero to one hundred, with those beyond contained in the 100:1 and 1:100 rows. Just don’t get overconfident; it’s not shooting fish in a barrel. The data of Figure 1 are averages of annual rates, meaning some are larger, some smaller, and some are even negative where the data are positive (profits), or may be positive where the data is negative. Figure 2 tells what proportion of the experiences indicated by the #BUYS column are in fact positive. For the whole 5 years’ days, that is a bit better than two of every three measures which offer a long investor the chance to make money. But a loss is taken in every third. Figure 2 (click to enlarge) Yes, the nearly half of forecast days (553 of 1258) with twice as much or more downside price change prospect (1:2 RWD:RSK) have worse odds for gain then the average, as well as negative payoffs. But far better PAYOFFS under better ODDS exist for the long-position players. That doesn’t make investing in UDOW an easy task, even with the MMs help. They’re not GOD. One troublemaker in the assignment is TIME. A great philosopher (at least) once observed: “You only have from now on.” No do-overs in most stock investing. It may be interesting, reassuring, (or scary) to study history, but we can’t go back. Do it NOW or tomorrow, or not at all. But yesterday is out. Another troublemaker was identified by the great philosopher, POGO: “We have met the enemy and he is us.” Stock investing is a more challenging game than chess, because moves by the pieces are not tightly defined. There are rules, and over time they may change some, usually well announced. But the true challenge is in trying to guess what the other side will do, and when they may do it. Each side attempts to anticipate the other, some more stridently. That, combined with time, keeps the game alive. Here is a two-year illustration of how the expectations for coming prices of UDOW by the MM community (the vertical lines) have been followed by actual market quotes (the heavy dots splitting each vertical) Figure 3 (click to enlarge) (used with permission) The colors reflect the imbalances between upside and downside price prospects in each forecast, as defined by the contemporary market quote. When current price is at or close to the bottom of the range, green is seen, and at the top, red. Caution lights appear when price is nearing the top of the range. That guidance is helpful, but not perfect. Please note the “go” signals in mid-August this year before UDOW dropped from mid-60’s to mid-40’s. Still, we perform our standard behavioral analysis on the actions of the MM community because it provides another forward-looking evidence of how significant players in this serious game evaluate not only the other investor players, but all of the fundamentals that go into their decisions and probable actions as well. And by providing a disciplined analysis of their conclusions in a wealth-maximizing portfolio setting, we have an historical background of whether and when the behavioral analysis has provided useful guidance. Here is the update of that analysis for UDOW to Monday’s close, November 16, 2015. Figure 4 (click to enlarge) (used with permission) Figure 4 provides a recalculation of MM forecasts indicating a Range Index of 26, or some three times as much upside price change prospect as price drawdown exposure. The row of data between the pictures tells that past UDOW 26 RIs, 38 0f them in the past 5 years of daily forecasts, have actually experienced worst-case price drawdowns averaging -10.3%. Of those 38, 34 or 89% of them, recovered in price over the next 32 market days sufficient to produce profits (along with the 4 losers) of +6.6%, a CAGR of +65%. Conclusion UDOW is an interesting gauge of market sentiment since its price is driven by a market index of 30 huge-cap stocks making up a part of market capitalization that cannot be ignored in market valuations. Its structured price leverage forces additional attention to the ETF, and conversely back from the ETF onto the market as a whole. No question of which is driven by the other, but they must accompany one another. The perspective UDOW provides at this point in time is that UDOW is an odds-on ETF likely to provide a capital gain at a high rate of CAGR over the next 2-3 months. The question of whether a better opportunity may soon be present is ever present, since prior experiences at present forecast levels have seen -10% further price drawdowns. In terms of unleveraged market indexes, that might be -3% to -3.5%. But there is no sign that a more serious concern is present among folks continuously and seriously addressing the matter. Save powder for a better shot, or go for a bird in hand? It’s your capital; it should be your call.