Markets of any kind work because some parties have goods or property that they are willing to exchange for other goods (barter) or for a recognized medium of value (money) that will be acceptable in payment for other goods considered by the buyer to be of at least equal or greater value at the time. Decisions about present and likely future value are essential to the process.
Those decisions are personal to the individuals owning the exchangeable properties or the money that simplifies the exchanges. As has been said, "beauty and value are in the eye of the beholder."
That condition keeps ideas about value from being resolved into any kind of immutable law of value, while keeping the ability of markets sufficient to encourage exchanges, which provide a history of values, once and now, demonstrated through real transactions.
But the essential need for notions of future value has driven many, through the years, to evolve ideas (hypotheses) about what should determine value, and have sought them to verify those ideas into theories by the evidence of subsequent transaction prices. This scientific method requires that the supporting theory contain a set of rules that can be replicated, in order to justify the underlying hypothesis.
Now comes the problem of applying the scientific method of deriving theories which might be accepted as laws, when dealing with so subjective an artistic topic as beauty or value. When the transaction price history fails to support the theory (as in CAPM), the usual approach is to modify the set of rules so that they will now include the recently miscreant experiences.
Continual repetition of such serious modifications tends to deteriorate the credibility of the proclaimed theory and begins to raise fatal questions about the underlying hypothesis. In some cases, the hypothesis becomes so discredited that it falls from any practical use. In other cases it lingers on, and encourages its followers to be part of "the other side of the trade" that is necessary to have transactions take place.
Our hypothesis is that . . .
Market-Makers [MMs] ensure their continuation in a highly profitable role by being as well-informed as is possible about developing influences on future values of subjects of interest. Also, by hedging actions in related markets they seek protection against the unforeseeable, arranging insurance against unwanted price change.
What they are willing to pay for that insurance tells how far the parties involved in the hedge transaction believe the subject's price is likely to move from its present point, during the defined life of the hedge transaction. We know how to translate those expectations into explicit possible future price ranges.
Where the subject's most recent transaction price lies within the expectations range is helpful in determining the direction and extent of subsequent near-term price moves. That can be shown by the following table whose rows striate the expectations ranges by the balance between their upside to downside prospects.
This shows the combined price-change experiences of 3,000+ stocks and ETFs over the past 5+ years, during the four months (16 weeks, or 80 market days) following each day the Market-Makers [MMs] hedged the capital they had to put at risk to perform their essential duties.
The light-blue row in the middle of the table is an average of all 2 ½ million-plus daily forecasts, as their subjects' prices changed following each forecast. The changes are stated in annual-rate terms to make it easier to compare holding periods of differing lengths.
As each row steps away from the overall average, it reduces the number of forecasts to only those that have the price change prospects or more extreme indicated by the green and red upside to downside balances of the far left captions. Their count is shown in the #BUYS column. The 35,910 forecasts at 100:1 contain all those where the contemporary market quote was at, or below, the bottom of the implied MM forecast price range.
Relating the rows above and below the blue row to that average, and to each other leaves a very clear impression that the MMs do have a good idea of near-term stock price direction on an individual issue basis. Further, the persistence of the size of changes on an annual rate of change basis as time is extended in columns from left to right eliminates the potential, in the aggregate, of impressive scores from cherry-picking any holding period. Likewise, the inclusion of all daily forecasts rules out score finagling by start-date to end-date mischief.
Does this evidence validate our hypothesis?
For many observers, this kind of evidence should start to move our hypothesis toward theory. But how useful is a theory that offers perhaps credible evidence that 1-2% of a population of opportunities can produce +15% annual rates of price returns compared to a blind selection producing +10%?
Let's see, 40,000 instances in 5 years is 8,000 a year of 252 market days, or over 30 a day if evenly spread out over time like creamy peanut butter instead of the chunk kind. That's not bad from an opportunity-frequency point of view - identifying 30 out of 3,000 daily could be a time-saver, even with internet access to EDGAR.
But is a +5% advantage worth having to pay attention, when you could, say, just buy and hold SPY? And SPY itself has risen in this time closer to +15% than to +10%.
What can the approach really produce?
The strength of the hypothesis is in an easily overlooked dimension. Focusing directly on price forecasts, rather than earnings, economics, politics, competition, technology, or the myriad of things influencing prices makes the choice of alternatives a directly comparable matter. Like the spaghetti sauce, they're all in there, along with an important element only the MMs can add: "order flow".
Being present every day, and knowing all the principal players and their habits and inclinations, the MMs enrich the sauce with their special insights. Rather than making the comparisons different, it adds a leveling influence of how each subject is viewed right now, in the light of today's market.
The comparability dimension makes it possible to select any subset of investments in which we are interested and see what prospects are presented there, and compare them to the population or to any other subset. Here is how one such subset looks in the same terms as the overall population's price change table above:
This is the impact that MM price range forecasts have had on selection opportunities among long-leveraged ETFs. Note that their average actual price changes have been close to those of the population average. But seeking out those with market quotes at or near the bottom of their forecast ranges has seen subsequent price gains four times those of their own averages. A +40% rate instead of a +15% rate is a lot more advantage over the +10% norm.
So, let's see what leveraged long ETFs are attractive now
Prospective rate of return is important, but so is risk exposure. To see what is attractive, we should look at both. Here is a map that plots forecast return on the horizontal axis, and on the vertical axis are actual experiences of worst-case price drawdowns during holding periods after MM forecasts of upside to downside like today's.
(used with permission)
Oops! That's kind of scary! Most of these leveraged long ETFs are priced at levels where past risk exposures are worse than the prospective gains being foreseen. Are there any candidates here worth taking a chance on?
To answer that we need to have a clearer sense of how likely it is that the gain may get captured, and how big that payoff might be. Here is a map that plots just that for the more interesting ETFs at hand.
Only one candidate stands out here: the 3x leveraged ProShares UltraPro Russell 2000 ETF (NYSEARCA:URTY) at  in both maps. At its current reward-to-risk level it has achieved +15% gains better than 9 times out of 10. Others have slightly better odds, but produce much smaller returns.
Well, let's take a look at URTY's details:
Here the vertical lines of the graph are forward-looking forecasts of ranges of coming likely prices, rather than the back-in-time looking displays of actual price experiences found in most stock "charts". In both the actual market quote is marked by the heavy dot.
Evidently, it is in URTY's nature to emulate a yo-yo, pricewise. If that process is continuing, the $93.44 sell-target top of its forecast (vertical line at right in picture above) is less ambitious than recent actual price experiences. And the +10.2% reach for that target seems credible, in light of 35 similar forecasts (out of 1100) having captured gains averaging +16%. The 35 contained 3 losers, hence the 91/100 winning odds.
By looking at URTY's prior experiences following our standard time-efficient risk management discipline we can get some perspective on what might be possible as a loss at today's forecast balance between upside and downside prospects. The measure we use to describe that balance is the Range Index, which tells what percentage of the price range (from top to bottom) lies beneath the current market quote.
Here is a picture of how URTY's achieved gains under this discipline have related to the range index at the time of forecast.
Any dot below the 1.00 of the y-axis vertical scale indicates a loss. Only one out of every 8 are there, for an overall win odds of 87 out of 100, an exceptionally high score. On the Range Index scale, very few loss instances exist in the area of 23, but one worst-case of .52 is present, meaning a capital loss of -48%.
Taking a chance on an investment in URTY at this point means going for a better than 9 out of 10 odds of a win, forecast at +10%. Larger such wins have been attained in 32 market days, or a month and a half calendar days, permitting reinvestment compounding of capital 8 times in a year. That math converts a +10.2% target into an annual rate of +115%.
But there might be a loss lurking, of as bad as half your investment, in that 10th try.
So, as the movie man says "Well kid, do ya feel lucky?"
Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it (other than from Seeking Alpha). The author has no business relationship with any company whose stock is mentioned in this article.