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Summary This article compares Facebook, Amazon, Apple, Netflix, Google, and Tesla, not in their competitive market environments, but in the investment market competition for higher coming stock prices. As we are wont to do, the comparisons are through the eyes of Market-Makers [MMs], protecting themselves as they serve the block-size transaction orders of their big-$ clients. But those views are importantly conditioned by big-$ clients order-flows, the ones with the money muscle to move markets. Opportunity can be created – their collective competitive motto These companies all seek to build their kingdoms by upsetting the established order in their spheres of influence. Information, retail, entertainment, transportation all have in common huge size of markets, with innovations of varied types the principal tool of disruption. Wielded by able, controversial, charismatic leaders. But equity investment markets provide a common denominator for comparisons among even the most diverse of subjects. And behavioral analysis of the players in this very serious game ensures a look at what investors actually expect in coming days weeks and months, (not what others may want you to think) in the scorecard common to all: stock price changes. Price changes initiated and supported by players with the money muscle to make things happen. Price changes to enrich – or wound – active investors who are sensitive to the value and power of the investment resource of time. The role of self-protection Inside the ropes of the investing ring, the rule has to be: Protect yourself at all times. So MMs, when exposing their capital to risk in balancing buyers with sellers in million-dollar-plus trades, buy protection to ensure the continued presence of that capital for employment in the hundreds of like deals yet to occur each day. “All” that takes, is finding some other player to take on the perceived risk – for a price. The price of the protection, and the structure of the hedging deal tells what the players think is reasonably possible to happen to the subject issue’s price during the days, weeks, and months it takes to remove the capital from risk and to unwind the contract commitments of the options, futures or swaps performing the risk transfer. This is analysis of the behavior of experienced, qualified professional experts, doing what is right, so that we can borrow his judgment to help us in our goals. Other behavioral analysis tends to focus on the human errors that folks make, perhaps so that they may be victimized. Such efforts have so far failed to identify any meaningful rewards from that approach. In each subject of this analysis there will be clear evidence of the frequency and extent of the investment rates of return previously achieved subsequent to prior forecasts like those of today. No guarantees, just the perspective of what previously has been accomplished. The basic Risk vs. Reward tradeoff We start by comparing what differences presently exist between the FAANGT stocks upside price change forecasts, and the worst-case price drawdowns in the 3 months subsequent to prior forecasts like those of today. Figure 1 (used with permission) Pictured are Facebook (NASDAQ: FB ), Amazon (NASDAQ: AMZN ), Apple (NASDAQ: AAPL ), Netflix (NASDAQ: NFLX ), Google (NASDAQ: GOOG ) (NASDAQ: GOOGL ), and Tesla Motors (NASDAQ: TSLA ). In general, these stocks present a fairly uniform tradeoff of expected returns from coming price changes against the worst experiences of what has happened in the past, following MM forecasts like those of this day. The one modest divergence is that of AAPL [2], where downside experiences have been less extreme than all others, especially vs. FB [4] and GOOG [5] which offer slightly less optimistic upsides. The biggest return payoff prospect, NFLX [1] carries the highest risk exposure prior experiences. But while this tradeoff is paramount for most investors, there are times and circumstances that raise other considerations in importance. Thoughtful investors usually have many of these other dimensions in mind at all times, with varying degrees of importance. The table in Figure 2 lays several of them out in an orderly comparison. Figure 2 (click to enlarge) What has been pictured in Figure 1 was taken from columns (5) and (6) of Figure 2. In turn, (5) is a calculation of (2) divided by (4), minus 1, expressed as a percent. Column (6) is an average of the largest negative experiences of the first sub-column of (12). All of (12) tells how many times in the past 5 years’ market days of forecasts there has been a forecast like this day’s. You may note that FB has only been around since its more recent IPO of some 3+ years ago, while most of the other “old-timers” have a full 1261 days of forecasts. TSLA is but a couple of 21-day months short of that mark. Importantly, Reward~Risk maps like Figure 1 and tables like Figure 2 are creatures of the date those snapshots were taken, not lasting comparisons of the “goodness” of these stocks versus one another over long periods of time. The forecasts involved here are implied from the “crowd-source” judgments and actions of buyers and sellers of price change protection caused by market makers seeking to fill volume (block) transaction orders by “institutional” clients managing Billion-$ portfolios who must operate on a scale that “regular-way” market transactions cannot accommodate. Those transactions normally involve the MMs having to put at risk amounts of firm capital in order to bring to balance buyers and sellers of the stock involved. Hedging of those capital risk exposures involves negotiations between the MM and sellers of such protection via derivative securities contracts involving options, futures, or swaps. The prices paid for protection and the structure of the deal tell how far the subject’s price may be likely to travel, as seen by all parties involved. That includes the investing organization initiating the trade order, since they wind up paying the cost of the hedge, as a part of the price of market liquidity arranged by the MM. It turns out that all 3 parties are well-informed, experienced, “consenting adults”, whose actions provide information that is generally not recognized. Such traffic goes on day after day, causing changes in the relative attractiveness of stocks to one another. But the implied price range forecasts of columns (2) and (3) are limited in their time scope to the life of the derivatives contracts used in the hedge deals. Those are usually kept as brief as possible, out of cost considerations. It turns out, based on decades of daily forecast experiences, that their reliability and usefulness diminishes markedly out beyond 6 months. Within that horizon, a more critical limit of 3 months turns out to be a useful boundary. Using that limit we look to see what bad things – like price drawdowns from (4) – have happened following prior like forecasts. That average of worst experiences is in (6). Column (8) tells what percentage of the forecasts have had prices recover from (6) to be profitable in reaching (2), or by the time 3 months later has arrived. At either of those points, the net of gains minus losses is presented in (9), the time taken in (10), and the CAGR in (11). Other measures of comparative interest are the proportion of (5) to (9) in (13), and the relation of (5) to (6) in (14). A figure-of-merit is calculated in (15) using the odds of (8) and its complement to weight (5) and (6), recognizing the frequency emphasis provided in (12). This Figure 2 table is ranked by (15). So, what to do with this data? Right now, it can be used to make comparisons between the probable coming near-term price movements of the six stocks. The investor may want to embed in his considerations the personal preferences he may have as to the need for price gain in the face of potential price loss, and the implications of the time horizon involved. For example, in 80 past days FB has had knowledgeable and experienced market professionals making good-sized bets that a +10% gain is likely to occur, and in 65 of those times a profit has occurred. Net of the other 15 times, a 7% gain was had on all 80 bets. That has happened in 10% of FB’s entire market existence, so it’s not a rare occurrence. On the other hand, NFLX could indeed be up by 18% within 3 months, and smart money is willing to pay to avoid being hurt that way. But in the 135 prior times his kind of forecast has appeared, the investor has seen about 1/6th of his investment disappear at least temporarily, and at least 3 times out of every ten, some of it permanently. All of that to earn a slightly smaller net payoff in about the same period of capital commitment to get a CAGR like FB’s. Then TSLA appears to offer a prospect of over +15% gain in the same period, with odds similar to NFLX and drawdown exposures of -12% instead of -16%. But its average net payoffs, a little better than NFLX, took measurably longer to be achieved, so its CAGR is markedly less. Perhaps more important is a comparison with what else is available. The blue summary lines at the bottom of Figure 2 tell what a market-tracking-proxy, SPY, currently offers, how that compares with the population average of 2500+ equity alternatives, and the best-ranked 20 of that population. SPY currently reflects the concerns of many market gloom-&-doomers with a not very credible upside possibility of under +9%, while having delivered less than +3% under similar forecast circumstances in over a year’s worth of experiences. Only GOOG presents as poor a CAGR history (from today’s forecast) as does SPY. The whole population provides some eye-catching +15% or better possibilities, but is dramatically short on deliverance, with only 6 out of every 10 profitable, and achieved gains only one fifth of the promises. The history of the 20 best-ranked stocks on the other hand is of interest. No guarantees that it will be repeated now, but in over 10% of the forecast opportunities of this group of stocks, they have delivered on price gain return prospects of +11%. With drawdown experiences of only half of the gain prospects, they have recovered in 7 out of every 8 cases to deliver net gains at an annual rate of better than 100%. On our figure-of-merit scorecard, they have collectively ranked some three times better than FB, the best of the 6 competitively disruptive stocks discussed above. Conclusion This appears to be a more opportune time to buy FB and AAPL than SPY or the other identified alternative stock candidates. Tomorrow is likely to be some different. But a good deal of what goes into choosing between investment commitments depends beyond simple measures, like P/E ratios, or ephemeral measures like what this charismatic CEO foresees. What really counts is what the investor community believes is possible for the stock’s price in times to come. And to what degree those beliefs have been borne out in the harsh reality of the marketplace. Comparison is the essential tool of the valuator. History may help in providing perspective. But equity investors need to be mindful that uncertainty is always present because investing involves the future. So some guessing about the odds of success (however it is defined) is always required. Help in that effort may be useful. Or not. What are the odds? Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.