Behavioral Analysis, when aimed at what select, experienced, well-informed and highly-motivated people do correctly, can produce startlingly-productive investment results.
When aimed at the common every-day errors that average folks casually incur, it is a pretty useless waste of investment research time and effort.
Volume-trade market makers [MMs] are the ideal subjects for this analysis approach, since they are required to put their firms' capital at risk in aiding big-money-fund clients as they shuffle portfolio holdings in their pursuit of capital gain and the avoidance of losses. MMs earn outrageous compensations by knowing how to hedge the risks they must take, how much protection they need to buy, and what their clients are likely to do to the prices of the stocks and ETFs in question during the time their hedges may need to be in place.
What the MMs will pay for that protection, and how it gets structured, tells just how far they believe prices are likely to rise, and to fall, in their multi-week, multi-month foreseeable forecast horizon.
We daily apply this form of analysis to some 2,000+ stocks and ETFs. We further check to see how well the MM's anticipations are confirmed in actual market prices during the following 3 months. A standard test measures how long it takes for the high price in each forecast range to be achieved, and how big its average may be, on a stock-by-stock basis for all experiences like the present ones, previously expected daily during the prior 5 years. Of interest are the typical maximum price drawdowns from cost, the odds of profitable experiences, and the annual rates of gain achieved from forecasts like the present.
The price range forecasts currently being implied by MM actions are in the first 3 data columns, followed by the upside "sell-target" price move from Price Now to the Range Forecast High. The remaining columns tell the results of prior forecasts with proportions of upside-to-downside like those of today.
The number of such forecasts are shown in the Sample Size columns at far right, the average CAGR of that sample resulting from the average holding periods required and the simple percentage net payoffs achieved on the capital employed, and the proportion of the sample providing profitable [win] experiences. Average worst-case drawdowns are based on all of the sample involvements.
AMZN is of interest because it has offered so many opportunities (188) previously, when the MMs behaved as though there was likely more than 3 times as much upside price in the coming 3 months as downside. The Range Index measures that proportion of the range that lies below the Price Now.
Despite its frequency (some 15% of the time) profits were available to be earned in 91% of the commitments, at average gains of over +10%, with average holding periods of 2 months (41 market days) which compounds to an +83% annual rate.
During these periods, drawdowns were typically less than 5%. That pertains only to the Sample holding periods. Drawdowns in other periods were avoided by the discipline of having capital only exposed during the test procedure, not continually. Risk, defined this way, was less than half of the average +10% payoff gains.
Ford is of interest because it presents an upside potential of +15%, along with a prior history of +15 ½% gains, with a 93% incidence of winning positions. Despite a longer average holding period (9 ½ weeks) the larger payoffs compound into an annual rate of +116%. This kind of experience has been present only about 3% of the time in the past five years, so it is an uncommon opportunity.
Several other very promising stocks are present in this "best" list. DIRECTV (DTV) has a near-perfect winning trades experience (97), Sunoco Logistics Partners (SXL) has low drawdowns and lower CAGR, but still is above multiples of market returns, NetGear (NTGR) supports a near +13% target with previous 18%+ payoffs that produced CAGR above +200%.
These ten buy candidates are supported by buy histories producing annual rates of return in excess of +100%, some 4 times that of the population from which they were drawn, and some 5-6 times the annual rate of major market averages. Better than 9 out of every 10 prior experiences (950 in all) offered winning trades at gains of over +10% on average. The population offered wins on only 2 out of 3, and average gains of only +4%