Based on the complete dataset of Facebook equity prices (discarding initial values because of market interference by outside agencies) and taking into account documented and verified statistical values from reliable sources of great precision, comprising tens of thousands of individual price movements and transaction values, we calculate that Facebook common equity shares will be worthless within a few months. Since the value of a negatively-compounded sum never actually achieves zero, there is some room for debate as to what threshold value equates to a statistically-valid zero equivalent (extinction event), but this discussion is not materially useful except as an academic exercise.
While some may object to the methodology employed in our model, we point out that we are constrained by the limitations of the data available. The price data itself cannot be questioned with any credibility; as for the methodology itself; it employs very powerful and sophisticated algorithms generally accepted as state-of-the-art in calculating negative compound interest outcomes.
All computer models, however sophisticated, are subject to certain limitations. Here, however, the number of parameters is finite and manageable, comprising present value (31.00), future value (0) and rate (-0.098360655737705). We have run this calculation through multiple iterations and obtain a margin of error of less than 0.0012%, which we believe is attributable largely to the probability of input error in some iterations, and not any meaningful flaw in the algorithm.
Further, we find that all price action derives from one of two sources: a computer-driven trading algorithm (whether input electronically without human supervision or discretion); or anthropogenic decision-making. Since the anthropogenic component is substantial, we can only conclude that that human behavior contributes significantly to the downtrend.
While we understand that there will always be those who refuse to accept the inevitability of unpleasant news, we are confident in the extreme that our data, our models and our protocols meet the highest standards for scientific investigation, and stand by our result, however much we (or others) may wish it were different.
It’s settled science.