Investment markets are highly determined by the opinion of a goods valuation in the eyes of others. For this reason, information cascades can be highly influential, especially when it comes to the formation of a bubble. Bubbles are inflated by the speculation that comes from one investor thinking not that a good is worth more than it is priced in a real value kind of way, but that someone else will buy it for more than it is priced sometime soon. This article from the Economist discusses this in relation to a Netflix stock bubble that occurred about two years ago. A more recent example (and one with a much shorter time frame) is the Bitcoin bubble and collapse which took place over the course of about 3 weeks. Both of these rises in value were driven by reporting of the rapid rise in value taking place. This signals to others that these are “good” investments and assure them that others will think so when the time comes to sell. At first, rising trade numbers alone is enough to raise the value.
This is ultimately unsustainable as eventually bubbles reach a point where some people turn away and refuse to push the price higher. This is another signal which kicks off a frenzy of selling which deflates the price, generally past the levels of actual valuation as calculated by other means. What would be interesting to examine is whether a proportion level of say price to trade volume, share amount or some other metric could be calculated for the bubbling and collapsing tipping points. For the price to keep rising, investor number will have to rise, but is a collapse triggered by too many people signaling “Sell” or not enough continuing to buy? More often than not, investing is a game of expectation identification, being able to put any kind of coefficient to the cascade would give someone a significant leg up in knowing when to hop on the new fad, and when to get off before it’s too late.