Measuring the greater fool effect

Felix Salmon had some interesting insights recently on bubbles:

The word “bubble”, at least for me, is a loaded term, with a specific meaning. For one thing, it implies speculation: people buying an asset which is going up in price, just because they think they’re going to be able to sell it to a greater fool at a substantial profit. The dot-com bubble was a prime example of that, with investors jumping onto high-flying technology stocks not because they thought the stocks were cheap but just because they thought the stocks were rising, and that they could make money day-trading these things. Much of the housing bubble looked like that too: you could buy a tract home in Phoenix with no money down, hold on to it for a few months, and then flip it for a substantial payday — even if you never expected to live in it. And certainly the bitcoin bubble fits the bill: pretty much the only reason to buy bitcoins and hold them for more than about 10 minutes is that you think they’re going to go up in value and that you’ll be able to make money as a result.

I have to wonder if there's not a way to measure at least part of what Salmon is talking about while it's actually happening. Is there, for example, some kind of survey or other instrument that could measure different expectations about the long and short term value of an investment by those about to purchase it? In a bubble, you would think that the numbers for the short term would climb relative to the long term. This is way out of my field so I have no idea if someone has looked at this.

Of course, investors might be reluctant to answer these questions honestly, even to themselves. This might be a good place to try implicit association tests. Being a relatively new approach, it's possible no one has tried this particular application yet. Not an easy test to set up but it might be worth thinking about.