For example, if one believes that the price of GameStop will increase in the future, instead of buying a share of GameStop one can buy a call option on GameStop. Vice versa, if one thinks the GameStop price will fall in the future, one can buy a put option. This is usually done because options are cheaper, albeit riskier, than the stock itself. Hence, given their forward-looking nature, options are natural instruments, we argue, that one should use when trying to detect and quantify a bubble (if present). Since the option market as a whole has witnessed a tremendous growth over the past decade, our approach directly leverages the expectations that investors embed into option prices through their trading.
A further advantage of our method is how it estimates the fundamental value of the stock. In the traditional approach, the fundamental value of the stock needs to be estimated separately. This is done by assuming a specific financial model over which there is usually little consensus. Our approach, on the other hand, directly uses options, and this allows us to implicitly account for the fundamental value, without having to actually compute it. This overcomes an important drawback of the traditional approach, thereby making our method easily applicable.
Why did you decide to focus in your paper on Amazon and Facebook?
We focused on them for two reasons. First, both firms have highly liquid stocks and options with high interest among many investors. This is crucial for us, because reliable option prices are the sole input required to implement our methodology.
Second, both are tech firms whose prices have almost doubled from 2014 to 2018, which is the period for the data sample that we investigate. So, we wanted to see whether such a large price increase was primarily due to a bubble or whether it was justified based on the company fundamentals.
Our evidence is largely consistent with the idea that the overall price trends of the stocks (Amazon and Facebook) and market indexes (S&P 500 and NASDAQ) that we have analyzed (with the exception of GameStop) were sustained by fundamentals. However, we did find short-lived bubble episodes when market prices temporarily deviated from fundamental values. We found that these deviations are more likely to occur when trading activity is particularly high and before earning announcements (in the cases of Amazon and Facebook).
Can you explain how your estimation procedure and statistical test work?
As I mentioned before, options allow investors to take a bet on the future price of a stock. Now, if a stock is affected by a bubble, intuitively one might expect that the price of call and put options should be somehow affected by it, since option prices depend on the future price of the stock itself.
Indeed, our theory says that if a stock has a bubble, the prices of calls and puts written on that stock react to the bubble in different ways. While the prices of puts (which we can think of as instruments that allow investors to bet on the decline of the stock price) are not affected by a bubble, the prices of calls (which allow investors to bet on a possible stock price increase) are inflated by a bubble. We exploit this differential impact of a bubble on calls and puts to identify and estimate the magnitude of a bubble in the underlying stock price.
When you say a bubble, such as the one with GameStop, can be detected in real time, what kind of time frame do you mean exactly? Must a minimal amount of time elapse before you can declare that a bubble is occurring?
We believe this shows the strength of our proposed approach. It is relatively easy to deem an episode of price run-up as a bubble after the fact, as in the dot-com bubble of the late 1990s or the more recent case of GameStop. Looking back at the dot-com bubble, it is now apparent that it was indeed a bubble. However, things look different when we are in the midst of them. Our method is designed to detect a bubble without having to wait for the bubble to burst, and, as mentioned before, without having to rely on historical data that might not reflect investors’ most current expectations.