The Disposition Effect in Crypto
What we found while going through On-Chain addresses
Most retail investors either don’t spend too much time thinking about their behavior or believe that, when it comes to financial and economic decisions, human actions are completely rational and tend to follow patterns that will on average produce them the most utility. In fact, what most of them don’t realize is that this latter school of thought has been widely studied over the last decades and its foundings challenge classical economic theories from the 18th century. By now you are probably getting where this is going, as investors making only rational decisions could not be farther from the truth.
Behavioral Economics is a school of thought of the 20th century created by great minds such as Richard Thaler and Rober Shiller, that studies how an individual’s decision-making process varies according to different psychological, cognitive, emotional, cultural, and social factors; and it extends vastly to different areas that would actually be impossible to cover in a single book, let alone in this short article. Many sources note Prospect Theory by Daniel Kahneman and Amos Tversky as to how Behavioral Economics came to be, however, I’d like to keep the focus of this writing towards two key areas:
- The rationale of decision-making when investing in cryptocurrencies, and
- An anomaly called “Disposition Effect”, which we have been able to observe in the IntoTheBlock platform after leveraging statistical models through multiple public ledgers and scraped over millions of investors addresses.
But first, let’s go through some important concepts that will help me frame my point later (pardon my history class).
Classical economic theory emerged by the mid-1700s shortly after western capitalism was born, and supported ideas such as free trade, rational pricing of products based on supply and demand, rational decision making based on “Expected Utility”, amongst many other theories.
Now, in economics “Expected Utility”, which was first mentioned by Daniel Bernoulli (1738), is “used as a tool for analyzing situations where individuals must make a decision without knowing which outcomes may result from that decision, i.e., decision making under uncertainty. These individuals will choose the action that will result in the highest expected utility, which is the sum of the products of probability and utility over all possible outcomes” according to Investopedia. Or in other words, people will make completely rational decisions.
In addition, “Risk Aversion” is used to describe the behavior of investors that will favor the preservation of capital over a higher return investment with greater risks. It is actually easier to understand the concept by looking at the chart below, where the Y-axis represents changes in Utility, and the X-axis represents changes in Compensation.
In essence:
- Risk-Averse = Favors higher Utility over less Compensation.
- Risk-Seeking = Favors Higher Compensation over less Utility.
So what does all this have to do with the Disposition Effect?
Fast forward almost 300 years and we’re back at Behavioral Economics, where two scholars named Hersh Shefrin and Meir Statman discovered an anomaly that relates to the way investors tend to realize gains quicker by selling assets that have increased in value, and how they treat unrealized losses by keeping those assets that have decreased in value (which they named “Disposition Effect”). This by itself doesn’t explain the whole picture, but other studies have exposed that company stocks that performed well over the last 6 months tend to keep momentum and do well on the subsequent other 6 months; and other stocks that have performed poorly over a 6 months period continue to do so over the next 6 months. Therefore, the rational thing to do would be to keep those stocks with good performance and sell the ones with poor returns, but investors tend to do the exact opposite.
In their original paper, Shefrin and Statman described that “people dislike incurring losses much more than they enjoy making gains, and people are willing to gamble in the domain of losses”, which is a completely irrational action contradicting classical economics. Now you see how both concepts of Expected Utility and Risk Aversion are intertwined with how investors act when it comes to realizing gains and losses?
Like I briefly mentioned in the second paragraph, we have been able to observe evidence of The Disposition Effect thanks to the intrinsic nature of public ledgers where anyone is able to see the complete transactional activity (and history) of these networks, and by running statistical models through blockchain datasets, which have produced an analysis that we have called the “In/Out of the Money”.
The In/Out of the Money (IOM) Indicator
At IntoTheBlock, we take every address with a positive balance of a particular token and estimate the average cost at which the address acquired those tokens. Then we organize addresses in clusters according to their average purchase cost, which we then compare to the current price of that crypto asset. This in turn, allows us to see how many addresses would either make or lose money if they were to sell their tokens at the current moment in time.
Correspondingly, if the current price of the asset is higher than the average cost of the tokens being held on those addresses, it is said that they are “In the Money”. On the contrary, if the current price of the asset is less than the average cost of the assets being held, then those addresses are “Out of the Money”. Lastly, if the current price of the crypto asset is very close to the average price of the tokens being held, it is said that those addresses are “At the Money”.
What we found
After going through different crypto assets and comparing their current “Global In/Out of the Money”, we were able to pinpoint a couple of occasions where investors are reluctant to realize losses, such as Litecoin (LTC) and Ethereum Classic (ETC) as you can see below.
For Litecoin there are about 2.45M of addresses with positive LTC balance, of which 69.36% are considered to be “Out of the Money”, with only 23.58% of them “In the Money”.
In the case of ETC, of the nearly 1.8M addresses that currently hold Ethereum Classic tokens, 62.92% of them are considered to be “Out of the Money”, while only 33.61% to be “In the Money”.
On the other hand, there are other crypto assets that show most of it addresses currently “In the Money”, as it is the case with BTC where 75.19% of addresses holding Bitcoins acquired them on average at a cheaper price than $9,439.5 USD.
It is worth noting that this indicator is closely related to how the price of these crypto-assets fluctuate, but the reasons behind these differences from some tokens to others are extensive. One possible explanation, at least for BTC is that most of the individuals that were “Out of the Money” for a very long time decided to sell their holdings and recoup a portion of their investment than to stay at a losing position, something in investing we call “shaking off the weak hands” (investors/traders that lack conviction of their strategies and don’t have the resources to see them through).
So, what can we do to avoid being affected by the “Disposition Effect”?
The literature about strategies for avoiding getting caught by the Disposition Effect is also quite extensive, but something called “Hedonic Framing” resonates a bit louder from the sources I was able to gather, which takes us back to Richard Thaler, considered as one of the founding fathers of Behavioral Economics.
“Hedonic Framing refers to how people try to maximize psychological pleasure and minimize pain (regret) when faced with decisions relating to gains and losses. This means that two individual gains are perceived to be more valuable than a single larger gain of the same value. Similarly, two separate losses will be perceived to be less painful if combined into a single, larger loss” according to Conversion Uplift.
Moreover, Thaler went to identify a set of simple strategies that investors can use to avoid the “Disposition Effect” through “Hedonic Framing” such as segregating gains, adding multiple losses together, including some of the smaller losses within larger gains and separating small gains from larger losses (sometimes referred to as the “silver lining effect”).
I’ve been personally affected by loss aversion multiple times and, as you would have guessed, results haven’t been positive, therefore I highly recommend you read more about these interesting topics and to keep an eye on fascinating metrics like the In/Out of the Money indicator on the IntoTheBlock platform, as we continue creating actionable intelligence for crypto assets.
Thanks!
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