Research Article #45 - The Seasonality Effect that Drives Cryptocurrency Returns
Research Article #45 - The Seasonality Effect that Drives Cryptocurrency Returns
In-depth exploration of turn-of-the-month effect in cryptocurrencies returns
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Have you ever read “The Richest Man in Babylon” by George S. Clason?
It's one of the classics on personal finance.
It teaches the importance of investing a portion of income, every month, to have a better chance at a robust financial situation later in life.
It’s a very famous book, so you probably read this book, or if not, you probably read other books on personal finance.
At some point, we’re all interested on how we can make our finances a bit better.
So do millions of other people.
But what can we do with that information?
One of the core principles about personal finance is, to invest a portion of our earnings, into “safe”, long-term investments, right?
You may be wondering:
“But Pedma, I thought we were going to discuss a trading strategy today!”
Sure, and we’re going to!
But we need to start from a core idea about market behavior. And those core ideas are out there, in the real world!
As researchers, we want to think more than we analyze.
Imagine that you, me, your neighbor, we all invest at around the same time.
If a decent chunk of capital is being allocated, at specific times of the month, why don’t we facilitate that transaction, by providing inventory that the market has a need for?
If our initial thesis has some level of merit, we should observe an impact, in the markets where most people allocate to, right?
That exact idea was first reported by Lakonishok and Smidt in 1988.
The beginning of the turn-of-the-month period, is defined as the last trading day of the month , ending with the third day of the following month.
What was found is that on average, the first 4 days of the month, account for most of the positive returns from 1897-1986.
That is a big deal.
There has been a lot of research over the following years, to explain the driver behind the phenomenon.
If this is real, and we can have some edge, by accumulating inventory before the month ends, to sell into the buying pressure caused by month end investors, that could be a good source of returns.
As usual, you know that I am an ideas first researcher, so when I am doing research for these articles, I don’t care about the equity curves or the fancy metrics.
That comes later, when we do portfolio allocation and risk models.
When I read an author’s work, I want to see his reasoning, for why his idea might be a valid source of edge.
Why should I be compensated for taking this risk? Who am I serving?
That is what matters. If you restrict your research to that type of mentality, you start to make better and faster progress in developing models.
Sure, you won’t be having ideas for models every day of the week, but your signal to noise ratio, in terms of research, should increase.
This idea is often applied to equities, but I am a crypto trader, so this article will be mainly devoted to see if the same phenomenon is also present in crypto.
Let’s dive in!
Index
Strategy’s Thesis
Index
Driver of Strategy Returns
Introductory Test
Multi Asset Long-Only Model Performance Analysis
Parameter Settings Overview
Conclusion
Python Code Section
Driver of Strategy Returns
In all of our tests, we want to start with a core idea about the factor that drives the returns of our models.
I know that I repeat that a lot, but it’s for a good reason!
In the case of today’s article, it will be seasonality.
What is seasonality?
Imagine that you’re a farmer with a beautiful apple orchard.
Every year you notice this pattern where, during certain months, your apple trees are full of ripe, juicy apples.
Other months, the trees are empty and just resting.
As the years pass, you record this pattern, and you notice that it happens every year, just like clock work.
You love farming, but you also need to make some money. So you sell your apples at the local market.
During the harvest season, you have plenty of apples to sell, so the prices are lower, because you have so much supply.
In the off-season, apples are not as abundant, and prices skyrocket, as a function of a demand increase, and a shorter supply.
For the sake of the story, let’s say that you find a way to store apples, that keep them in perfect shape, until the off-season.
Given this, you produce and purchase all the apples that you can hold, when supply is really high, and store them.
Once the apple supply starts to dry up once again, you have this plentiful reserve of apples, ready to serve your clients at a higher price than what you initially bought them for.
But why should you sell to your clients at higher prices?
Are you some unethical person, trying to squeeze unreasonable amounts of money out of people?
I don’t think so, because you also need to pay for the risk that you take, when things don’t go so well.
Let’s keep it simple, and talk about the most apparent risk.
The risk of storage.
If the quality of the apples in your storage decays, or there’s a storm that destroys your storage facilities, you have a big problem, because you just lost all of your inventory, that you spent a lot of money on.
Not even considering the returns you could have gotten, on that “sunk capital” elsewhere.
While the other farmers, that sold right away for lower prices, got an instant return on their investment.
Even if at lower margins…
So you must be compensated for that risk, by asking higher prices, when supply is low.
And that, ladies and gentleman, is how you start building a thesis, around a a core fundamental about market behavior, and why you should be compensated for exposure.
Let’s continue with our test!