Exploring the Day of the Week Effect in CryptocurrenciesHey, crypto traders and enthusiasts! Have you ever wondered if the day of the week could sway your crypto earnings?
Well, a fascinating study from 2019 titled "The day of the week effect in the cryptocurrency market" by Guglielmo Maria Caporalea and Alex Plastunb decided to dive into this question.
The study zeroes in on some of the big names in crypto - Bitcoin, LiteCoin, Ripple, and Dash. Caporalea et al. decided to analyze data from 2013 to 2017. Here’s the result:
The key takeaway from the research is that Bitcoin exhibits a unique pattern of higher returns on Mondays compared to other days, thus showing the “Day of the Week effect.” This anomaly was, however, not observed in other major cryptocurrencies like LiteCoin, Ripple, and Dash.
While a trading strategy based on this Monday effect in Bitcoin shows profit potential, it's important to approach it cautiously. There’s a lot of variability in annual results and a lack of consistency in the broader cryptocurrency market.
█ The Day of the Week Effect
It’s like a financial urban legend – stocks behave differently depending on the day of the week. Weird, right? But there's some solid data behind it. For example, it's been observed that Mondays often bring the blues with lower returns, while Fridays can feel like a party with higher ones. Think of it as the market's weekly routine, just like some of us are sluggish on Mondays and supercharged by Fridays.
So, why does this happen? There are a bunch of theories.
Some say it's because of the accumulation of news over the weekend that investors react to on Monday. Others argue it's due to the trading patterns of institutional investors. It's still a bit of a mystery, which makes it so intriguing.
█ Methodology
So, how did the researchers determine if cryptocurrencies have a favorite day of the week?
The Data Dive
The team sifted through daily data from 2013 to 2017 for Bitcoin, LiteCoin, Ripple, and Dash, provided by CoinMarketCap. That's a hefty chunk of info, covering years of ups, downs, and sideways movements. Why these years, you ask? This period saw some of the most significant developments in crypto, making it a gold mine for data analysts.
Crunching the Numbers
The study used a mix of average analysis and more complex statistical tests to scrutinize the data.
Average Analysis: This essentially looks at the mean returns for each day of the week across the data set. It's like zooming out to see the broader pattern.
For example, they calculated the average return for Bitcoin on Mondays, Tuesdays, and so on, then compared these averages to see if any day stood out.
Parametric Tests:
Student's t-test: Used to compare the means between two groups. In this context, it might be used to compare the average Monday returns to the average Tuesday returns for Bitcoin. It assumes that the data follows a normal distribution, a common assumption for large data sets.
ANOVA (Analysis of Variance): This test steps it up a notch. It's used when you have more than two groups to compare – like comparing the average returns of all weekdays. ANOVA checks if there's a statistically significant difference between these group means.
Non-Parametric Tests:
Kruskal–Wallis Test: This is the non-parametric counterpart to ANOVA. It's used when you can't assume the data is normally distributed. It ranks the data and compares these ranks across groups. For example, it would rank all the Monday returns, Tuesday returns, and so on, then see if these rankings differ significantly across the week.
Mann–Whitney Test: Similar to the Student's t-test but for non-parametric data. It compares two independent groups, like Monday versus Wednesday returns, without assuming a normal distribution.
These parametric and non-parametric tests provide a rigorous examination of the data. They help determine if there are differences in returns on different days of the week and whether these differences are statistically significant and not just due to chance.
█ Findings and Analysis
In the study focusing on the day-of-the-week effect in the cryptocurrency market, the key findings were:
⚪ Bitcoin's Monday Effect:
A notable trend was observed in Bitcoin, where it displayed consistently higher returns on Mondays than on other days. This suggests a unique pattern where the beginning of the week seems more favorable for Bitcoin investments.
A trading simulation based on this Monday effect indicated a 60% chance of generating a profit from 2013 to 2017. This highlights the potential profitability of adapting trading strategies to capitalize on this pattern.
⚪ Other Cryptocurrencies:
The study found no significant day-of-the-week effect in other major cryptocurrencies like LiteCoin, Ripple, and Dash. Their returns did not show a consistent pattern tied to specific weekdays, indicating more randomness in their daily returns.
These findings imply that while Bitcoin exhibits a distinct weekly return pattern, other major cryptocurrencies do not follow the same trend.
This insight could be pivotal for traders focusing on Bitcoin, suggesting reevaluating trading strategies around weekends and Mondays. However, the lack of a consistent pattern suggests a need for different trading approaches for other cryptocurrencies.
█ Conclusion
The big takeaway is that Bitcoin shows a unique 'day of the week' effect, particularly on Mondays, but this isn't a universal crypto trend.
For your trading strategies, this means there's potential in timing your Bitcoin moves, but always keep an eye on the broader market dynamics. Remember, the crypto world is fast-paced and ever-changing, so continual research and analysis are your best friends.
Stay curious, stay informed, and who knows what other patterns you might uncover!
█ Reference
Caporale, G. M., & Plastun, A. (2019). The day of the week effect in the cryptocurrency market. Finance Research Letters, 31, 258-269.
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Disclaimer
Our results are approximate. We encourage you to test the assumption yourself. We do not guarantee that you will get the same results. This is an educational study for entertainment purposes only.
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