Introduction
Cryptocurrency markets have matured into a definite asset class characterised by excessive volatility, deep liquidity swimming pools, and worldwide retail participation. Conventional fairness and commodity markets exhibit a well-documented pre-holiday impact, the place returns on buying and selling days instantly previous public holidays are inclined to outperform different days. Provided that Bitcoin is commonly described because the archetypal absolute danger asset, it’s pure to hypothesize that any calendar-driven anomalies noticed in equities ought to manifest—and even amplify—in crypto markets.
Nonetheless, not like fairness markets, the place institutional buyers and advertising and marketing calendars drive collective conduct, crypto markets are extra dispersed, retail-dominated, and influenced by nontraditional data flows. This text investigates whether or not the traditional pre-holiday impact applies to Bitcoin and assesses the extent to which it may be amplified by an attention-grabbing momentum filter primarily based on native worth highs.
Background
The pre-holiday impact was first documented by Ariel (1990), who confirmed that U.S. fairness markets earn abnormally excessive returns on buying and selling days instantly previous public holidays. Subsequent work by Kim and Park (1994) confirmed this sample throughout the NYSE, AMEX, NASDAQ, FT30 (U.Ok.), and Nikkei Dow (Japan), demonstrating the anomaly’s persistence beneath differing institutional preparations. Quantitative analysis platforms, resembling us at Quantpedia, classify the pre-holiday anomaly as probably the most substantial calendar results, reporting that common returns on pre-holiday days could be greater than ten instances bigger than on common buying and selling days. Hansen and Lunde (2003) developed a rigorous testing framework that circumstances on the whole set of attainable calendar dummies to keep away from data-mining biases, they usually present this strategy retains good energy by exploiting the particular correlation construction of calendar results.
Behavioral finance casts calendar anomalies as departures from market effectivity pushed by sentiment and a spotlight biases. Fama (1998) characterizes what’s now often known as the anomalies literature, highlighting how phenomena just like the pre-holiday impact contradict the semi-strong type of the environment friendly market speculation.
Methodology & Information
Information span every day closing costs of Bitcoin within the type of BITO ETF from January 2018 to June 2025, sourced from our inside database (2018-2021 as futures proxy, 2021 – 2025 as ETF itself). Why did we use BITO ETF (which tracks the bitcoin futures) and never spot Bitcoin ETFs like IBIT, GBTC, or FBTC, or the spot Bitcoin itself? As we repeatedly said, we contemplate solely knowledge from 2018 onward to be related for the backtesting functions (because the introduction of the regulated Bitcoin futures) and mixture of the Bitcoin futures knowledge and BITO ETF offers the lengthy historic window over which we will run our checks.
Public vacation dates are outlined by a consensus U.S. calendar, as per Time and Date (see instance for 2025), acknowledging that U.S. holidays typically set the tone for all international fairness markets, thus hypothetically influencing international crypto sentiment.
Whereas ProShares Bitcoin ETF (BITO) is traded on U.S. exchanges, it can’t be traded on D0, which falls on the day of a vacation. Therefore, we all the time maintain lengthy Bitcoin positions in the course of the holidays, hoping to capitalize on attention-grabbing occasions.
Public Vacation Window Evaluation
To begin, we look at buying and selling interval beginning 5 buying and selling days earlier than every vacation (D–5) and ending 5 buying and selling days after (D+5).
Following is the histogram (bar chart) exhibiting the every day distribution of returns, within the talked about days previous and after the vacation:
Image 1: Vacation drift in Bitcoin
This charts visualizes whether or not there may be any pre-holiday drift and can assist us to organize fundamental seasonal technique that can span a number of days.
Following that preliminary evaluation, we will outline a easy buying and selling technique that buys crypto on the day previous the vacation (D-1), holds by way of the vacation, and liquidates on the shut of the day after the vacation (D+1). And right here is the fairness curve, which exhibits the appreciation of the preliminary funding for such a technique:
Image 2: Vacation drift in Bitcoin – D+1 do D-1, fairness curve
Nothing extraordinary, proper? Whereas this technique yields some optimistic returns, it’s not very passable and tends to be flat for more often than not. Due to this fact, we have to search for one other element to reinforce the technique’s returns.
Consideration-Augmented Technique
Primarily based on the primary easy technique, we hypothesize that coupling the straightforward vacation drift with one other attention-grabbing anomaly will yield considerably higher outcomes. And what may these 2nd anomaly be? Our favourite 10-day excessive technique. Why ought to we mix these two anomalies and hope for higher returns? Round holidays, retail merchants sometimes have extra free time and are extra inclined to have interaction with monetary markets out of curiosity and even boredom. If, throughout this era, crypto costs are already breaking to new short-term highs, the extra consideration and speculative exercise from retail members can act as gas for additional worth will increase. In essence, the mixture of a bullish technical sign and heightened retail exercise round holidays can exacerbate upward strikes, making a repeatable and doubtlessly worthwhile buying and selling sample.
We thus outline an N-day excessive filter: a day on which Bitcoin’s closing worth exceeds the utmost closing worth of the previous N buying and selling days (N ∈ {5, 10, 20}). Then we determine dates which might be concurrently inside the vacation window (D–5 to D+5) and fulfill the N-day excessive situation. For every qualifying date, one enters an extended place on the shut and liquidates on the subsequent buying and selling day’s shut.
The next are the outcomes from such an investigation, first within the type of histograms depicting the distribution of outcomes for every thought-about day alone:
Image 3: 5-day excessive filter + Vacation drift
Image 4: 10-day excessive filter + Vacation drift
Image 5: 20-day excessive filter + Vacation drift
That’s a major distinction in comparison with the histogram exhibiting simply the pre-holiday impact with out the 10-day excessive filter. It actually appears that the boredom of the vacations helps cryptocurrency hypothesis. The entire interval between D-5 and D+1 has considerably optimistic common every day returns.
Technique Analysis
Let’s consider they fairness curves themselves. We in the end selected window D-5 to D+1 and listed below are the outcomes:
Image 6: Cumulative fairness curves for every N-day excessive filter + Pre-holiday Drift (D-5 to D+1)
Efficiency and danger metrics (compound annual return, annualized [yearly] volatility, Sharpe ratio, most drawdown, and CAR / most drawdown) could be discovered within the desk under:
1 Also referred to as the Calmar Ratio.
One of the best-performing 5-day excessive variant model comes at a value with a tradeoff of the worst danger phrases, together with the very best volatility and most drawdown from the examined pattern. Nonetheless, the Sharpe ratio over 2.0 and Calmar ratio over 7.0 (for the 10-day variant) look actually engaging for us. It actually pays off to affix the speculating crowd infrequently…
Dialogue & Conclusion
Our findings affirm that Bitcoin reveals a pre-holiday drift akin to that noticed in fairness markets, however solely when coupled with a short-term momentum set off. The N-day excessive filter serves as a proxy for heightened market consideration, capturing cases by which retail and institutional members concurrently face discretionary buying and selling time and optimistic suggestions loops. This synergy yields sturdy returns with engaging risk-adjusted profiles.
These outcomes underscore the significance of mixing calendar-based alerts with behavioral proxies in absolute danger belongings. They counsel that crypto markets will not be resistant to the herding and sentiment biases that characterize conventional markets; the truth is, these biases could also be magnified given the asset’s speculative nature and retail focus. As soon as the N-day excessive filter is utilized, the technique’s efficiency improves markedly. The filtered vacation technique is performing higher than the unfiltered one, demonstrating the compounding impact of calendar timing and momentum screening.
Creator: Cyril Dujava, Quant Analyst, Quantpedia
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