Is Bitcoin’s Market Maturing? Cumulative Abnormal Returns and Volatility in the 2024 Halving and Past Cycles
Bibliographic record
Abstract
This study examines how cumulative abnormal returns (CARs, the sum of abnormal returns over a period) and volatility behave around Bitcoin halving events, focusing on whether these patterns have evolved as the cryptocurrency market matures. Halvings are periodic events defined by Bitcoin’s algorithm, during which the reward—in the form of newly issued bitcoins—paid to miners for validating network transactions is reduced, impacting miners’ profitability and potentially influencing the asset’s price due to a decreased supply. To carry out the analysis, we collected data on returns and risk for the 2012, 2016, 2020, and 2024 halving events and compared abnormal returns before and around the event, focusing on the 2020 and 2024 halvings. The results reveal significant shifts in Bitcoin’s price behavior within the event window, with an increased occurrence of abnormal returns in 2020 and 2024, alongside variations in average return, volatility, and maximum drawdown across all events. These findings suggest that Bitcoin’s returns and volatility during halvings are decreasing as the cryptocurrency market becomes more regulated and attracts greater participation from institutional investors and governments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".