Jakub Zwydak, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
arXiv 15 Jul 2026 · Finance — Trading · publishedEntropy (2026)
arXiv:2607.13916 · PDF · DOI · OpenAlex · Extracted main text
Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from April 1 to June 30, 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | S. Drożdż and J. Kwapień and M. W atorek (2023) What is mature and what is still emerging in the cryptocurrency market? | 0.928 | 4 | 3 | 100% |
| 2 | Drożdż, Stanisław and Kluszczyński, Robert and Kwapień, Jarosław and… (2025) Multifractality and its sources in the digital currency market self | 0.811 | 4 | 2 | 100% |
| 3 | Zhi Qiang Jiang and Wen Jie Xie and Wei Xing Zhou and Didier Sornette (2019) Multifractal analysis of financial markets: A review | 0.811 | 4 | 2 | 100% |
| 4 | Amiram, Dan and Lyandres, Evgeny and Rabetti, Daniel (2025) Trading Volume Manipulation and Competition Among Centralized Crypto Exchanges | 0.737 | 3 | 2 | 100% |
| 5 | Cong, Lin William and Li, Xi and Tang, Ke and Yang, Yang (2023) Crypto Wash Trading | 0.737 | 3 | 2 | 100% |
| 6 | Cont, Rama (2001) Empirical properties of asset returns: stylized facts and statistical issues | 0.737 | 3 | 2 | 100% |
| 7 | Richman, Joshua S. and Moorman, J. Randall (2000) Physiological time-series analysis using approximate entropy and sample entropy | 0.737 | 3 | 2 | 100% |
| 8 | M. W atorek and S. Drożdż and J. Kwapień and Ludovico Minati and P.… (2021) Multiscale characteristics of the emerging global cryptocurrency market | 0.737 | 3 | 2 | 100% |
| 9 | J.-P. Bouchaud (2010) Price impact | 0.644 | 2 | 2 | 100% |
| 10 | Jialan Chen and Dan Lin and Jiajing Wu (2022) Do cryptocurrency exchanges fake trading volumes? An empirical analysis of wash trading based on data mining | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 91 scored citations.