Temat: cryptocurrency markets

Skocz do pozycji: 1.

Tytuł:: Cross-Sectional Interactions in Cryptocurrency Returns
Autorzy:: Będowska-Sójka, Barbara
Mercik, Aleksander
Karim, Sitara
Zaremba, Adam
Wydawca:: Elsevier
Cytata wydawnicza:: Mercik, A., Będowska-Sójka, B., Karim, S., & Zaremba, A. (2025). Cross-sectional interactions in cryptocurrency returns. International Review of Financial Analysis, 97, 103809. https://doi.org/10.1016/j.irfa.2024.103809
Opis:: We investigate interaction effects in cryptocurrency markets by constructing and evaluating double-sorted portfolios based on 40 different characteristics. Using a dataset of over 500 major coins and tokens from 2017 to 2023, we identify numerous significant interactions. The most pronounced effects arise from the interplay of liquidity, risk, and past return measures. An out-of-sample long-short strategy that selects the top and bottom interactions achieves a Sharpe ratio exceeding 1. However, network graph analysis and additional tests reveal that low liquidity, which raises transaction costs, can dampen trading activity and contribute to the persistence of these anomalies.
This work is supported by the National Science Centre in Poland through the project "Cross-Sectional Properties of Cryptocurrency Returns", no. 2021/41/B/HS4/02443" and COST Action CA19130 "Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry". We also acknowledge the support of the Marie Skłodowska-Curie Actions under the European Union’s Horizon Europe research and innovation program for the Industrial Doctoral Network on Digital Finance, acronym: DIGITAL, Project No. 101119635, within the Diversity Team.
Dostawca treści:: Repozytorium Centrum Otwartej Nauki

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Skocz do pozycji: 2.

Tytuł:: Cryptocurrency anomalies and economic constraints
Autorzy:: Fieberg, Christian
Zaremba, Adam
Liedtke, Gerrit
Wydawca:: Elsevier
Cytata wydawnicza:: Fieberg, C., Liedtke, G., & Zaremba, A. (2024). Cryptocurrency anomalies and economic constraints. International Review of Financial Analysis, 94, 103218. https://doi.org/10.1016/j.irfa.2024.103218
Opis:: National Science Center of Poland [Grant no. 2021/41/B/HS4/02443]
The asset pricing literature documents a growing list of predictable patterns in the cross-section of cryptocurrency returns. But can they be forged into viable trading profits? We answer this question by examining the interplay between economic restrictions and return predictability in cryptocurrency markets. We find that size and volume anomalies originate from micro-cap coins of negligible economic importance. Conversely, the momentum effect prevails in larger cryptocurrencies but incurs substantial trading costs and extracts alphas largely from short positions. Most abnormal returns occur primarily in bull markets and fade over time. Therefore, protocols for identifying tradable cryptocurrency anomalies should focus on long positions, account for transaction costs, consider hard-to-trade coins, and emphasize performance in recent years.
Dostawca treści:: Repozytorium Centrum Otwartej Nauki

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Skocz do pozycji: 3.

Tytuł:: Machine learning and the cross-section of cryptocurrency returns
Autorzy:: Cakici, Nusret
Będowska-Sójka, Barbara
Shahzad, Syed Jawad Hussain
Zaremba, Adam
Wydawca:: Elsevier
Cytata wydawnicza:: Cakici, N., Shahzad, S. J. H., Będowska-Sójka, B., & Zaremba, A. (2024). Machine learning and the cross-section of cryptocurrency returns. International Review of Financial Analysis, 94, 103244. https://doi.org/10.1016/j.irfa.2024.103244
Opis:: National Science Center of Poland [Grant No. 2021/41/B/HS4/02443]
We employ a repertoire of machine learning models to investigate the cross-sectional return predictability in cryptocurrency markets. While all methods generate substantial economic gains—unlike in other asset classes—the benefits from model complexity are limited. Return predictability derives mainly from a handful of simple characteristics, such as market price, past alpha, illiquidity, and momentum. Contrary to the stock market, abnormal returns in cryptocurrencies originate from the long leg of the trade and persist over time. Furthermore, despite high portfolio turnover, most machine learning strategies remain profitable after trading costs. However, alphas are concentrated in hard-to-trade assets and critically depend on harvesting extreme returns on small, illiquid, and volatile coins.
Dostawca treści:: Repozytorium Centrum Otwartej Nauki

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Skocz do pozycji: 4.

Tytuł:: Non-standard errors in the cryptocurrency world
Autorzy:: Fieberg, Christian
Poddig, Thorsten
Günther, Steffen
Zaremba, Adam
Wydawca:: Elsevier
Cytata wydawnicza:: Fieberg, C., Günther, S., Poddig, T., & Zaremba, A. (2024). Non-standard errors in the cryptocurrency world. International Review of Financial Analysis, 92, 103106. https://doi.org/10.1016/j.irfa.2024.103106
Opis:: National Science Center of Poland, Grant No. 2021/41/B/HS4/02443
Motivated by recent findings from the equity market, we investigate non-standard errors in cryptocurrency research. We examine ten prevalent decisions related to data sources, sample preparation, and portfolio construction, generating 20,736 research designs for 43 sorting variables. Our findings reveal remarkable variation in portfolio performance tied to seemingly trivial choices. The non-standard errors in cryptocurrency studies not only surpass those in the stock market but also clearly exceed standard errors—though varying considerably across coin characteristics. Notwithstanding the above, the most prominent cryptocurrency factors, such as size and momentum, remain consistently robust across numerous specifications. Lastly, we find that reducing the influence of the smallest coins effectively decreases the non-standard errors.
Dostawca treści:: Repozytorium Centrum Otwartej Nauki

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