2 Search Results for "Yang, Ke"

Document
Extended Abstract
DOI: 10.4230/OASIcs.Tokenomics.2021.10
Detecting and Quantifying Crypto Wash Trading (Extended Abstract)

Authors: Lin William Cong, Xi Li, Ke Tang, and Yang Yang

Published in: OASIcs, Volume 97, 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)

Abstract
We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges. Regulated exchanges feature patterns consistently observed in financial markets and nature; abnormal first-significant-digit distributions, size rounding, and transaction tail distributions on unregulated exchanges reveal rampant manipulations unlikely driven by strategy or exchange heterogeneity. We quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and userbase), market conditions, and regulation.
Cite as

Lin William Cong, Xi Li, Ke Tang, and Yang Yang. Detecting and Quantifying Crypto Wash Trading (Extended Abstract). In 3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021). Open Access Series in Informatics (OASIcs), Volume 97, pp. 10:1-10:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{cong_et_al:OASIcs.Tokenomics.2021.10,
  author =	{Cong, Lin William and Li, Xi and Tang, Ke and Yang, Yang},
  title =	{{Detecting and Quantifying Crypto Wash Trading}},
  booktitle =	{3rd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2021)},
  pages =	{10:1--10:6},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-220-4},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{97},
  editor =	{Gramoli, Vincent and Halaburda, Hanna and Pass, Rafael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.Tokenomics.2021.10},
  URN =		{urn:nbn:de:0030-drops-159072},
  doi =		{10.4230/OASIcs.Tokenomics.2021.10},
  annote =	{Keywords: Bitcoin, Cryptocurrency, FinTech, Forensic Finance, Fraud Detection, Regulation}
}
Document
DOI: 10.4230/LIPIcs.FORC.2021.7
Causal Intersectionality and Fair Ranking

Authors: Ke Yang, Joshua R. Loftus, and Julia Stoyanovich

Published in: LIPIcs, Volume 192, 2nd Symposium on Foundations of Responsible Computing (FORC 2021)

Abstract
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used in many contexts, ranging from Web search to college admissions, but causal inference for fair rankings has received limited attention. Additionally, the growing literature on causal fairness has directed little attention to intersectionality. By bringing these issues together in a formal causal framework we make the application of intersectionality in algorithmic fairness explicit, connected to important real world effects and domain knowledge, and transparent about technical limitations. We experimentally evaluate our approach on real and synthetic datasets, exploring its behavior under different structural assumptions.
Cite as

Ke Yang, Joshua R. Loftus, and Julia Stoyanovich. Causal Intersectionality and Fair Ranking. In 2nd Symposium on Foundations of Responsible Computing (FORC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 192, pp. 7:1-7:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

Copy BibTex To Clipboard

@InProceedings{yang_et_al:LIPIcs.FORC.2021.7,
  author =	{Yang, Ke and Loftus, Joshua R. and Stoyanovich, Julia},
  title =	{{Causal Intersectionality and Fair Ranking}},
  booktitle =	{2nd Symposium on Foundations of Responsible Computing (FORC 2021)},
  pages =	{7:1--7:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-187-0},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{192},
  editor =	{Ligett, Katrina and Gupta, Swati},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2021.7},
  URN =		{urn:nbn:de:0030-drops-138756},
  doi =		{10.4230/LIPIcs.FORC.2021.7},
  annote =	{Keywords: fairness, intersectionality, ranking, causality}
}
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