Pith. sign in

REVIEW 2 cited by

Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2311.17715 v3 pith:RTGPJNKA submitted 2023-11-29 q-fin.CP

classification q-fin.CP
keywords defimisconductmarketregulatoryfinanceformsanalysisblockchain
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Technological advancement drives financial innovation, reshaping the traditional finance landscape and redefining user-market interactions. The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology and finance. While DeFi has introduced exciting opportunities, it has also exposed the ecosystem to new forms of market misconduct. This paper aims to bridge the academic and regulatory gaps by addressing key research questions about market misconduct in DeFi. We begin by discussing how blockchain technology can potentially enable the emergence of novel forms of market misconduct. We then offer a comprehensive definition and taxonomy for understanding DeFi market misconduct. Through comparative analysis and empirical measurements, we examine the novel forms of misconduct in DeFi, shedding light on their characteristics and social impact. Subsequently, we investigate the challenges of building a tailored regulatory framework for DeFi. We identify key areas where existing regulatory frameworks may need enhancement. Finally, we discuss potential approaches that bring DeFi into the regulatory perimeter.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ManiScope: LLM-Assisted Visual Analytics of Cryptocurrency Manipulation Risk

    cs.HC 2026-07 conditional novelty 6.0 of 10

    An LLM co-analyst plus coordinated crypto views can reduce manual evidence-seeking and organize manipulation-risk findings around user hypotheses in a 12-person practitioner study.

  2. From Rules to Rewards: Reinforcement Learning for Interest Rate Adjustment in DeFi Lending

    cs.LG 2025-05 reject novelty 4.0 of 10

    An offline RL policy (TD3-BC) is claimed to beat Aave's rule-based rates on responsiveness, lender returns, and stress response, but the evidence is limited to historical replay.

Pith tools