Detecting market manipulation with dual-branch self-supervised learning: A unified framework integrating frequency-informed anomaly synthesis and domain-specific features

Effective detection of financial market manipulation is critically impeded by three fundamental challenges: signal concealment, data sparsity, the boundary vagueness. This paper introduces SD-FMM , a S elf-supervised D etection framework tailored for F inancial M arket M anipulation that addresses these fundamental challenges through three innovative components. First, our Amplification Component extracts and fuses domain-specific features grounded in market microstructure theory, substantially amplifying subtle manipulation signals that would otherwise remain concealed. Second, our Synthesis Component generates realistic synthetic anomalies through few-shot learning and dynamic frequency analysis using Discrete Wavelet Transform, enabling self-supervised training without relying on scarce labeled data. Third, our Detection Component employs a novel Dual-branch Contrastive Detection Neural Network that enhances sensitivity to manipulation boundaries through local contrastive learning and holistic modeling of temporal dependency. We evaluate SD-FMM using a newly collected proprietary dataset of 25 Chinese stock market manipulation cases and a public benchmark of 338 cryptocurrency pump-and-dump schemes. Extensive experiments against 12 state-of-the-art baselines demonstrate the significant superiority of SD-FMM. On the stock dataset, our method outperforms the second-best baseline by 47.61% in average precision metrics and reduces the false alarm rate by 47.46%. Meanwhile, it shortens the mean detection delay by 25.05%, enabling swift regulatory intervention. On the cryptocurrency dataset, SD-FMM exhibits remarkable sensitivity, achieving a Hit Rate@3 of 83.13% and Hit Rate@20 of 97.93%. Overall, our framework offers a generalized solution that can not only accurately distinguish manipulations from normal trading but also deliver a faster and stronger response to manipulations across diverse financial markets.