254 research outputs found
Detecting Financial Market Manipulation with Statistical Physics Tools
We take inspiration from statistical physics to develop a novel conceptual
framework for the analysis of financial markets. We model the order book
dynamics as a motion of particles and define the momentum measure of the system
as a way to summarise and assess the state of the market. Our approach proves
useful in capturing salient financial market phenomena: in particular, it helps
detect the market manipulation activities called spoofing and layering. We
apply our method to identify pathological order book behaviours during the
flash crash of the LUNA cryptocurrency, uncovering widespread instances of
spoofing and layering in the market. Furthermore, we establish that our
technique outperforms the conventional Z-score-based anomaly detection method
in identifying market manipulations across both LUNA and Bitcoin cryptocurrency
markets
Joint Event Extraction via Structural Semantic Matching
Event Extraction (EE) is one of the essential tasks in information
extraction, which aims to detect event mentions from text and find the
corresponding argument roles. The EE task can be abstracted as a process of
matching the semantic definitions and argument structures of event types with
the target text. This paper encodes the semantic features of event types and
makes structural matching with target text. Specifically, Semantic Type
Embedding (STE) and Dynamic Structure Encoder (DSE) modules are proposed. Also,
the Joint Structural Semantic Matching (JSSM) model is built to jointly perform
event detection and argument extraction tasks through a bidirectional attention
layer. The experimental results on the ACE2005 dataset indicate that our model
achieves a significant performance improvemen
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