Discussing the article: "Neural Networks in Trading: Hybrid Graph Sequence Models (GSM++)"
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Check out the new article: Neural Networks in Trading: Hybrid Graph Sequence Models (GSM++).
In recent years, graph transformers adapted from natural language processing and computer vision domains have attracted particular attention. Their ability to model long-range dependencies and efficiently handle irregular financial structures makes them a promising tool for tasks such as volatility forecasting, market anomaly detection, and the construction of optimal investment strategies. However, classical transformers face a number of fundamental challenges, including high computational costs and difficulties in adapting to unordered graph structures.
The authors of "Best of Both Worlds: Advantages of Hybrid Graph Sequence Models" propose a unified Graph Sequence Model, GSM++, which combines the strengths of various architectures to create an effective method for representing and processing graphs. The model is built around three key stages: graph tokenization, local node encoding, and global dependency encoding. This approach allows the model to capture both local and global relationships in financial systems, making it versatile and applicable to a wide range of tasks.
A core component of the proposed model is the hierarchical graph tokenization method, which transforms market data into a compact sequential representation while preserving its topological and temporal features. Unlike standard time series encoding methods, this approach improves feature extraction quality and simplifies the processing of large volumes of market data. Combining hierarchical tokenization with a hybrid architecture, that includes transformer and recurrent mechanisms, yields superior performance across multiple tasks. This makes the method a powerful tool for handling complex financial datasets.
Author: Dmitriy Gizlyk