Exploring the Potential of Graph-based Models in Financial Data Analysis

My research aims to explore the use of graph-based models for financial data analysis.

Financial systems are complex and interconnected, and traditional methods based on time series analysis may not be sufficient to fully understand and make accurate predictions about these systems.

By using graph-based methods, we can better incorporate the connectivity and dependence of companies and events in a financial system, leading to more accurate predictions. One aspect of my research will be to study the dynamics of financial networks and how they change over time. This is important for understanding the underlying functions of financial systems and detecting critical states.

I will also examine the use of node functions and transformations in network models, as nodes in a financial system may perform actions on value, such as funds, collateral, and assets.

In addition to network science approaches, I will also explore the use of graph neural network (GNN) algorithms for financial data analysis.

GNNs have the advantage of being able to frame the feature extraction process as a learning task, rather than relying on manually chosen indicators or inflexible handcrafted features.

GNNs have been shown to be effective for tasks such as stock movement prediction, loan default risk prediction, event prediction, and fraud detection.

However, there are still some limitations to current GNN architectures, such as their reliance on pre-defined graph structures and difficulty in interpretation.

I will work on developing novel approaches that address these limitations and make GNNs more applicable for financial tasks.

In conclusion, my research has the potential to make a significant contribution to the field of financial data analysis by developing more accurate and interpretable models.

It has the potential to benefit society by improving financial market predictions and helping to make informed investment decisions.

It may also challenge current assumptions and approaches in the field by introducing new methods for studying financial systems.