Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns

TL;DR

This paper introduces a novel deep learning framework that models financial asset dependencies as image sequences to predict their dynamics, aiding risk management and portfolio diversification.

Contribution

It proposes the Asset Dependency Neural Network (ADNN) using ConvLSTM to effectively capture and forecast complex asset dependency patterns.

Findings

ADNN outperforms baseline models in dependency prediction

Improves downstream financial application performance

Effectively models non-natural order asset dependencies

Abstract

Financial assets exhibit complex dependency structures, which are crucial for investors to create diversified portfolios to mitigate risk in volatile financial markets. To explore the financial asset dependencies dynamics, we propose a novel approach that models the dependencies of assets as an Asset Dependency Matrix (ADM) and treats the ADM sequences as image sequences. This allows us to leverage deep learning-based video prediction methods to capture the spatiotemporal dependencies among assets. However, unlike images where neighboring pixels exhibit explicit spatiotemporal dependencies due to the natural continuity of object movements, assets in ADM do not have a natural order. This poses challenges to organizing the relational assets to reveal better the spatiotemporal dependencies among neighboring assets for ADM forecasting. To tackle the challenges, we propose the Asset Dependency Neural Network (ADNN), which employs the Convolutional Long Short-Term Memory (ConvLSTM) network, a highly successful method for video prediction. ADNN can employ static and dynamic transformation functions to optimize the representations of the ADM. Through extensive experiments, we demonstrate that our proposed framework consistently outperforms the baselines in the ADM prediction and downstream application tasks. This research contributes to understanding and predicting asset dependencies, offering valuable insights for financial market participants.