Dynamic Portfolio Optimization with Inverse Covariance Clustering

Wang, Yuanrong; Aste, Tomaso

doi:10.2139/ssrn.4092344

Cited by 2 publications

(1 citation statement)

References 119 publications

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“…At the finest granular level, in finance, modeling the levels of limit order book is a multivariate problem for high-frequency trading with the aim of mid-price prediction [7,8]. For longer-term investment like portfolio management [37,60], prices of assets in a portfolio are usually multivariate time-series. Multivariate time series forecasting methods assume inter-dependencies among dynamically changing variables, which captures systematic trends.…”

Section: Introductionmentioning

confidence: 99%

Network Filtering of Spatial-temporal GNN for Multivariate Time-series Prediction

Wang

Aste

2022

Proceedings of the Third ACM International Conference on AI in Finance

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We propose an architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a filtering module which filters the inverse correlation matrix into a sparse network structure. In contrast with existing sparsification methods adopted in graph neural networks, our model explicitly leverages time-series filtering to overcome the low signal-to-noise ratio typical of complex systems data. We present a set of experiments, where we predict future sales volume from a synthetic time-series sales volume dataset. The proposed spatial-temporal graph neural network displays superior performances to baseline approaches with no graphical information, fully connected, disconnected graphs, and unfiltered graphs, as well as the state-of-the-art spatial-temporal GNN. Comparison of the results with Diffusion Convolutional Recurrent Neural Network (DCRNN) suggests that, by combining a (inferior) GNN with graph sparsification and filtering, one can achieve comparable or better efficacy than the state-of-the-art in multivariate time-series regression. CCS CONCEPTS• Computing methodologies → Machine learning approaches.

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Section: Introductionmentioning

confidence: 99%

Network Filtering of Spatial-temporal GNN for Multivariate Time-series Prediction

Wang

Aste

2022

Proceedings of the Third ACM International Conference on AI in Finance

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Dependency Structures in Cryptocurrency Market from High to Low Frequency

Briola

Aste

2022

Entropy

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We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones.

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Dynamic Portfolio Optimization with Inverse Covariance Clustering

Cited by 2 publications

References 119 publications

Network Filtering of Spatial-temporal GNN for Multivariate Time-series Prediction

Network Filtering of Spatial-temporal GNN for Multivariate Time-series Prediction

Dependency Structures in Cryptocurrency Market from High to Low Frequency

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