Forecasting Asset Dependencies to Reduce Portfolio Risk


  • Haoren Zhu Hong Kong University of Science and Technology
  • Shih-Yang Liu Hong Kong University of Science and Technology
  • Pengfei Zhao BNU-HKBU United International College
  • Yingying Chen London School of Economics and Political Science
  • Dik Lun Lee Hong Kong University of Science and Technology



Data Mining & Knowledge Management (DMKM), Machine Learning (ML), Domain(s) Of Application (APP), Knowledge Representation And Reasoning (KRR)


Financial assets exhibit dependence structures, i.e., movements of their prices or returns show various correlations. Knowledge of assets’ price dependencies can help investors to create a diversified portfolio, aiming to reduce portfolio risk due to the high volatility of the financial market. Since asset dependency changes with time in complex patterns, asset dependency forecast is an essential problem in finance. In this paper, we organize pairwise assets dependencies in an Asset Dependency Matrix (ADM) and formulate the problem of assets dependencies forecast to predict the future ADM given a sequence of past ADMs. We propose a novel idea viewing a sequence of ADMs as a sequence of images to capture the spatial and temporal dependencies among the assets. Inspired by video prediction tasks, we develop a novel Asset Dependency Neural Network (ADNN) to tackle the ADM prediction problem. Experiments show that our proposed framework consistently outperforms baselines on both future ADM prediction and portfolio risk reduction tasks.




How to Cite

Zhu, H., Liu, S.-Y., Zhao, P., Chen, Y., & Lee, D. L. (2022). Forecasting Asset Dependencies to Reduce Portfolio Risk. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4397-4404.



AAAI Technical Track on Data Mining and Knowledge Management