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Yang, Y. and Hospedales, T.M. (2023) An Evaluation of Self-Supervised Learning for Portfolio Diversification. In: Iliadis, L., Papaleonidas, A., Angelov, P. and Jayne, C., Eds., Artificial Neural Networks and Machine Learning—ICANN 2023, Springer, 283-294. https://doi.org/10.1007/978-3-031-44213-1_24

has been cited by the following article:

• TITLE: Hybrid Data-Driven and Deep Learning Based Portfolio Optimization

  AUTHORS: Joy Dip Das, Sulalitha Bowala, Ruppa K. Thulasiram, Aerambamoorthy Thavaneswaran

  KEYWORDS: BiLSTM, BiGRU, Affinity Propagation, Diversification, Data-Driven Algorithm, Portfolio Optimization

  JOURNAL NAME: Journal of Mathematical Finance, Vol.14 No.3, June 24, 2024

  ABSTRACT: This research introduces a novel hybrid architecture that combines deep learning, data-driven algorithms, and an affinity propagation-based approach to build robust investment portfolios. This study evaluates the efficacy of BiLSTM and BiGRU in constructing resilient portfolios of stocks from diverse sectors under varying market conditions. The results highlight the superior performance of BiGRU, particularly in dynamic and volatile market scenarios. The research emphasizes the importance of precise stock prediction and effective diversification for building resilient portfolios, leveraging advanced techniques from deep learning and data-driven optimization. Comparative analyses indicate similar performance between portfolios constructed with actual and predicted data using data-driven optimization. The findings offer valuable insights into constructing robust portfolios by employing advanced techniques, thereby enhancing decision-making in financial markets.