A clustering-based portfolio strategy incorporating momentum effect and market trend prediction

DOI10.1016/j.chaos.2018.10.012zbMath1442.91089OpenAlexW2897571783WikidataQ129160447 ScholiaQ129160447MaRDI QIDQ2201385

Ya-Nan Lu, Sai-Ping Li, Fei Ren, Li-Xin Zhong, Xiong-Fei Jiang

Publication date: 29 September 2020

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2018.10.012

zbMATH Keywords

cluster algorithmfinancial networkportfolio strategymomentum effectmarket trend prediction

Mathematics Subject Classification ID

Portfolio theory (91G10) Financial markets (91G15)

Cites Work

• A network-based data mining approach to portfolio selection via weighted clique relaxations
• Portfolio selection using neural networks
• Statistical analysis of financial networks
• Correlation based networks of equity returns sampled at different time horizons
• The multiplex dependency structure of financial markets
• Sparse causality network retrieval from short time series
• Multilayer feedforward networks are universal approximators
• Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock index.
• Mining market data: a network approach
• Community structure in social and biological networks
• Wavelet evolutionary network for complex-constrained portfolio rebalancing
• Comparative evaluation of genetic algorithm and backpropagation for training neural networks
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