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Innovative deep matching algorithm for stock portfolio selection using deep stock profiles.

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This study introduces a novel deep learning model for stock portfolio selection, transforming it into a matching problem. The approach effectively handles noisy financial data, outperforming existing methods in risk-adjusted returns.

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Area of Science:

  • Quantitative Finance
  • Machine Learning
  • Financial Data Analysis

Background:

  • Constructing reliable stock portfolios is a persistent challenge in quantitative investment.
  • Existing machine learning models struggle with financial data's low signal-to-noise ratio, time-series nature, and non-independent distribution.
  • Practical application of quantitative investment models is limited by data complexities.

Purpose of the Study:

  • To develop a novel deep learning framework for stock portfolio selection.
  • To address the limitations of existing models in handling financial data characteristics.
  • To improve the performance of stock selection and portfolio strategies.

Main Methods:

  • Transformed stock selection into a stock-to-target matching problem.
  • Developed a novel representation algorithm for stock selection targets.
  • Proposed a deep matching algorithm (TS-Deep-LtM) integrating three text matching algorithms.
  • Utilized a deep stock profiling method for optimal feature extraction.

Main Results:

  • The TS-Deep-LtM algorithm demonstrated effectiveness in capturing time-series signals and adapting to non-independent data.
  • The proposed model was applied to stock selection, testing long-only portfolio strategies from 2010-2017.
  • Portfolio strategies significantly outperformed the CSI300 index and learning-to-rank approaches in risk-adjusted returns.

Conclusions:

  • The novel deep matching framework offers a promising solution for reliable stock portfolio construction.
  • The approach effectively overcomes challenges associated with financial time-series data.
  • This method enhances quantitative investment strategies by improving risk-adjusted returns.