A comparative study of stock scoring using regression and genetic-based linear models

@article{Huang2011ACS,
  title={A comparative study of stock scoring using regression and genetic-based linear models},
  author={Chien-Feng Huang and Tsung-Nan Hsieh and Bao Rong Chang and Chih-Hsiang Chang},
  journal={2011 IEEE International Conference on Granular Computing},
  year={2011},
  pages={268-273}
}
Stock selection has long been a challenging and important task in investment and finance. Researchers and practitioners in this area often use regression models to tackle this problem due to their simplicity and effectiveness. Recent advances in machine learning (ML) are leading to significant opportunities to solve these problems more effectively. In this paper, we present a comparative study between the traditional regression-based and ML-based linear models for stock scoring, which is… CONTINUE READING

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