Enhancing Portfolio Optimization with Weighted Scoring for Return Prediction Through Machine Learning and Neural Networks

Accurately predicting stock return can enhance the effectiveness of portfolio optimization models. Many previous studies typically divide machine learning algorithms and portfolio optimization into two separate stages: the first step leverages the powerful modeling capabilities of machine learning algorithms to select stocks, and the second step optimizes weights using traditional portfolio models. This separation means that the modeling strengths of machine learning are only utilized in the stock selection phase and not fully exploited during weight optimization. Therefore, this study proposes a portfolio construction method based on Return Prediction Weighted Scoring (RPWS). RPWS generates a stock ranking by assigning weighted scores to each stock, cleverly maps this ranking to weight biases, and then optimizes actual weights using a traditional covariance matrix. This process successfully integrates the modeling capabilities of machine learning into the weight optimization phase, ensuring its full utilization throughout the portfolio construction process. Backtesting experiments are conducted using the U.S. stock market, A-share market, and major cryptocurrencies as datasets, with Support Vector Regression (SVR), Transformer, and other machine learning algorithms as prediction models. Empirical results from these three markets show that the SVR-RPWS and Transformer-RPWS models significantly outperform mainstream funds and traditional portfolio models in terms of annualized returns, sharpe ratio, and drawdown control.

@article{Sun2025,
  title     = {Enhancing Portfolio Optimization with Weighted Scoring for Return Prediction Through Machine Learning and Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {7},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ruili Sun and Qiongchao Xia and Shiguo Huang},
  doi       = {10.14569/IJACSA.2025.0160781},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160781}
}