Application of Deep Learning for Stock Prediction Within the Framework of Portfolio Optimization in Quantitative Trading - Xiaoyu Qin (HighTech and Innovation Journal, 2025)
This paper proposes a method for stock prediction and portfolio optimization as a part of quantitative trading based on a combination of Bi-RNN and a modified snake optimization algorithm (MSOA) to build optimal portfolios and outperform conventional models and benchmarks. Methods/Analysis: We employ the Bi-RNN model, which processes historical stock data in both forward and backward directions to unveil intricate temporal dependencies. MSOA is used to fine-tune the hyperparameters of the Bi-RNN with enhancements such as Latin Hypercube Sampling for initialization, dynamic temperature adjustment, adaptive learning rates, and hybrid exploration-exploitation mechanisms. The Markowitz mean-variance approach is used to optimize the portfolio from asset allocations that the MSOA then improves. The model is evaluated on the S&P 500 from 1993 to 2020. Results: Such findings in experiments indicate that the proposed model outperforms baseline models, e.g., LSTM, GRU, and HMM, with lower Mean Squared Percentage Error (MSPE) values and higher Sharpe ratios of constructed portfolios. For instance, Portfolio 3 produced a 10.9% expected return with a standard deviation of 12.9%, delivering risk-adjusted returns that exceed those of the S&P 500. Novelty/Improvement: A strong integrated approach of deep learning and advanced optimization techniques is proposed for stock prediction and portfolio optimization, which achieves notable improvements in terms of accuracy and efficiency. The proposed approach overcomes the drawbacks of traditional algorithms, making it a valuable tool for financial decision-making.