Mean–variance portfolio optimization using machine learning-based stock price prediction - Chen, Wei; Zhang, Haoyu; Mehlawat, Mukesh Kumar; Jia, Lifen (Applied soft computing, 2021)
The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction and mean–variance (MV) model for portfolio selection. Specifically, two stages are involved in this model: stock prediction and portfolio selection. In the first stage, a hybrid model combining eXtreme Gradient Boosting (XGBoost) with an improved firefly algorithm (IFA) is proposed to predict stock prices for the next period. The IFA is developed to optimize the hyperparameters of the XGBoost. In the second stage, stocks with higher potential returns are selected, and the MV model is employed for portfolio selection. Using the Shanghai Stock Exchange as the study sample, the obtained results demonstrate that the proposed method is superior to traditional ways (without stock prediction) and benchmarks in terms of returns and risks. •Stock prediction is integrated into portfolio selection to capture future features.•A hybrid model named IFAXGBoost is developed for stock price prediction.•The prediction model IFAXGBoost is incorporated into the mean-variance model.•Extensive experiments demonstrate the effectiveness of the proposed method.