In the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so it is important to select the optimal set of factors. Elastic Net, which combines the benefits of the L1 and L2 penalty terms, performs better at filtering features due to the complexity of the features in high-dimensional datasets than Lasso and Ridge regression. At the same time, the false discovery rate (FDR), which is important for making reliable investment decisions, is not taken into account by the current factor-selection methodologies. Therefore, this paper constructs the Knockoff Logistic regression Elastic Net (KF-LR-Elastic Net): combining Logistic regression with Elastic Net and using Knockoff to control the FDR of variable selection to achieve factor selection. Based on the selected factors, stock returns are predicted under Logistic regression. The overall model is denoted as Knockoff Logistic regression Elastic Net-Logistic regression (KL-LREN-LR). The empirical study is conducted with data on the CSI 300 index constituents in the Chinese market from 2016–2022. KF-LREN-LR is used for factor selection and stock-return forecasting to select the top 10 stocks and establish an investment strategy for daily position changing. According to empirical evidence, KF-LR-Elastic Net can select useful factors and control the FDR, which is helpful for increasing the accuracy of factor selection. The KF-LREN-LR forecast portfolio has the advantages of high return and controlled risk, so it is informative for optimizing asset allocation.

With the deepening of economic globalization and the expanding scale of the foreign exchange market, the study of the volatility characteristics and forecasting of foreign exchange has received widespread attention.In this paper, a new forex portfolio forecasting model is proposed, which uses fully integrated empirical modal adaptive noise decomposition (CEEMDAN) to decompose the original forex price into sub-layers of different frequencies; then, convolutional neural network, bi-directional long and short term memory and attention mechanism module (CNN-BiLSTM-Attention) are used to combine the forecasting of each sub-layer; finally, the forecasting results of different frequency sub-layers are summed Reconstructed as the final prediction value. The forex series of USD, EUR, JPY and HKD against CNY were selected for empirical analysis, and based on the results of both evaluation metrics and DM tests, it was concluded that the CEEMDAN-CNN-BiLSTM-Attention model performed better in the forex market and had higher accuracy in exchange rate forecasting compared to the single model and other combined models.