DEA-RNNs: An Ensemble Approach for Portfolio Selection in the Thailand Stock Market

This study presents a new approach that combines Data Envelopment Analysis (DEA) for stock selection based on fundamental metrics with Recurrent Neural Networks (RNNs), including simple RNN and Long Short-Term Memory (LSTM) networks, to predict future stock trends in the Thai stock market. DEA can identify the most qualified stocks based on fundamental metrics, while RNNs analyze the historical prices of these selected stocks to forecast their future trends. This innovative DEA-RNNs approach aims to improve the reliability of the investment decision-making process. Using the DEA methodology, our study evaluated thirty-seven stocks based on their financial metrics from the first quarter of 2017 to the third quarter of 2022. Following the analysis, DEA identified ten stocks as the most promising. Subsequent predictive modeling using RNNs showed that only seven out of these ten initially identified stocks were projected to experience an upward trend in the subsequent quarter. To assess the predictive models’ performance, we utilized metrics such as mean square error (MSE) and mean absolute error (MAE), consistently demonstrating the LSTM model’s superior performance over the simple RNN model.