Prediction of stock price movement using an improved NSGA-II-RF algorithm with a three-stage feature engineering process.

Prediction of stock price has been a hot topic in artificial intelligence field. Computational intelligent methods such as machine learning or deep learning are explored in the prediction system in recent years. However, making accurate predictions of stock price direction is still a big challenge because stock prices are affected by nonlinear, nonstationary, and high dimensional features. In previous works, feature engineering was overlooked. How to select the optimal feature sets that affect stock price is a prominent solution. Hence, our motivation for this article is to propose an improved many-objective optimization algorithm integrating random forest (I-NSGA-II-RF) algorithm with a three-stage feature engineering process in order to decrease the computational complexity and improve the accuracy of prediction system. Maximizing accuracy and minimizing the optimal solution set are the optimization directions of the model in this study. The integrated information initialization population of two filtered feature selection methods is used to optimize the I-NSGA-II algorithm, using multiple chromosome hybrid coding to synchronously select features and optimize model parameters. Finally, the selected feature subset and parameters are input to the RF for training, prediction, and iterative optimization. Experimental results show that the I-NSGA-II-RF algorithm has the highest average accuracy, the smallest optimal solution set, and the shortest running time compared to the unmodified multi-objective feature selection algorithm and the single target feature selection algorithm. Compared to the deep learning model, this model has interpretability, higher accuracy, and less running time.

A hybrid model integrating long short-term memory with adaptive genetic algorithm based on individual ranking for stock index prediction.

Modeling and forecasting stock prices have been important financial research topics in academia. This study seeks to determine whether improvements can be achieved by forecasting a stock index using a hybrid model and incorporating financial variables. We extend the literature on stock market forecasting by applying a hybrid model that combines wavelet transform (WT), long short-term memory (LSTM), and an adaptive genetic algorithm (AGA) based on individual ranking to predict stock indices for the Dow Jones Industrial Average (DJIA) index of the New York Stock Exchange, Standard & Poor's 500 (S&P 500) index, Nikkei 225 index of Tokyo, Hang Seng Index of Hong Kong market, CSI300 index of Chinese mainland stock market, and NIFTY50 index of India. The results indicate an overall improvement in forecasting of the stock index using the AGA-LSTM model compared to the benchmark models. The evaluation indicators prove that this model has a higher prediction accuracy when forecasting six stock indices.

Patterns and driving forces of the agricultural water footprint of Chinese cities.

The patterns and determinants of different types of agricultural water footprints in China are poorly understood at the prefecture-city level. In this paper, we evaluate Chinese agricultural water footprints from 2000 to 2017 and analyzed their spatio-temporal characteristics. Our estimation results show that the annual average agricultural water footprint in China was 5.038 × 109 m3, and the proportions of green water, blue water, and gray water were 70%, 9%, and 21%, respectively. In addition, high agricultural water-footprint cities with obvious urban agglomeration effects are mainly located in the Northeast, the Huanghuai River, the Yangtze River Basin, and Northwestern of Xinjiang, while low agricultural water-footprint cities are concentrated in high coastal urbanization-level areas or less developed agricultural areas of the west. We also investigate their determinants using a spatio-temporal fixed-effect model and find that GDP per capita, total investment in fixed assets, the income level of rural residents, the proportion of food grown, spray and drip irrigation technology, low-pressure pipe irrigation technology and seepage control irrigation technology have significant positive impacts on the agricultural water footprint. In contrast, the proportion of secondary and tertiary industries, social retail consumption, urbanization, technology expenditure, and the effective irrigation area proportion have a significant inhibitory effect. The primary determinants of the agricultural water footprint also vary substantially across water footprint categories (green, blue, and gray water footprints) and regions. Our findings imply that the agricultural water footprint should be incorporated into city water resource management and monitoring system.