Application of Feature Selection Based on Multilayer GA in Stock Prediction

Abstract

This paper proposes a feature selection model based on a multilayer genetic algorithm (GA) to select the features of a high stock dividend (HSD) and eliminate the relatively redundant features in the optimal solution by using layer-by-layer information transfer and two-dimensionality reduction methods. Combining the ensemble model and time-series split cross-validation (TSCV) indicator as the fitness function solves the problem of selecting the fitness function for each layer. The symmetry character of the model is fully utilized in the two-dimensionality reduction processes, according to the change in data dimensions and the unbalanced characteristics of the HSD, setting the corresponding TSCV indicators. We built seven ensemble prediction models for actual stock trading data for comparison experiments. The results show that the feature selection model based on multilayer GA can effectively eliminate the relatively redundant features after dimensionality reduction and significantly improve the balancing accuracy, precision and AUC performance of the seven ensemble learning models. Finally, adversarial validation is used to analyze the differences in the balanced accuracy of the training and test sets caused by the inconsistent distribution of the data sets.

Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming

“…For instance, Chen and Zhou used GA to rank factor importance and select features for a Long Short-Term Memory (LSTM) model, while Milad employed GA as a heuristic approach for selecting relevant features for an Artificial Neural Network (ANN) 24 , 25 . Li utilized a multilayer GA to select features and reduce high dimensionality in a stock dividend dataset 26 . Recently, Yun revised GA-based selection methods to a two-stage process, using a wrapper method to select important features to avoid the curse of dimensionality, followed by the filter method to select more critical factors 27 .…”

Can Long-short Term Memory Neural Network With Symbolic Genetic Algorithm Predict Stock Price Change Basing on Fundamental Indicators

“…For instance, Chen and Zhou used GA to rank factor importance and select features for a Long Short-Term Memory (LSTM) model, while Milad employed GA as a heuristic approach for selecting relevant features for an Arti cial Neural Network (ANN)(Chen & Zhou, 2021; Shahvaroughi Farahani & Razavi Hajiagha, 2021). Li utilized a multilayer GA to select features and reduce high dimensionality in a stock dividend dataset (Li et al, 2022). Recently, Yun revised GA-based selection methods to a two-stage process, using a wrapper method to select important features to avoid the curse of dimensionality, followed by the lter method to select more critical factors (Yun et al, 2023).…”

Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization

“…The general iterative operation makes the neural network algorithm fall into the local minimum and loop phenomenon, and then the neural network algorithm cannot run, and GA is a global optimization algorithm, which can overcome this phenomenon [52]. In order to improve the inventory bonus, Xiaoning Li et al used GA to eliminate relatively redundant features in the optimal solution of the model, and further explained the superiority of GA [53]. Dunjing Yu et al used GA to optimize the nonlinear predictive controller of the ship trajectory tracking model.…”