Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection

Yun, Kyung Keun; Yoon, Sang Won; Won, Daehan

doi:10.1016/j.eswa.2022.118803

Cited by 30 publications

(8 citation statements)

References 26 publications

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“…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 .…”

Section: Related Workmentioning

confidence: 99%

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

Li,

Kamaruddin,

Yuhaniz

et al. 2024

Sci Rep

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This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts.

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Section: Related Workmentioning

confidence: 99%

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

Li,

Kamaruddin,

Yuhaniz

et al. 2024

Sci Rep

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show abstract

“…The prediction of cellular network traffic involves forecasting future cellular network traffic data through the analysis of the spatial-temporal distribution of known cellular traffic data. Over the past decade, deep learning techniques have gained widespread application in time-series prediction, including the prediction of vehicle flow and subway passenger flow [9][10][11][12][13][14]. Incorporating deep learning into time-series prediction has significantly contributed to the advancement of cellular network traffic prediction.…”

Section: Related Workmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

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“…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). Tuning a DNN model seems theoretically feasible, but in practice, most DNN models have an excessive number of parameters.…”

Section: Related Workmentioning

confidence: 99%

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

Li,

Kamaruddin,

Al-Jaifi

2023

Preprint

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This paper presents an enhanced framework that combines Symbolic Genetic Algorithm (SGA) with Long-Short Term Memory Neural Network (LSTM) for predicting cross-sectional price returns using fundamental indicators of 4,500 listed stocks in China. The study addresses the challenges posed by fundamental indicators resembling smart beta factors in efficient markets and the low frequency of fundamental indicator updates for deep learning models (DNN). The proposed DNN framework incorporates data augmentation and feature selection techniques, resulting in significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1,128% and 5,360% for fundamental driven data. Additionally, a rule-based strategy based on the hybrid SGA-LSTM model outperforms major Chinese stock indexes, generating impressive average annualized excess returns compared to the CSI 300 and CSI 500 indexes. These findings highlight the effectiveness of LSTM with SGA in optimizing cross-sectional stock return predictions based on fundamental indicators, providing valuable insights for financial professionals..

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Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection

Cited by 30 publications

References 26 publications

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

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

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

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

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