Article
Business, Finance
Jae H. Kim, Abul Shamsuddin
Summary: We use extreme bounds analysis (EBA) to assess the robustness or fragility of various stock market anomalies using U.S. daily data from 1960 to 2023. EBA is a large-scale sensitivity analysis that allows the isolation of the effects of potential data-mining or p-hacking given model uncertainty. The anomalies explored include Halloween, sports events, seasonal affective disorder, weather, political cycle, daylight saving, and lunar phase. We find that the empirical evidence for these anomalies is highly fragile in terms of effect size estimates and statistical significance.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
B. C. Barroso, R. T. N. Cardoso, M. K. Melo
Summary: This article proposes a fusion between Technical Analysis indicators and Multiobjective Portfolio Optimization, with two scenarios for optimization and numerical simulations based on data from the Brazilian Stock Exchange. Results show that this fusion can improve portfolio performance, providing optimal strategies to investors for higher returns at certain risk levels.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Federico Gatta, Carmela Iorio, Diletta Chiaro, Fabio Giampaolo, Salvatore Cuomo
Summary: This paper proposes a new statistical arbitrage approach by clustering stocks based on their exposure to common risk factors. A linear multifactor model is used as the theoretical background, and risk factors are extracted through Principal Component Analysis. The Adaptive Lasso technique is employed to standardize and select these factors, and assets are then clustered and their exposure to each factor is removed to achieve statistical arbitrage. Optimal weights for constructing the portfolio are determined using Sequential Least SQuares Programming. The methodology is tested on multiple stock markets and evaluated for robustness against three benchmarks using Cross-Validation.
NEURAL COMPUTING & APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Mahinda Mailagaha Kumbure, Christoph Lohrmann, Pasi Luukka, Jari Porras
Summary: This literature review explores the application of machine learning techniques in stock market prediction. It focuses on the stock markets investigated in the literature and the types of variables used as input in machine learning techniques for predicting these markets. The review includes an examination of 138 journal articles published between 2000 and 2019 and provides extensive insights into the data and machine learning techniques used for stock market prediction.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Automation & Control Systems
Sunita M. Dol, Pradip M. Jawandhiya
Summary: Educational data mining (EDM) applies data mining techniques in the field of education to classify, analyze, and predict students' academic performance, dropout rate, and instructors' performance. This review article analyzes 142 research articles from 2010 to 2020 and discusses the current developments in EDM in 2021 and 2022. It presents the use of classification techniques, clustering algorithms, association rule algorithms, regression techniques, and ensemble techniques in EDM. The article also compares different classification techniques and identifies research gaps for future improvement in the teaching-learning process.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Review
Clinical Neurology
Jieting Chen, Yufeng Xie, Qingchan Lin, Ziliang Qian, Jun Feng, Jianmei Zhang, Yun Chen, Wenhan Chen, Yueting Wu, Ziyi Guo
Summary: Using data mining analysis of published studies, this study provides valuable information regarding the selection of the most effective acupoints and point combinations for clinical acupuncture practice for treating tic disorders (TDs).
FRONTIERS IN NEUROLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Tengfei Wang, Baorong Xiao, Weixiao Ma
Summary: This study focuses on the application of association rule mining in analyzing student behavioral data. It proposes a four-layer data association mining architecture and updates the mining algorithm to improve efficiency.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2022)
Article
Mathematics
Jia-Hao Syu, Yi-Ren Yeh, Mu-En Wu, Jan-Ming Ho
Summary: The proposed self-management portfolio system, utilizing adaptive association mining, significantly improves annual return rate and Sharpe ratio, while reducing drawdown risk. It also features rapid closing and gradual increasing of positions, outperforming benchmarks in all measurements and on randomly sampled datasets.
Review
Computer Science, Artificial Intelligence
Iztok Fister Jr, Iztok Fister, Dusan Fister, Vili Podgorelec, Sancho Salcedo-Sanz
Summary: Association rule mining aims to search for relationships between attributes in transaction databases. The process involves pre-processing techniques, rule mining, and post-processing with visualization. This review paper provides a literature review and analysis of techniques, applications, and future research directions in association rule mining and visualization.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Business, Finance
Xiaotao Zhang, Ziqiao Wang, Jing Hao, Feng He
Summary: The daily price limit in the ChiNext market was changed from 10% to 20% in 2020. We used the difference-in-difference (DID) approach to examine the impact of this price limit change on firm-level market quality indicators. The results showed that the implementation of the new price limit range significantly improved market liquidity and increased market volatility and the likelihood of informed trading.
PACIFIC-BASIN FINANCE JOURNAL
(2022)
Article
Thermodynamics
Yilin Ma, Yudong Wang, Weizhong Wang, Chong Zhang
Summary: This paper explores the use of return and volatility prediction to improve energy portfolio models, using extreme gradient boosting regression trees. Six classical portfolio models are tested, and the results show that using return and volatility prediction significantly enhances these models' performance. Prediction-based weights are also found to be more effective in transforming multiple objectives compared to equal weights. The CVaR-F-PW portfolio performs the best and is recommended for energy stock market portfolio management.
Article
Psychology, Multidisciplinary
Yujia Chen, Jiangdan Liu, Yanzi Gao, Wei He, Hongyu Li, Guangling Zhang, Hongwei Wei
Summary: A stock market analysis method based on evidential reasoning and hierarchical belief rule base is proposed in this study. The method includes an evaluation model for assessing market sentiment and a decision model for supporting investment decisions. Experimental results demonstrate the effectiveness of the proposed method in comprehensively analyzing the stock market and aiding investment decisions.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Business, Finance
Wendai Lv, Jipeng Qi
Summary: This paper investigates the predictability of stock market returns based on traditional macroeconomic variables. The empirical results suggest that the mean combination forecast model outperforms other models in forecasting stock market returns, and its performance remains robust across different forecasting windows, market conditions, and multi-step-ahead forecasts. Importantly, the mean combination forecast consistently generates higher CER gains compared to other models, considering different investors' risk aversion coefficients and trading costs.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2022)
Article
Chemistry, Multidisciplinary
Jieh-Ren Chang, You-Shyang Chen, Chien-Ku Lin, Ming-Fu Cheng
Summary: Based on more than 8000 items of SSD error data, this study uses association rule algorithm to propose three improvement strategies for production control, including speeding up error judgment, formulating quality strategy, and customer service strategy.
APPLIED SCIENCES-BASEL
(2021)
Article
Construction & Building Technology
Abbas Al-Refaie, Banan Abu Hamdieh, Natalija Lepkova
Summary: This study proposed a data mining framework for predicting sequential patterns of maintenance activities. The framework consisted of data collection, prediction of maintenance activities with and without attributes, and then the comparison between prediction results. The results showed that the proposed framework effectively predicted the next maintenance activities and planning preventive maintenance based on product attributes.
Article
Social Issues
Shu-Hsien Liao, Retno Widowati, Yu-Chieh Hsieh
Summary: Online social media platforms facilitate the creation of virtual communities and information exchange, driving the development of social commerce. By analyzing customer data with recommendation systems, online sales rates and user experience are improved.
TECHNOLOGY IN SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Shu-Hsien Liao, Retno Widowati, Hao-Yu Chang
Summary: Online streaming has become increasingly popular due to the availability of broadband networks and the increase in computing power and electronic distribution. Operators face challenges in developing flexible business alternatives according to users' changing streaming behaviors to establish a good and profitable business model.
APPLIED ARTIFICIAL INTELLIGENCE
(2021)
Article
Industrial Relations & Labor
Shu-Hsien Liao, Da-Chian Hu, Yi-Ching Huang
Summary: This study investigates the relationships between emotional intelligence, psychological capital, job performance, organizational citizenship behavior, and perceived organizational support. The results show that psychological capital mediates the effect of emotional intelligence on organizational citizenship behavior, and perceived organizational support moderates this mediated effect.
EMPLOYEE RELATIONS
(2022)
Article
Business
Shu-Hsien Liao, Da-Chian Hu, Yi-Wen Fang
Summary: This study investigates Taiwanese consumers' repurchase intentions for communication service and 3C products in physical stores, focusing on the mediating role of perceived value (PV) between consumers' channel brand image (CBI) and store image (SI) on repurchase intention (RI), as well as the moderating role of electronic word-of-mouth (EWOM) on the relationship between CBI, SI, PV, and RI.
INTERNATIONAL JOURNAL OF RETAIL & DISTRIBUTION MANAGEMENT
(2023)
Article
Information Science & Library Science
Shu-hsien Liao, Retno Widowati, Ching-Yu Lee
Summary: This study focuses on TikTok users in Taiwan and analyzes user profiles and social media app development patterns through data mining and association rules' analysis. The findings indicate that social media apps are a valuable research topic in online social media development.
Article
Social Sciences, Interdisciplinary
Shu-Hsien Liao, Da-Chian Hu, Huan-Lun Chou
Summary: This study investigates the relationships between perceived service quality, brand image, customer satisfaction, and purchase intention. It also examines the moderated mediating role of brand love. The results show that brand love plays a moderating mediating role on the relationship between perceived service quality, brand image, customer satisfaction, and purchase intention.
Article
Computer Science, Information Systems
Shu-Hsien Liao, Ching-An Yang
Summary: This study examines the experience of various Taiwanese fan page users through market survey and big data analysis, and develops a personalized recommendation system to help users obtain behavioral knowledge and generate personalized recommendations for building a SNM mechanism.
SOCIAL NETWORK ANALYSIS AND MINING
(2021)
Article
Management
Shu-Hsien Liao, Da-Chian Hu, Yii-Shun Shih
Summary: By establishing a collaborative supply chain mechanism, enterprises can thrive in the competitive business environment, enhance supply chain capabilities, and achieve innovation. The study found that supply chain collaboration has a positive impact on innovation capability, and moderated mediation effects exist.
TOTAL QUALITY MANAGEMENT & BUSINESS EXCELLENCE
(2021)
Article
Business
Shu-Hsien Liao, Ling-Ling Yang
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2020)
Article
Management
Wen-Jung Chang, Shu-Hsien Liao, Yu-Chun Chung, Hung-Pin Chen
TOTAL QUALITY MANAGEMENT & BUSINESS EXCELLENCE
(2020)
Article
Business
Shu-Hsien Liao, Szu-Yu Hsu
ASIA PACIFIC JOURNAL OF MARKETING AND LOGISTICS
(2020)
Article
Business
Shu-hsien Liao, Yi-Shan Tasi
BUSINESS PROCESS MANAGEMENT JOURNAL
(2019)
Article
Management
Shu-Hsien Liao, Yu-Chun Chung, Wen-Jung Chang
INTERNATIONAL JOURNAL OF SERVICES TECHNOLOGY AND MANAGEMENT
(2019)
Article
Computer Science, Artificial Intelligence
Chih-Hao Wen, Shu-Hsien Liao, Shu-Fang Huang
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY
(2018)
Article
Management
Shu-Hsien Liao, Chih-Chiang Chen
LEADERSHIP & ORGANIZATION DEVELOPMENT JOURNAL
(2018)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)