Intelligent Systems in Accounting, Finance and Management最新文献_第2页

Analysis of Economic Environment Incidence in Genetic Programming-Evolved Multiperiod Bankruptcy Prediction Models 遗传规划中的经济环境影响分析——演化的多期破产预测模型

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-02-09 DOI: 10.1002/isaf.70034

Ángel Beade, José Santos, Manuel Rodríguez

{"title":"Analysis of Economic Environment Incidence in Genetic Programming-Evolved Multiperiod Bankruptcy Prediction Models","authors":"Ángel Beade, José Santos, Manuel Rodríguez","doi":"10.1002/isaf.70034","DOIUrl":"10.1002/isaf.70034","url":null,"abstract":"<p>Genetic programming (GP) is used to obtain multiperiod bankruptcy prediction models, as well as to perform a prior feature selection process for these models. Given the controversy in the field of bankruptcy prediction about the need to include (or not) variables from the economic environment as input information for the prediction models, an analysis is carried out to check whether the impact that the economic environment undoubtedly has on the firms can be captured using only the financial variables of the firm as explanatory variables. To this end, the analysis includes a study of the correlation between the estimates of the prediction models and certain economic indicators. The results confirm the possibility of capturing the evolution of the economic environment using only financial information as input, as strong correlations are shown between the predictions of the models and important economic indicators over a very long postlearning period (2008–2020) and varied in terms of the economic environment (crisis, recovery, COVID, etc.).</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading 有序加权平均算子在深度学习和量子计算中的应用

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-01-27 DOI: 10.1002/isaf.70033

David Alaminos, M. Belén Salas-Compás, Ana J. Cisneros-Ruiz

{"title":"Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading","authors":"David Alaminos, M. Belén Salas-Compás, Ana J. Cisneros-Ruiz","doi":"10.1002/isaf.70033","DOIUrl":"10.1002/isaf.70033","url":null,"abstract":"<div>\u0000 \u0000 <p>Crypto assets have experienced significant growth in recent years, attracting substantial investments from institutional entities and individual investors alike. This surge in popularity necessitates sophisticated strategies to optimize returns. Concurrently, advancements in machine learning have revolutionized the forecasting of crypto asset returns, facilitating algorithmic trading. Leveraging robust algorithms, this approach enables comprehensive market exploration and capitalizes on escalating computational capabilities. This manuscript presents a comparative analysis of neural networks, genetic algorithms, and fuzzy logic, framed within the ordered weighted average (OWA) operator paradigm. These methods are integrated with deep learning and quantum computing principles to predict price movements in crypto assets and other financial indices. Our findings indicate that the quantum genetic algorithm excels in accurately forecasting asset price trends, whereas the quantum fuzzy approach exhibits comparatively lower precision in predicting cryptocurrency price fluctuations. The empirical analysis employs high-frequency data sampled at 10-, 30-, and 60-min intervals from October 2021 to February 2023. The dataset encompasses 11 cryptocurrencies (e.g., Bitcoin and Ethereum), 10 fan tokens, 10 NFTs, and nine reference financial indices (including Gold, WTI Oil, S&P 500, and Euro Stoxx 60). The implications of this research extend to the development of advanced algorithmic trading strategies, offering valuable tools for market participants and stakeholders in the financial sector. The methodologies discussed herein provide versatile and quantitative frameworks for analyzing diverse financial markets, highlighting their potential to enhance decision-making and improve investment outcomes.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading 有序加权平均算子在深度学习和量子计算中的应用

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-01-27 DOI: 10.1002/isaf.70033

David Alaminos, M. Belén Salas-Compás, Ana J. Cisneros-Ruiz

{"title":"Ordered Weighted Average Operators Into Deep Learning and Quantum Computing for Algorithmic Trading","authors":"David Alaminos, M. Belén Salas-Compás, Ana J. Cisneros-Ruiz","doi":"10.1002/isaf.70033","DOIUrl":"https://doi.org/10.1002/isaf.70033","url":null,"abstract":"<div>\u0000 \u0000 <p>Crypto assets have experienced significant growth in recent years, attracting substantial investments from institutional entities and individual investors alike. This surge in popularity necessitates sophisticated strategies to optimize returns. Concurrently, advancements in machine learning have revolutionized the forecasting of crypto asset returns, facilitating algorithmic trading. Leveraging robust algorithms, this approach enables comprehensive market exploration and capitalizes on escalating computational capabilities. This manuscript presents a comparative analysis of neural networks, genetic algorithms, and fuzzy logic, framed within the ordered weighted average (OWA) operator paradigm. These methods are integrated with deep learning and quantum computing principles to predict price movements in crypto assets and other financial indices. Our findings indicate that the quantum genetic algorithm excels in accurately forecasting asset price trends, whereas the quantum fuzzy approach exhibits comparatively lower precision in predicting cryptocurrency price fluctuations. The empirical analysis employs high-frequency data sampled at 10-, 30-, and 60-min intervals from October 2021 to February 2023. The dataset encompasses 11 cryptocurrencies (e.g., Bitcoin and Ethereum), 10 fan tokens, 10 NFTs, and nine reference financial indices (including Gold, WTI Oil, S&P 500, and Euro Stoxx 60). The implications of this research extend to the development of advanced algorithmic trading strategies, offering valuable tools for market participants and stakeholders in the financial sector. The methodologies discussed herein provide versatile and quantitative frameworks for analyzing diverse financial markets, highlighting their potential to enhance decision-making and improve investment outcomes.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Portfolio Optimization of Cryptocurrencies via Clustering Methods 基于聚类方法的加密货币动态投资组合优化

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-01-08 DOI: 10.1002/isaf.70032

Hossein Dastkhan, Ali Norouzi

{"title":"Dynamic Portfolio Optimization of Cryptocurrencies via Clustering Methods","authors":"Hossein Dastkhan, Ali Norouzi","doi":"10.1002/isaf.70032","DOIUrl":"https://doi.org/10.1002/isaf.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>The rise of cryptocurrencies has generated significant interest from the public and investors due to their decentralized nature, advanced security features, and potential for high returns. This research uses K-Means clustering and Inverse Covariance Clustering (ICC) to optimize cryptocurrency portfolios by addressing market dynamics and traditional portfolio management limitations. The study involved three phases: collecting daily price data from the top 100 cryptocurrencies from January 2018 to January 2024, performing calculations to identify cryptocurrencies through clustering methods, and constructing and dynamically optimizing investment portfolios from early 2022 to early 2024. We evaluate the constructed portfolios against the Cryptocurrency Benchmark Index (CRIX) using metrics like the Sharpe and Treynor ratios. Results show that both clustering methods can create efficient portfolios, but their effectiveness varies with dataset characteristics and investor objectives. K-Means produces more diversified portfolios, while ICC yields lower volatility portfolios, with ICC generally outperforming K-Means compared to the CRIX index. The findings highlight the potential of clustering methods in enhancing cryptocurrency portfolio selection and suggest the need for further research on real-world applications and advanced techniques tailored for the cryptocurrency market.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145983568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Portfolio Optimization of Cryptocurrencies via Clustering Methods 基于聚类方法的加密货币动态投资组合优化

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-01-08 DOI: 10.1002/isaf.70032

Hossein Dastkhan, Ali Norouzi

{"title":"Dynamic Portfolio Optimization of Cryptocurrencies via Clustering Methods","authors":"Hossein Dastkhan, Ali Norouzi","doi":"10.1002/isaf.70032","DOIUrl":"https://doi.org/10.1002/isaf.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>The rise of cryptocurrencies has generated significant interest from the public and investors due to their decentralized nature, advanced security features, and potential for high returns. This research uses K-Means clustering and Inverse Covariance Clustering (ICC) to optimize cryptocurrency portfolios by addressing market dynamics and traditional portfolio management limitations. The study involved three phases: collecting daily price data from the top 100 cryptocurrencies from January 2018 to January 2024, performing calculations to identify cryptocurrencies through clustering methods, and constructing and dynamically optimizing investment portfolios from early 2022 to early 2024. We evaluate the constructed portfolios against the Cryptocurrency Benchmark Index (CRIX) using metrics like the Sharpe and Treynor ratios. Results show that both clustering methods can create efficient portfolios, but their effectiveness varies with dataset characteristics and investor objectives. K-Means produces more diversified portfolios, while ICC yields lower volatility portfolios, with ICC generally outperforming K-Means compared to the CRIX index. The findings highlight the potential of clustering methods in enhancing cryptocurrency portfolio selection and suggest the need for further research on real-world applications and advanced techniques tailored for the cryptocurrency market.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145983567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI Chatbot as IFRS Advisory Tool: GPT-4 Experimental Design AI聊天机器人作为IFRS咨询工具：GPT-4实验设计

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-01-02 DOI: 10.1002/isaf.70031

Todor Tocev, Atanasko Atanasovski

{"title":"AI Chatbot as IFRS Advisory Tool: GPT-4 Experimental Design","authors":"Todor Tocev, Atanasko Atanasovski","doi":"10.1002/isaf.70031","DOIUrl":"https://doi.org/10.1002/isaf.70031","url":null,"abstract":"<div>\u0000 \u0000 <p>The complexity of International Financial Reporting Standards (IFRS) challenges accounting professionals to navigate intricate judgment calls and estimations. This paper tackles a pressing question: Can OpenAI's ChatGPT (Version GPT-4) serve as a reliable artificial intelligence (AI) advisory tool to interpret and apply IFRS standards in real-world scenarios? The importance of this inquiry lies in the potential of generative AI to revolutionize financial reporting by enhancing accuracy, efficiency, and decision-making speed, which are critical demands in today's globalized financial environment. Through an experimental design employing practical case studies, this research evaluates GPT-4's performance under three prompting strategies: zero shot (ZS), few shot (FS), and chain of thought (CoT). This research examines the ability of AI to address judgment-driven, complex IFRS problems, expanding the scope of prior studies that primarily relied on theoretical exams or professional certification tests. Our findings reveal that GPT-4 can consistently identify the correct IFRS standard and produce professionally usable guidance, exhibiting strong potential. ZS proved fastest and most practical for a first advisory pass, FS delivered more structured and accounting-like answers but required greater preparation, and CoT generated the richest explanations at the expense of efficiency. Across all strategies, expert review remained necessary in areas involving item and measurement choices, contract integration, or business-model interpretation. This study efforts to advance the dialogue on AI's role in accounting and lays a foundation for future research exploring its broader implications in accounting decision-making. With insights into GPT-4's strengths and constraints, this study emphasizes its role as a transformative, yet supplementary, tool in advancing IFRS compliance and reporting standards.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI Chatbot as IFRS Advisory Tool: GPT-4 Experimental Design AI聊天机器人作为IFRS咨询工具：GPT-4实验设计

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2026-01-02 DOI: 10.1002/isaf.70031

Todor Tocev, Atanasko Atanasovski

{"title":"AI Chatbot as IFRS Advisory Tool: GPT-4 Experimental Design","authors":"Todor Tocev, Atanasko Atanasovski","doi":"10.1002/isaf.70031","DOIUrl":"https://doi.org/10.1002/isaf.70031","url":null,"abstract":"<div>\u0000 \u0000 <p>The complexity of International Financial Reporting Standards (IFRS) challenges accounting professionals to navigate intricate judgment calls and estimations. This paper tackles a pressing question: Can OpenAI's ChatGPT (Version GPT-4) serve as a reliable artificial intelligence (AI) advisory tool to interpret and apply IFRS standards in real-world scenarios? The importance of this inquiry lies in the potential of generative AI to revolutionize financial reporting by enhancing accuracy, efficiency, and decision-making speed, which are critical demands in today's globalized financial environment. Through an experimental design employing practical case studies, this research evaluates GPT-4's performance under three prompting strategies: zero shot (ZS), few shot (FS), and chain of thought (CoT). This research examines the ability of AI to address judgment-driven, complex IFRS problems, expanding the scope of prior studies that primarily relied on theoretical exams or professional certification tests. Our findings reveal that GPT-4 can consistently identify the correct IFRS standard and produce professionally usable guidance, exhibiting strong potential. ZS proved fastest and most practical for a first advisory pass, FS delivered more structured and accounting-like answers but required greater preparation, and CoT generated the richest explanations at the expense of efficiency. Across all strategies, expert review remained necessary in areas involving item and measurement choices, contract integration, or business-model interpretation. This study efforts to advance the dialogue on AI's role in accounting and lays a foundation for future research exploring its broader implications in accounting decision-making. With insights into GPT-4's strengths and constraints, this study emphasizes its role as a transformative, yet supplementary, tool in advancing IFRS compliance and reporting standards.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products 基于竞争电子商务产品语义的智能产品概念设计方法

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-12-25 DOI: 10.1002/isaf.70025

Haiying Ren, Jun Guan, Jingru Guo

{"title":"Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products","authors":"Haiying Ren, Jun Guan, Jingru Guo","doi":"10.1002/isaf.70025","DOIUrl":"https://doi.org/10.1002/isaf.70025","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the limitations of existing product concept design (PCD) methods in the rapidly changing market environments, this study proposes a PCD method using e-commerce product data and artificial intelligence techniques. First, data of competing e-commerce products are acquired from an e-commerce platform. Second, monthly sales of products are categorized and selected as the indicator for evaluating product concepts (PCs). Third, Doc2Vec is used to vectorize the product description to obtain the semantic representation of PCs, and a machine learning-based PC evaluation model is built using the concept vector as features. Finally, a PC element library is built based on Word2Vec, and the tabu search algorithm is applied to identify the optimal combination of concept elements, determining the most favorable combination of PCs for the new product. Results indicate that the PC evaluation model based on multilayer perceptron achieves an average accuracy of 85.62% in predicting the quartiles of sales in the case of middle-aged and elderly home products, with the area under the receiver operating characteristic curve ranging from 0.96 to 0.99. The proposed PCD method can produce novel PCs with good market potential and a high degree of automation, improving the time efficiency and quality of PCD.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products 基于竞争电子商务产品语义的智能产品概念设计方法

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-12-25 DOI: 10.1002/isaf.70025

Haiying Ren, Jun Guan, Jingru Guo

{"title":"Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products","authors":"Haiying Ren, Jun Guan, Jingru Guo","doi":"10.1002/isaf.70025","DOIUrl":"https://doi.org/10.1002/isaf.70025","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the limitations of existing product concept design (PCD) methods in the rapidly changing market environments, this study proposes a PCD method using e-commerce product data and artificial intelligence techniques. First, data of competing e-commerce products are acquired from an e-commerce platform. Second, monthly sales of products are categorized and selected as the indicator for evaluating product concepts (PCs). Third, Doc2Vec is used to vectorize the product description to obtain the semantic representation of PCs, and a machine learning-based PC evaluation model is built using the concept vector as features. Finally, a PC element library is built based on Word2Vec, and the tabu search algorithm is applied to identify the optimal combination of concept elements, determining the most favorable combination of PCs for the new product. Results indicate that the PC evaluation model based on multilayer perceptron achieves an average accuracy of 85.62% in predicting the quartiles of sales in the case of middle-aged and elderly home products, with the area under the receiver operating characteristic curve ranging from 0.96 to 0.99. The proposed PCD method can produce novel PCs with good market potential and a high degree of automation, improving the time efficiency and quality of PCD.</p>\u0000 </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"33 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Complexity and Heterogeneity in Cryptocurrency Prices: An Analysis Based on Gaussian Mixture Model and Consensus Clustering 加密货币价格的复杂性和异质性：基于高斯混合模型和共识聚类的分析

IF 3.7

Intelligent Systems in Accounting, Finance and Management Pub Date : 2025-12-22 DOI: 10.1002/isaf.70024

Tâmara Leal, Pedro Campos, Carlos Alves

{"title":"Complexity and Heterogeneity in Cryptocurrency Prices: An Analysis Based on Gaussian Mixture Model and Consensus Clustering","authors":"Tâmara Leal, Pedro Campos, Carlos Alves","doi":"10.1002/isaf.70024","DOIUrl":"https://doi.org/10.1002/isaf.70024","url":null,"abstract":"<p>This study investigates the daily price patterns and behavioral similarities among cryptocurrencies, focusing on two key research questions: (1) Do cryptocurrency prices vary consistently throughout the day? (2) Can cryptocurrencies be meaningfully grouped based on their behavioral patterns? Using Gaussian mixture models (GMMs), we analyze the opening, closing, high, and low prices of a broad range of cryptocurrencies. The findings reveal that while opening prices exhibit uniform patterns, closing, high, and low prices show more complex, multi-component behaviors, reflecting diverse market dynamics throughout the day. Consensus clustering identifies four distinct cryptocurrency clusters, each demonstrating unique price behaviors, challenging the notion of cryptocurrencies as a homogeneous group. The results suggest that cryptocurrencies behave as differentiated financial products, influenced by factors such as volatility, adoption, and technology. These findings contribute to the understanding of cryptocurrency market dynamics and have implications for investment strategies, risk management, and regulatory approaches.</p>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"32 4","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/isaf.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0