Intelligent Systems in Accounting, Finance and Management最新文献

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.

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.

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.

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.

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.

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.

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.

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.