Search Results (386)

In this research, the primary goal was to investigate the relationship between strategic planning and organizational performance in Iraq’s manufacturing context. This study’s primary data sources were 360 manager respondents. A structured questionnaire was used to collect primary data from manufacturing firms located throughout Iraq. To analyze the results, the researchers used descriptive statistics, correlation, and multiple regression analysis. SPSS version 16 software was used to conduct data analysis. The results reveal that the process of strategic planning has a beneficial effect on financial performance. Environmental scanning has a statistically significant positive effect on a company’s nonfinancial performance. Management participation and planning formality positively and statistically significantly affect a business’s nonfinancial performance at the 10 percent level. The domain of strategy and technique does not impact a company’s nonfinancial performance. Full article

This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel user attention mechanism that dynamically adjusts attention weights based on data characteristics and user privacy needs, enhancing the ability to identify and protect sensitive information effectively. Significant methodological advancements differentiate our approach from existing techniques by incorporating user-specific attention into traditional LLMs, ensuring both data accuracy and privacy. We succinctly highlight the enhanced performance of this framework through a selective presentation of experimental results across various applications. Notably, in computer vision, the application of our user attention mechanism led to improved metrics over traditional multi-head and self-attention methods: FasterRCNN models achieved precision, recall, and accuracy rates of 0.82, 0.79, and 0.80, respectively. Similar enhancements were observed with SSD, YOLO, and EfficientDet models with notable increases in all performance metrics. In natural language processing tasks, our framework significantly boosted the performance of models like Transformer, BERT, CLIP, BLIP, and BLIP2, demonstrating the framework’s adaptability and effectiveness. These streamlined results underscore the practical impact and the technological advancement of our proposed framework, confirming its superiority in enhancing privacy protection without compromising on data processing efficacy. Full article

Predicting the directions of financial markets has been performed using a variety of approaches, and the large volume of unstructured data generated by traders and other stakeholders on social media microblog platforms provides unique opportunities for analyzing financial markets using additional perspectives. Pretrained large language models (LLMs) have demonstrated very good performance on a variety of sentiment analysis tasks in different domains. However, it is known that sentiment analysis is a very domain-dependent NLP task that requires knowledge of the domain ontology, and this is particularly the case with the financial domain, which uses its own unique vocabulary. Recent developments in NLP and deep learning including LLMs have made it possible to generate actionable financial sentiments using multiple sources including financial news, company fundamentals, technical indicators, as well social media microblogs posted on platforms such as StockTwits and X (formerly Twitter). We developed a financial social media sentiment analyzer (FinSoSent), which is a domain-specific large language model for the financial domain that was pretrained on financial news articles and fine-tuned and tested using several financial social media corpora. We conducted a large number of experiments using different learning rates, epochs, and batch sizes to yield the best performing model. Our model outperforms current state-of-the-art FSA models based on over 860 experiments, demonstrating the efficacy and effectiveness of FinSoSent. We also conducted experiments using ensemble models comprising FinSoSent and the other current state-of-the-art FSA models used in this research, and a slight performance improvement was obtained based on majority voting. Based on the results obtained across all models in these experiments, the significance of this study is that it highlights the fact that, despite the recent advances of LLMs, sentiment analysis even in domain-specific contexts remains a difficult research problem. Full article

Time series analysis is pivotal for business and financial decision making, especially with the increasing integration of the Internet of Things (IoT). However, leveraging time series data for forecasting requires extensive preprocessing to address challenges such as missing values, heteroscedasticity, seasonality, outliers, and noise. Different approaches are necessary for univariate and multivariate time series, Gaussian and non-Gaussian time series, and stationary versus non-stationary time series. Handling missing data alone is complex, demanding unique solutions for each type. Extracting statistical features, identifying data quality issues, and selecting appropriate cleaning and forecasting techniques require significant effort, time, and expertise. To streamline this process, we propose an automated strategy called Preptimize, which integrates statistical and machine learning techniques and recommends prediction model blueprints, suggesting the most suitable approaches for a given dataset as an initial step towards further analysis. Preptimize reads a sample from a large dataset and recommends the blueprint model based on optimization, making it easy to use even for non-experts. The results of various experiments indicated that Preptimize either outperformed or had comparable performance to benchmark models across multiple sectors, including stock prices, cryptocurrency, and power consumption prediction. This demonstrates the framework’s effectiveness in recommending suitable prediction models for various time series datasets, highlighting its broad applicability across different domains in time series forecasting. Full article

This study explores the diverse array of risks inherent in building renovation investments and proposes effective strategies for risk mitigation tailored to investors. Through a combination of qualitative analysis, expert interviews, and quantitative risk quantification techniques, the research identifies and evaluates key risk factors across regulatory, financial, technical, market, and other domains. Thorough due diligence, proactive stakeholder engagement, and contingency planning emerge as critical components of effective risk management in renovation projects. The study underscores the importance of proactive risk mitigation in enhancing project success and investor returns. By providing investors with a comprehensive understanding of the challenges they may face and practical strategies for addressing them, this research aims to empower stakeholders to make informed decisions and achieve positive outcomes in building renovation investments, ultimately contributing to a more resilient and sustainably built environment. Full article

In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model’s exceptional ability to withstand market turbulence and enhance profitability, affirming its substantial utility in quantitative stock selection. Full article

Green finance in the context of sustainable development sits within the broader discourse of environmental economics and sustainable finance. Their integration has become imperative in addressing global challenges, with the aims of understanding how financial mechanisms can be aligned with sustainability goals, investigating the role of green finance in promoting environmentally friendly investments, and fostering sustainable development. This bibliometric analysis explores the evolution, trends, and challenges in green finance research. It examines 436 articles published between 2016 and 2024, revealing insights into influential publications, authors, journals, institutions, and countries engaged in green finance for sustainability. The study identifies China, the UK, and Pakistan as leaders in research output and citation impact. Furthermore, it highlights the interdisciplinary nature of green finance, reflected in diverse publication outlets spanning environmental, social, and economic domains. The analysis underscores the increasing global interest in green finance, as evidenced by the growing citation rates over time. Key findings include the pivotal role of green finance in energy efficiency, renewable energy development, and the promotion of sustainable economic growth. Overall, this research provides valuable insights for policymakers, researchers, and practitioners, emphasizing the importance of interdisciplinary collaboration and continued research efforts in advancing sustainable finance agendas. Full article

This study presents a comprehensive bibliometric analysis of studies on financial information/accounting manipulation. The dataset of research includes 1.266 studies from the Web of Science database for the period 1991–2023. All studies included in the research contain either the term ‘financial information manipulation’ or ‘accounting manipulation’ in the topic (title, abstract, or keywords). The bibliometric network mapping technique was used for the analysis of the data. The analysis was conducted utilizing the Biblioshiny interface of the R package programs Bibliometrix and Vosviewer. The results pointed out a notable upward trend in the publication and citation rates of financial information/accounting manipulation studies over the last two decades. Several key findings were identified. Firstly, a substantial rise in research output on financial information/accounting manipulation was observed, particularly after 2000, driven by global financial scandals. Secondly, prolific contributors to this field include authors such as Valaskova and Durana. Thirdly, the United States leads in research output, with significant contributions from institutions like the State University System of Florida and the State University System of Ohio. Lastly, The Accounting Review was identified as the most prolific journal in this domain, with the Journal of Accounting Economics being the most impactful based on citations. The most frequently used keywords indicate that the research topics focus on earnings management as a method of manipulation, fraudulent financial reporting, and the relationship with corporate governance. The comprehensiveness of the bibliometric data lends itself to a further examination of how financial information/accounting manipulation has progressed as a subject in the literature since the 2000s. In addition, this study reveals the social and intellectual structures of the issue, the key research streams, and potential research directions for future research. Full article

The complexity and interconnection within the financial ecosystem demand innovative solutions to improve transparency, security, and efficiency in financial reporting and liquidity management, while also reducing accounting fraud. This paper presents Blockchain Financial Statements (BFS), an innovative accounting system designed to address accounting fraud, reduce data manipulation, and misrepresentation of company financial claims, by enhancing availability of the real-time and tamper-proof accounting data, underpinned by a verifiable approach to financial transactions and reporting. The primary goal of this research is to design, develop, and validate a blockchain-based accounting prototype—the BFS system—that can automate transformation of transactional data, generated by traditional business activity into comprehensive financial statements. Incorporating a Design Science Research Methodology with Domain-Driven Design, this study constructs a BFS artefact that harmonises accounting standards with blockchain technology and business orchestration. The resulting Java implementation of the BFS system demonstrates successful integration of blockchain technology into accounting practices, showing potential in real-time validation of transactions, immutable record-keeping, and enhancement of transparency and efficiency of financial reporting. The BFS framework and implementation signify an advancement in the application of blockchain technology in accounting. It offers a functional solution that enhances transparency, accuracy, and efficiency of financial transactions between banks and businesses. This research underlines the necessity for further exploration into blockchain’s potential within accounting systems, suggesting a promising direction for future innovations in tamper-evident financial reporting and liquidity management. Full article

The early detection of sickle cell disease (SCD) is vital to reduce mortality among affected children. Suriname currently lacks a newborn screening programme (NSP) for SCD. We performed a pilot programme to evaluate the scalability of such an initiative. Dried blood spots were collected from five birth centres and subjected to electrophoresis analysis. The programme scalability was evaluated using the non-adoption, abandonment, scale-up, spread, and sustainability framework. Challenges across six domains (illness, technology, value proposition, adopter system, organisation, and societal system), were categorised hierarchically as simple 😊, complicated 😐, or complex 😢. It has been proven that implementing programmes with mainly complicated challenges is difficult and those in mainly complex areas may be unachievable. SCD was detected in 33 of 5185 (0.64%) successfully screened newborns. Most of the domains were classified as simple or complicated. Disease detection and technology suitability for screening in Suriname were confirmed, with favourable parental acceptance. Only minor routine adjustment was required from the medical staff for programme implementation. Complex challenges included a reliance on external suppliers for technical maintenance, ensuring timely access to specialised paediatric care for affected newborns, and securing sustainable financial funding. Scaling up is challenging but feasible, particularly with a targeted focus on identified complex challenges. Full article

In an increasingly mobile world, the integration of immigrants and displaced individuals is an important factor in creating cohesive and inclusive societies. Integration has different dimensions; this scoping review examines the conceptualization and measurement of economic integration among immigrants and refugees in the United States. Quantitative peer-reviewed journal papers measuring or conceptualizing the economic integration of first-generation documented adult immigrants or refugees in the United States, as well as relevant conceptual or theory papers on this topic, were included in the review. The search strategy included an online search of the Web of Science Core Collection, PsycINFO, Applied Social Sciences Index and Abstracts (ASSIA), and EconLit. Additional search strategies included scanning the reference lists of studies identified as relevant in the initial database search. An analysis of 72 studies included in the review using a data extraction table reveals seven key domains of economic integration: income and economic security, employment and occupational categories, assets and use of financial services, neighborhood and housing, health, education, and use of public assistance. Income and economic security emerged as the most common indicators of integration in the reviewed studies. Notably, less than half of the reviewed publications had a multidimensional approach to defining or measuring economic integration, and the majority of studies were focused on immigrants, with a smaller proportion dedicated to refugees. This review emphasizes the need for comprehensive frameworks in assessing economic integration among immigrants and refugees, reflecting the multifaceted nature of their economic integration experiences. Full article

The Republic of Serbia is traditionally a country of emigration, especially since the 1960s. As a result of this emigration, return migration has become an increasingly intensive migratory process in the 21st century. This study aims to examine the factors behind return migration, as well as to explore the characteristics of the reintegration process in Serbia, including the sustainability of return. This paper is based on a survey (N = 172) and interviews (N = 20) conducted with return migrants in Serbia. The research findings point to the diversity of the return migration factors, among which a longing for the country of origin is singled out as the most important. Regarding the reintegration process, this study highlights several differences that are apparent between retired returnees on the one hand and other returnees (students, employed, unemployed) on the other. The results show that the satisfaction with quality of life upon return is higher among older returnees and that the satisfaction with quality of life decreases as the respondents’ level of education increases. It is also found that the sustainability of return is connected to the life satisfaction and that respondents who plan to migrate again are the least satisfied with the quality of life compared to those who plan to stay and those who have not decided yet. This paper provides insights into some of the critical elements of the return migration and reintegration process in Serbia. Since return migrants can contribute to sustainable socio-economic development due to their human, social and financial capital, this study may be of relevance to the development of strategies and the implementation of policies in the domain of migration governance. Full article

The proliferation of cloud computing has amplified the need for robust privacy-preserving technologies, particularly when dealing with sensitive financial and human resources (HR) data. However, traditional differential privacy methods often struggle to balance rigorous privacy protections with maintaining data utility. This study introduces DPShield, an optimized adaptive framework that enhances the trade-off between privacy guarantees and data utility in cloud environments. DPShield leverages advanced differential privacy techniques, including dynamic noise-injection mechanisms tailored to data sensitivity, cumulative privacy loss tracking, and domain-specific optimizations. Through comprehensive evaluations on synthetic financial and real-world HR datasets, DPShield demonstrated a remarkable 21.7% improvement in aggregate query accuracy over existing differential privacy approaches. Moreover, it maintained machine learning model accuracy within 5% of non-private benchmarks, ensuring high utility for predictive analytics. These achievements signify a major advancement in differential privacy, offering a scalable solution that harmonizes robust privacy assurances with practical data analysis needs. DPShield’s domain adaptability and seamless integration with cloud architectures underscore its potential as a versatile privacy-enhancing tool. This work bridges the gap between theoretical privacy guarantees and practical implementation demands, paving the way for more secure, ethical, and insightful data usage in cloud computing environments. Full article

With the rise of Large Language Models (LLMs), distinguishing between genuine and AI-generated content, particularly in finance, has become challenging. Previous studies have focused on binary identification of ChatGPT-generated content, overlooking other AI tools used for text regeneration. This study addresses this gap by examining various AI-regenerated content types in the finance domain. Objective: The study aims to differentiate between human-generated financial content and AI-regenerated content, specifically focusing on ChatGPT, QuillBot, and SpinBot. It constructs a dataset comprising real text and AI-regenerated text for this purpose. Contribution: This research contributes to the field by providing a dataset that includes various types of AI-regenerated financial content. It also evaluates the performance of different models, particularly highlighting the effectiveness of the Bidirectional Encoder Representations from the Transformers Base Cased model in distinguishing between these content types. Methods: The dataset is meticulously preprocessed to ensure quality and reliability. Various models, including Bidirectional Encoder Representations Base Cased, are fine-tuned and compared with traditional machine learning models using TFIDF and Word2Vec approaches. Results: The Bidirectional Encoder Representations Base Cased model outperforms other models, achieving an accuracy, precision, recall, and F1 score of 0.73, 0.73, 0.73, and 0.72 respectively, in distinguishing between real and AI-regenerated financial content. Conclusions: This study demonstrates the effectiveness of the Bidirectional Encoder Representations base model in differentiating between human-generated financial content and AI-regenerated content. It highlights the importance of considering various AI tools in identifying synthetic content, particularly in the finance domain in Pakistan. Full article

Introduction: With the passage of the Physician Payment Sunshine Act, there has been increased transparency regarding the industrial financial relations that physicians have. Orthopedic surgeons have been highly studied in this domain with approximately 50% of all orthopedic surgeons engaging in industrial financial relationships. Furthermore, an increasing number of orthopedic surgeons are seeking fellowship training with pediatric fellowship programs gaining popularity in recent years. The purpose of this study is to evaluate the impact various pediatric orthopedic fellowship programs have on industry earnings and academic productivity. Methods: Pediatric orthopedic fellowship programs were identified via the Orthopedic Society of North America (POSNA) website. Information on individual fellowship programs was obtained from their respective websites. Academic productivity was measured via an aggregate of all employed physicians’ H-index at a specific fellowship as found on the Scopus website. The Open Payments Database (OPD) website was used to assess lifetime industry earnings. Other variables such as Newsweek or Doximity ranking were taken directly from relevant websites. Statistical analysis was performed using a Kruskal–Wallis test with Bonferroni correction and Mann–Whitney U-test. Results: A total of 43 pediatric orthopedic surgery fellowships in the United States were identified with a total of 392 physicians as fellowship faculty. Complete OPD and H-index information were available for 336 of those physicians (85.7%). On average, there were 7.81 ± 5.18 physicians and 1.56 ± 0.93 fellows per program. The mean combined physician H-index was 117.23 ± 122.51, and the mean combined physician lifetime supplemental earnings in dollars was $646,684.37 ± $1,159,507.17. There was no significant relationship between region of pediatric orthopedic fellowship, Newsweek ranking of affiliated hospital, Doximity ranking of affiliated hospital, presence of MBA program, type of program (public, private, mixed), and the lifetime industry earnings or academic productivity of program graduates. Conclusions: Despite the observed lack of statistical significance, there were clear trends observed with fellowship programs in the northeast and west coast regions being the highest earning and fellowship programs with top 10 Newsweek ranking of affiliated hospital having by far the greatest industry earnings. Sample size limitations likely prevented the detection of statistical significance. Future studies should examine if any relation exists when accounting for type of industry payment received and case volume per fellowship program. Full article