Financial Contexts Research Articles

Development of Minimum Competency Assessment (MCA)-Like Mathematics Problems Using Financial Context in Algebra

The asesmen kemampuan minimum or Minimum Competency Assessment (MCA) is a new form of assessment in Indonesia implemented in several schools since 2021. Implementing MCA aims to prepare students for the skills needed in the 21st century. However, the availability of MCA-type mathematics problems is still limited for learning in printed books and online-based media. This research aims to produce MCA-like mathematics problems using financial contexts in algebra that are valid, practical, and effective. This research employed a development study consisting of six stages: preliminary, self-evaluation, expert reviews, one-to-one, small group, and field test stages. The test subjects were 85 students of grade 11 in one vocational high school in Sigli, Aceh, Indonesia. The research instruments were a validation sheet, an interview sheet, and a practicality and effectiveness test questionnaire. This research has produced 21 MCA-type items with a financial context in algebra that is valid, practical, and effective. Therefore, the generated mathematics problems can be used by teachers to improve students' numeracy and financial literacy skills.

Federated machine learning in finance: A systematic review on technical architecture and financial applications

Abstract. With the advancement of technology, an increasing amount of data is stored online, including substantial amounts of personal information. Especially in the financial industry, where large sums of money are often involved, protecting personal privacy is crucial. Federated learning, as a privacy-preserving distributed machine learning method, offers a solution to these challenges by enabling data privacy while addressing the difficulties of data sharing in the financial sectorissues that frequently impede innovation, risk management, and fraud detection. This paper delves into the principles of federated learning algorithms, exploring their mechanisms in detail. Besides, applications and case studies in applications such as financial fraud detection, supply chain financing prediction, and other financial services are examined. Furthermore, sample dataset will be introduced. Moreover, the potential benefits and challenges of implementing federated learning within financial contexts are also assessed. Finally, promising research directions for the application of federated learning in the financial industry are outlined.

Ai-Driven Big Data Transformation And Personally Identifiable Information Security In Financial Data: A Systematic Review

This systematic review explores the impact of adopting artificial intelligence (AI) to analyze and transform big data in financial and economic contexts, with a specific focus on the privacy and security of personally identifiable information (PII). By examining 37 articles spanning the latest advancements in AI-driven big data technologies, the review identifies both opportunities and challenges in safeguarding PII during financial data transformation. Key findings reveal that while AI enhances data processing capabilities—enabling faster insights and predictive accuracy in economic trends—PII faces increased risks due to sophisticated data aggregation and correlation techniques. The review categorizes major AI methodologies used, including machine learning algorithms, natural language processing, and predictive analytics, highlighting how each can affect data privacy. The findings suggest that, to maintain trust, organizations must adopt AI responsibly, integrating privacy-by-design principles and adhering to data governance standards. This review contributes to a clearer understanding of the interplay between AI and PII protection, offering practical insights for stakeholders in the financial sector aiming to harness AI while prioritizing ethical data handling.

Deep Learning in Finance: A Survey of Applications and Techniques

Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in processing and analyzing complex and large datasets. This paper provides a comprehensive overview of key deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief Networks (DBNs), Transformers, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations and learning processes, this study offers new insights into how these models are applied in real-world financial contexts, highlighting their specific advantages and limitations in tasks such as algorithmic trading, risk management, and portfolio optimization. It also examines recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity. These insights can guide future research directions toward developing more efficient, robust, and explainable financial models that address the evolving needs of the financial sector.

Green AI in the finance industry: Exploring the impact of feature engineering on the accuracy and computational time of Machine Learning models

As research and practice in Artificial Intelligence (AI) applications rapidly expand, the support for AI deployment is also increasing. While the abundance of data allows for sophisticated feature engineering techniques that can enhance accuracy, it is crucial to highlight both the computational costs and the efficiency with which these models operate. This paper compares the processing time and accuracy of individual and hybrid Machine Learning (ML) models in predicting customer loyalty within financial contexts. Frameworks that incorporate feature engineering and green AI principles are used separately in both individual and hybrid approaches. The individual models are the commonly used regressor-based algorithms applied to business problems. The hybrid models first use k-Means to cluster customers, followed by the application of individual regressor-based models (e.g., decision trees, gradient boosting, and LightGBM). The present results show that using fewer features results in only a marginally lower accuracy compared to models with more features (a difference of ≈0.01 in MAE when comparing the use of 18 versus 85 features). Additionally, this article clearly demonstrate the trade-off between higher accuracy and longer computational time in hybrid ML models versus lower accuracy and shorter computational time in individual models when predicting customer loyalty. Hybrid models exhibit a lower MSE (≈0.88) compared to individual models (≈0.91). These findings provide managers with insights on selecting the most appropriate model based on their organization’s specific needs.

College Women’s Establishing of Peer Social-relationships and Communicating about Pandemic-related Political Identities and Topics

This mixed-methods study examines two research areas concerning whether U.S. women college students, compared with men, enrolled in online classes during the pandemic suffered from stress regarding their ability to socialize and negotiate a community/national political issue(s) affecting them negatively academically in terms of their grade point average and course outcomes, as well as whether the women would experience a sense of belonging and camaraderie with classmates by discussing topics impacting their identities and circumstances in terms of their individual and communal, social-relational, health, and financial pandemic contexts, which might also influence them negatively academically as stressors linked to their course outcomes and progress. Conducted in a rural, geographically marginalized area, the study implements survey and short-answer question instruments and utilizes an exploratory case-study design and a convergent-parallel strategy assessing 188 women and 188 men participants, including racial/ethnic minority and low socio-economic status (SES) women, with women’s median age of 23. of women and men surveyed, the former reported statistically significantly higher stress levels than men connected to the women’s need to socialize and tackle pandemic-oriented community and national political issues affecting their lives and statuses, which subsequently influenced their academic progress. Overall, the pandemic damaged women, especially racial/ethnic minorities, more than men, with women having higher stress rates revolving around their need for socializing (59% versus 33%) and addressing political issues (54% versus 32%), stressors hampering the women’s academic outcomes and status, as well as some experiencing additional stress regarding health and financial concerns. In response to these settings, 21% of women (n = 40) desired to discuss controversial topics, such as their pandemic stressors and related experiences in the classroom, including the pandemic’s political consequences for themselves, to promote self-expression, forge bonds, and seek and provide assistance in addressing all classmates’ academic, personal, and community needs.

A Novel Multi-criteria Decision Making Approach Based on Bipolar Neutrosophic Set for Evaluating Financial Markets in Egypt

In the evolving landscape of financial markets, assessing company performance with precision and reliability is crucial for investors and analysts. This paper introduces an innovative framework for evaluating the financial performance of companies listed on the Egyptian Exchange (EGX) by integrating Bipolar Neutrosophic Sets (BNS) with Multi-Criteria Decision-Making (MCDM) methods. The proposed framework utilizes BNS to incorporate the inherent uncertainty and imprecision of financial data, and integrates MCDM methods to provide a comprehensive assessment of multiple performance indicators. Specifically, the evaluation focuses on seven critical criteria: Stock Returns, Volatility, Liquidity, Market Capitalization, Earnings Growth, Revenue Growth, and Analyst Ratings. By utilizing Bipolar Neutrosophic Weighted Average (BNWA) operators and similarity measures, the study effectively ranks companies through a nuanced analysis of these criteria. The results highlight the effectiveness of the proposed approach in providing a robust and accurate financial performance evaluation framework tailored to the unique dynamics of the Egyptian market. The study demonstrates that the hybrid model not only accommodates variations in criteria weights but also offers valuable insights for investment decisions in emerging markets. The approach exhibits resilience and adaptability, providing a more informed and reliable basis for evaluating financial performance. This research contributes to the advancement of financial performance assessment methodologies by showcasing the potential of combining BNS with MCDM techniques. The research also emphasizes the framework's practical applications and suggests future research directions for refining the model and exploring its use in diverse financial contexts.

The Role of Nudges and Behavioral Interventions in Financial Decision Making

Financial decision-making, both at individual and institutional levels, plays a critical role in determining economic outcomes. Traditional economic theory posits that individuals are rational agents who make decisions based on complete information and a consistent set of preferences. However, behavioral economics has challenged this notion, highlighting cognitive biases, heuristics, and social influences that can drive suboptimal decisions. Nudges and behavioral interventions have emerged as tools to guide individuals toward better financial choices without restricting their freedom of choice. The purpose of this research is to present a descriptive investigation of behavioral interventions and nudges in financial decision-making. The main cognitive biases influencing financial behavior are first described, and then several nudge kinds and their applicability in diverse financial contexts— retirement savings, debt management, spending control, and investment decisions—are explored. Along with reviewing empirical data on nudges' efficacy, the paper addresses the ethical issues surrounding their application and pinpoints the variables that affect their effectiveness. This study aims to educate consumers, financial institutions, and policymakers about the potential advantages and constraints of behavioral interventions by providing a thorough overview of how nudges might impact financial decision-making. Keywords: Nudges, Behavioral Intervention, Financial Decision-Making, Cognitive Biases, Financial Literacy.

Competition, economic freedom and bank stability: Evidence from ASEAN

This study examines how economic freedom and competition affect bank stability. We use data from 70 ASEAN-4 banks from 2007 to 2019 using the system generalized technique of moments. Results corroborate competition-fragility hypothesis. Market strength (or less competition) can boost bank stability. However, in the ASEAN-4 area, competition and bank stability have a non-linear relationship, suggesting that bank stability may decline after market strength exceeds a threshold. Financial and economic freedom also boosts bank stability. This implies banks in free financial and economic contexts are more stable. Banks with more market dominance in nations with more economic or financial autonomy may also be more unstable. The findings suggest that authorities should allow some competition and economic flexibility to keep banks stable. The study examined ASEAN-4 economic freedom’s effects empirically for the first time. It illuminates competitiveness and bank stability.

Integration of Modified Heston Model (Jump and Sentiment Factor) with Artificial Neural Networks (ANN) for Volatility Forecasting in Financial Markets

This study examined a new methodology for predicting volatility in financial markets by combining the modified Heston model (JSF) with Artificial Neural Networks (ANN). Forecasting volatility is essential in various financial contexts, such as risk management, option pricing and portfolio optimization. Although the traditional Heston model is effective but it does not account for additional elements like jumps and investor sentiment factors, which impact asset price dynamics. To address this limitation, the extended Heston model with jump and sentiment variables helped in enhancing its ability to capture complex market trends. We utilized ANN's predictive capabilities on the modified Heston model to improve volatility predictions using historical random market data from NASDAQ Composite. The ANN was trained on simulated data from the modified Heston model and assessed on its effectiveness in forecasting volatility. This approach helped in developing a forecasting hybrid semi-parametric model known as the modified-Heston-ANN model. Our findings revealed that this integrated approach outperformed the individual model, offering more precise and robust volatility predictions

Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification

The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability.

Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation

This study investigates the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, with a focus on the ethical dimensions and perceived algorithmic fairness. Drawing upon organizational justice theory and the heuristic–systematic model, we examine how algorithm transparency, accountability, and legitimacy influence users’ perceptions of fairness and, subsequently, their satisfaction with and likelihood to recommend AI-driven financial inclusion services. Through a survey-based quantitative analysis of 675 users in China, our results reveal that perceived algorithmic fairness acts as a significant mediating factor between the ethical attributes of AI systems and the user responses. Specifically, higher levels of transparency, accountability, and legitimacy enhance users’ perceptions of fairness, which, in turn, significantly increases both their satisfaction with AI-facilitated financial inclusion services and their likelihood to recommend them. This research contributes to the literature on AI ethics by empirically demonstrating the critical role of transparent, accountable, and legitimate AI practices in fostering positive user outcomes. Moreover, it addresses a significant gap in the understanding of the ethical implications of AI in financial inclusion contexts, offering valuable insights for both researchers and practitioners in this rapidly evolving field.

A cancellable iris template protection scheme based on inverse merger and Bloom filter

Iris recognition has found extensive applications in real-world situations and financial contexts. However, Iris template protection schemes are highly vulnerable to well-planned attacks that can lead to the leakage of personal information. Once biological information is compromised, this loss is irreversible for the individual. Cancelable protection schemes for iris templates based on the Bloom filter have substantial attention in the field of iris biometrics. Nevertheless, Bloom filter-based template protection schemes face specific security challenges. Therefore, it is crucial to propose a method to protect iris templates that is both secure and efficient. To address irreversible limitations in security analysis, we propose a template protection scheme, a cancelable iris biometric protection scheme based on inverse merger and Bloom filter. The primary idea of the proposed scheme is to perform an inverse merger operation on the acquired codewords before mapping the iris templates to the Bloom filter specifically. Through a comparison of the sizes between the original templates and their inverted counterparts, the template with the smaller size is chosen as the definitive result, subsequently being mapped into the Bloom filter. Our proposed scheme exhibits significant advancements in accuracy across multiple datasets, as evidenced by empirical validations. In the optimal case, our model achieves an excellent performance of 98.04% in terms of GAR, while achieving a significant reduction of 0.51% in terms of EER. Furthermore, a comparative analysis with existing iris template protection methods is performed to evaluate its relative effectiveness in resisting the attack of averaging the columns of a block. The results demonstrate that the scheme exhibits robust resistance to such attacks. The experimental analysis demonstrated that the scheme provided a good balance between accuracy and safety.

The Impact of Artificial Intelligence on Financial Markets and Business Operations

The integration of artificial intelligence (AI) in financial markets and business operations has emerged as a transformative force, reshaping traditional practices and unlocking new opportunities. This paper presents a systematic literature review encompassing a wide array of studies on AI applications in finance and business. The review explores AI's role in enhancing financial forecasting, trading strategies, risk management, and fraud detection. It discusses various AI techniques such as machine learning, deep learning, and natural language processing, highlighting their effectiveness in analysing vast datasets and improving decision-making processes. Moreover, the review addresses the implications of AI adoption in optimising business operations, including process automation, predictive analytics, and customer experience enhancement. Key themes include the benefits of AI-driven innovations, such as increased efficiency, cost reduction, and personalised services, alongside challenges related to job displacement, algorithmic bias, and regulatory frameworks. The paper concludes with insights into future research directions aimed at advancing AI's interpretability, transparency, and ethical deployment in financial and business contexts.

The empire strikes back: the financialization of rental housing – a new frontier of accumulation and precarity

ABSTRACT The financialization of rental housing, driven by post-GFC financial contexts, sees assets concentrated in the hands of corporate landlords aided by technological innovations and state support. Foreclosures and evictions, especially in GFC-affected areas, enable corporate acquisition of property at bargain rates, followed by eviction strategies to increase turnover and rental prices, bypassing regulations. This upward distribution of value is supported by growing labor precarity, intertwined with rising rental prices globally. Labor market precarity, linked to stagnant wages, exacerbates housing precarity, creating new forms of accumulation by dispossession. The hypothesis suggests a convergence in “regulated deregulation,” blending formality and informality through financial processes, digital mechanisms, and public policies. Renting becomes a bridging mechanism, transforming housing into a service, managing time and income generation, with potential scalability through digital mechanisms. This marks a departure from historical constraints, potentially establishing unprecedented connections between informality and finance.

Law-invariant return and star-shaped risk measures

This paper presents novel characterization results for classes of law-invariant star-shaped functionals. We begin by establishing characterizations for positively homogeneous and star-shaped functionals that exhibit second- or convex-order stochastic dominance consistency. Building on these characterizations, we proceed to derive Kusuoka-type representations for these functionals, shedding light on their mathematical structure and intimate connections to Value-at-Risk and Expected Shortfall. Furthermore, we offer representations of general law-invariant star-shaped functionals as robustifications of Value-at-Risk. Notably, our results are versatile, accommodating settings that may, or may not, involve monotonicity and/or cash-additivity. All of these characterizations are developed within a general locally convex topological space of random variables, ensuring the broad applicability of our results in various financial, insurance and probabilistic contexts.

Portfolio Optimization with Prediction-Based Return Using Long Short-Term Memory Neural Networks: Testing on Upward and Downward European Markets

AbstractIn recent years, artificial intelligence has helped to improve processes and performance in many different areas: in the field of portfolio optimization, the inputs play a crucial role, and the use of machine learning algorithms can improve the estimation of the inputs to create robust portfolios able to generate returns consistently. This paper combines classical mean–variance optimization and machine learning techniques, concretely long short-term memory neural networks to provide more accurate predicted returns and generate profitable portfolios for 10 holding periods that present different financial contexts. The proposed algorithm is trained and tested with historical EURO STOXX 50® Index data from January 2015 to December 2020, and from January 2021 to June 2022, respectively. Empirical results show that our LSTM neural networks are able to achieve minor predictive errors since the average of the MSE of the 10 holding periods is 0.00047, the average of the MAE is 0.01634, and predict the direction of returns with an average accuracy over the 10 investment periods of 95.8%. Our prediction-based portfolios consistently beat the EURO STOXX 50® Index, achieving superior positive results even during bear markets.

Is It Worth Investing in Tokens? Investment Performance of Digital Tokens in Financial and Axiological Contexts

AbstractThe paper focuses on financial and axiological aspects of the new instrument in the digital economy, such as digital tokens (DTs). The purpose of the article is to examine the investment performance of certain types of DTs. We suggest a categorisation of DTs according to the types of assets that they represent and analyse them in terms of profitability, risk, and effectiveness. The investment performance of different types of DTs was compared with stock market indices and commodity prices. The empirical source of information is data from the coinpaprika platform from January 2018 to July 2022. It occurs that DTs demonstrate, on average, lower investment performance than traditional instruments. However, there is a wide group of the former that can be included in the portfolio as their investment performance is higher than the one represented by stock market assets. Our contribution comprises, first, the extension of existing research on tokenization and tokens to include the investment aspect. Second, we develop and apply the original proposal of DT classification, which takes into account the hitherto neglected aspect of worldview valuation of a given DT type by market participants. Finally, we assess the investment performance of DTs (both in terms of our proposed classification and in the context of stock market indices). We also specify recommendations for investors.

Behavioral Finance: The Impact of Investor Expectation on Financial Decision-Making

Behavioral finance challenges traditional financial theories by questioning the assumption of investor rationality, highlighting the influence of cognitive biases, emotions, and psychological factors on decision-making. This deviation from conventional models like the Efficient Market Hypothesis recognizes the complexity of human decision-making in financial contexts. The field focuses on key phenomena: loss aversion, market anomalies, the framing effect, and the endowment effect. Loss aversion reveals a stronger reaction to losses than equivalent gains, influencing overly cautious decisions. Market anomalies, such as momentum and trend reversals, challenge efficient market expectations. The framing effect shows how decision-making is swayed by information presentation. The endowment effect impacts perceived asset value, affecting financial decisions. These biases significantly impact investor behavior, potentially leading to suboptimal portfolio choices. Recognizing these biases is crucial for making informed financial decisions. Behavioral finance research has profound implications, emphasizing the integration of behavioral insights with traditional models. Future studies should explore cumulative behavioral effects, conduct cross-cultural analyses, and investigate diverse market conditions for more effective investment strategies and regulations. Understanding global financial behavior through behavioral finance is essential for comprehensive insights into market dynamics and decision-making processes.

Financial Empowerment for Adults Living With Brain Injury: Systems Advocacy for Inclusive and Accessible Financial Contexts to Improve Financial Well-being

Financial Empowerment for Adults Living With Brain Injury: Systems Advocacy for Inclusive and Accessible Financial Contexts to Improve Financial Well-being