Hasil untuk "Finance"

B.S. Prashanth, Manoj Kumar, Ariful Hoque et al.

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal.We utilize a supervised learning pipeline that incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives.Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

Paul Langley, Andrew Leyshon

Abstract With reference to Elon Musk’s FinTech strategy for X, the social media platform formerly known as Twitter, this essay critically interrogates the evolution of North American and European FinTech economies toward what is typically called ‘embedded finance’; that is, the technological integration of monetary and financial services into discrete social interactions and economic transactions by nonfinancial companies. We argue that embedded finance furthers the disappearance of FinTech as an evident market domain of technologically facilitated monetary and financial relations. Specialist FinTech startup intermediaries are receding into the background of an institutional and digital landscape shaped by strong monopolization tendencies. FinTech economies are increasingly dominated by major platform firms with the assistance of banks. Relatedly, FinTech services have become ubiquitous to the extent that they are taken for granted by people who are configured as platform users that are ripe for rent capture, rather than as sovereign consumers searching for products. The disappearance of FinTech should not be confused with its demise, however. Disappearance is the fullest expression of the transformative appearance of FinTech in people’s everyday monetary and financial lives over the last quarter century.

Claire Barraud

This article proposes a complementary theoretical framework in behavioural finance by interpreting financial markets during boom-and-bust episodes as a Le Bonian crowd. While behavioural finance has documented the limits of individual rationality through biases and heuristics, these contributions remain primarily microeconomic. A second, more macroeconomic strand appears to treat market instability as the aggregated result of individual biases, although it generally does so without an explicit theoretical account of how such aggregation operates. In contrast, this paper adopts a macro-psychological -and therefore macroeconomic -perspective, drawing on classical crowd psychology (Le Bon, 1895; Tarde, 1901; Freud, 1921). The central claim is that during speculative booms and crashes, markets behave as psychological crowds governed by unconscious processes, suggestion, emotional contagion, and impulsive action. These episodes cannot be understood merely as the sum of individual departures from rationality, but as the emergence of a collective mental state that follows its own psychological laws. By reintroducing crowd psychology into behavioural finance, this paper clarifies the mechanisms through which market-wide irrationality arises and offers a theoretical foundation for a macrobehavioural understanding of financial instability.

Zichen Chen, Jiaao Chen, Jianda Chen et al.

Standard benchmarks fixate on how well large language model (LLM) agents perform in finance, yet say little about whether they are safe to deploy. We argue that accuracy metrics and return-based scores provide an illusion of reliability, overlooking vulnerabilities such as hallucinated facts, stale data, and adversarial prompt manipulation. We take a firm position: financial LLM agents should be evaluated first and foremost on their risk profile, not on their point-estimate performance. Drawing on risk-engineering principles, we outline a three-level agenda: model, workflow, and system, for stress-testing LLM agents under realistic failure modes. To illustrate why this shift is urgent, we audit six API-based and open-weights LLM agents on three high-impact tasks and uncover hidden weaknesses that conventional benchmarks miss. We conclude with actionable recommendations for researchers, practitioners, and regulators: audit risk-aware metrics in future studies, publish stress scenarios alongside datasets, and treat ``safety budget'' as a primary success criterion. Only by redefining what ``good'' looks like can the community responsibly advance AI-driven finance.

Gagan Deep, Akash Deep, William Lamptey

We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothesis-driven signal generation with reinforcement learning and strict out-of-sample testing. The framework enforces strict information set discipline, employs rolling window validation across 34 independent test periods, maintains complete interpretability through natural language hypothesis explanations, and incorporates realistic transaction costs and position constraints. Validating five market microstructure patterns across 100 US equities from 2015 to 2024, the system yields modest annualized returns (0.55%, Sharpe ratio 0.33) with exceptional downside protection (maximum drawdown -2.76%) and market-neutral characteristics (beta = 0.058). Performance exhibits strong regime dependence, generating positive returns during high-volatility periods (0.60% quarterly, 2020-2024) while underperforming in stable markets (-0.16%, 2015-2019). We report statistically insignificant aggregate results (p-value 0.34) to demonstrate a reproducible, honest validation protocol that prioritizes interpretability and extends naturally to advanced hypothesis generators, including large language models. The key empirical finding reveals that daily OHLCV-based microstructure signals require elevated information arrival and trading activity to function effectively. The framework provides complete mathematical specifications and open-source implementation, establishing a template for rigorous trading system evaluation that addresses the reproducibility crisis in quantitative finance research. For researchers, practitioners, and regulators, this work demonstrates that interpretable algorithmic trading strategies can be rigorously validated without sacrificing transparency or regulatory compliance.

Fengbin Zhu, Junfeng Li, Liangming Pan et al.

Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc. In this work, we introduce FinTMMBench, the first comprehensive benchmark for evaluating temporal-aware multi-modal Retrieval-Augmented Generation (RAG) systems in finance. Built from heterologous data of NASDAQ 100 companies, FinTMMBench offers three significant advantages. 1) Multi-modal Corpus: It encompasses a hybrid of financial tables, news articles, daily stock prices, and visual technical charts as the corpus. 2) Temporal-aware Questions: Each question requires the retrieval and interpretation of its relevant data over a specific time period, including daily, weekly, monthly, quarterly, and annual periods. 3) Diverse Financial Analysis Tasks: The questions involve 10 different financial analysis tasks designed by domain experts, including information extraction, trend analysis, sentiment analysis and event detection, etc. We further propose a novel TMMHybridRAG method, which first leverages LLMs to convert data from other modalities (e.g., tabular, visual and time-series data) into textual format and then incorporates temporal information in each node when constructing graphs and dense indexes. Its effectiveness has been validated in extensive experiments, but notable gaps remain, highlighting the challenges presented by our FinTMMBench.

Aaron Green, Zihan Nie, Hanzhen Qin et al.

Survival modeling predicts the time until an event occurs and is widely used in risk analysis; for example, it's used in medicine to predict the survival of a patient based on censored data. There is a need for large-scale, realistic, and freely available datasets for benchmarking artificial intelligence (AI) survival models. In this paper, we derive a suite of 16 survival modeling tasks from publicly available transaction data generated by lending of cryptocurrencies in Decentralized Finance (DeFi). Each task was constructed using an automated pipeline based on choices of index and outcome events. For example, the model predicts the time from when a user borrows cryptocurrency coins (index event) until their first repayment (outcome event). We formulate a survival benchmark consisting of a suite of 16 survival-time prediction tasks (FinSurvival). We also automatically create 16 corresponding classification problems for each task by thresholding the survival time using the restricted mean survival time. With over 7.5 million records, FinSurvival provides a suite of realistic financial modeling tasks that will spur future AI survival modeling research. Our evaluation indicated that these are challenging tasks that are not well addressed by existing methods. FinSurvival enables the evaluation of AI survival models applicable to traditional finance, industry, medicine, and commerce, which is currently hindered by the lack of large public datasets. Our benchmark demonstrates how AI models could assess opportunities and risks in DeFi. In the future, the FinSurvival benchmark pipeline can be used to create new benchmarks by incorporating more DeFi transactions and protocols as the use of cryptocurrency grows.

Faiza Bouzemlal, Ali Nabil Belouard

Exchange rate volatility can have socioeconomic challenges and a significant impact on export diversification of major global economies. The main objective of this article is to assess the symmetric and asymmetric effect of exchange rate volatility on Algerian export diversification. For this purpose, the autoregressive linear and nonlinear distributed lag (ARDL) model and annual data for the period 1990-2023 were used.The empirical findings using the estimation for time series data reveal that the volatility of the exchange rate has a symmetric effect on export diversification. The results revealed the presence of cointegration between the variables. The relationship between exchange rate volatility and export diversification in Algeria is positive and symmetric, which is contrary to conventional wisdom, as both currency depreciation and appreciation were found to boost diversification. Economic openness and GDP per capita significantly promote diversification, while investment and infrastructure surprisingly hinder it. Inflation also has an unexpected positive effect. The model adjusts quickly to equilibrium, though short-run results show mixed results. Diagnostic tests confirm robustness, except for serial correlation, corrected via Newey-West standard errors. This article suggests that policy makers should adopt different policies to address socio-economic challenges and keep the exchange rate stable in order to promote export diversification.

Alireza Azarberahman, Mahmoodreza Mohammadnejadi Modi

PurposeThis research examines the structure of financial markets by integrating game theory and fuzzy logic. The objective is to develop a differential game model that analyzes competition among financial firms within a specific industry.Design/methodology/approachThis study employs a differential game model, where players set service prices, dynamically influencing market shares and profits over time. The model incorporates two fuzzy criteria—market power (price-variable cost ratio) and product differentiation (Herfindahl-Hirschman index)—to assess market structure. These criteria are applied to data from Tehran Stock Exchange (TSE) industries, specifically banking, insurance, and e-commerce, to evaluate their respective market structures.FindingsThe results indicate that financial industries tend to be closer to perfect competition compared to other market structures. Additionally, a comparative analysis of the status of these industries in relation to each other reveals that the banking and the e-commerce industries exhibit characteristics of monopolistic competition, whereas the insurance industry aligns more closely with perfect competition. This study provides useful insights into player behavior and its implications for financial policy, aiding in market analysis and forecasting.Originality/valueThis research offers a novel approach by integrating game theory and fuzzy logic to analyze the structure of financial markets.

Jean Lee, Nicholas Stevens, Soyeon Caren Han et al.

Large Language Models (LLMs) have shown remarkable capabilities across a wide variety of Natural Language Processing (NLP) tasks and have attracted attention from multiple domains, including financial services. Despite the extensive research into general-domain LLMs, and their immense potential in finance, Financial LLM (FinLLM) research remains limited. This survey provides a comprehensive overview of FinLLMs, including their history, techniques, performance, and opportunities and challenges. Firstly, we present a chronological overview of general-domain Pre-trained Language Models (PLMs) through to current FinLLMs, including the GPT-series, selected open-source LLMs, and financial LMs. Secondly, we compare five techniques used across financial PLMs and FinLLMs, including training methods, training data, and fine-tuning methods. Thirdly, we summarize the performance evaluations of six benchmark tasks and datasets. In addition, we provide eight advanced financial NLP tasks and datasets for developing more sophisticated FinLLMs. Finally, we discuss the opportunities and the challenges facing FinLLMs, such as hallucination, privacy, and efficiency. To support AI research in finance, we compile a collection of accessible datasets and evaluation benchmarks on GitHub.

Baridhi Malakar

The dissertation consists of three essays on responsible and sustainable finance. I show that local communities should be seen as stakeholders to decisions made by corporations. In the first essay, I examine whether the imposition of fiduciary duty on municipal advisors affects bond yields and advising fees. Using a difference-in-differences analysis, I show that bond yields reduce by 9\% after the imposition of the SEC Municipal Advisor Rule. In the second essay, we analyze the impact of USD 40 billion of corporate subsidies given by U.S. local governments on their borrowing costs. We find that winning counties experience a 15 bps increase in bond yield spread as compared to the losing counties. In the third essay, we provide new evidence that the bankruptcy filing of a locally-headquartered and publicly-listed manufacturing firm imposes externalities on the local governments. Compared to matched counties with similar economic trends, municipal bond yields for affected counties increase by 10 bps within a year of the firm filing for bankruptcy. The final essay examines whether managers walk the talk on the environmental and social discussion. We train a deep-learning model on various corporate sustainability frameworks to construct a comprehensive Environmental and Social (E and S) dictionary. Using this dictionary, we find that the discussion of environmental topics in the earnings conference calls of U.S. public firms is associated with higher pollution abatement and more future green patents.

Rubina Zadourian

The objective of this work is the investigation of complexity, asymmetry, stochasticity and non-linearity of the financial and economic systems by using the tools of statistical mechanics and information theory. More precisely, this thesis concerns statistical-based modeling and empirical analyses with applications in finance, forecasting, production processes and game theory. In these areas the time dependence of probability distributions is of prime interest and can be measured or exactly calculated for model systems. The correlation coefficients and moments are among the useful quantities to describe the dynamics and the correlations between random variables. However, the full investigation can only be achieved if the probability distribution function of the variable is known; its derivation is one of the main focuses of the present work.

Amrutha Muralidhar, Muralidhar Lakkanna

The evolution of cryptocurrency and decentralized finance (DeFi) marks a significant shift in the financial landscape, making it more accessible, inclusive, and participative for various societal groups. However, this transition from traditional financial institutions to DeFi demands a meticulous policy framework that strikes a balance between innovation and safeguarding consumer interests, security, and regulatory compliance. In this script we explore the imperative need for regulatory frameworks overseeing cryptocurrencies and DeFi, aiming to leverage their potential for inclusive economic advancement. It underscores the prevalent challenges within conventional financial systems, juxtaposing them with the transformative potential offered by these emergent financial paradigms. By highlighting the role of robust regulations, we examine their capacity to ensure user security, fortify market resilience, and spur innovative strides. We aim to proffer viable strategies for formulating regulatory structures that harmonize the twin objectives of fostering innovation and upholding fairness within financial ecosystems.

Biplab Bhattacharjee, Shubham Kumar, Piyush Verma et al.

The increase in digital disruptions and changing preferences of different stakeholders has led to digital adoption in all hierarchies of business ecosystem. This study focused on the identification of the determinants of digitalization in unorganized small, localized retail outlets (Kirana stores) of an emerging economy. A theoretical model was constructed with certain modifications based on technology adoption models such as Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to study the impact on business performance in general and as an effect of pandemic. A survey of 285 Unorganized Localized Retail Outlets Stores from different regions of India was used to validate this theoretical model, and structural equation modeling was then further employed. The findings underscore that cost, compatibility, perceived ease of use, and perceived usefulness significantly affect the intention to digitalize. By addressing the post-pandemic impact of digitalization within an unorganized sector in an emerging economy, this study adds to the scant literature that exists in this context.

Dzikrina Fikrotus Salma, Eko Ruddy Cahyadi, Budi Purwanto

Micro business is the sector that has been hit the hardest by the Covid-19 Pandemic. Business capital assistance is the assistance most needed by micro businesses. However, micro enterprises have barriers to access and use of finance. This study aims to analyze the effect of BMT resilience, financial inclusion and financial literacy on the resilience of micro-enterprises during the Covid-19 pandemic with a total of 150 micro-enterprises and 18 BMT respondents in Brebes Regency. Data processing method in the form of quantitative descriptive analysis using SEM-PLS. The results of this study show that the resilience of micro-enterprises during the Covid-19 pandemic was influenced by BMT financial inclusion and financial literacy. BMT makes it easier for micro-enterprises who are unbankable to gain access to financial institutions to obtain business capital and provide basic knowledge about basic financial management so they can survive during a crisis due to a pandemic. On the other hand, BMT as a financial institution requires efforts to remain resilient in the face of the Covid-19 pandemic, among internal and external factors that have a significant effect on BMT resilience are strengthening human resources, capital, management, regulatory compliance, supervision and infrastructure. Keywords: BMT resilience, business resilience, Covid-19 Pandemic, financial inclusion, financial literacy

Khalil A. Alruwaitee

Predictions on stock market prices are a noble task owing to huge complex, dynamic, and chaotic surroundings. Fast ups and downs arise in the stock market due to influences from foreign merchandise, such as sensitive political, stockholder, economic, and emotional behaviour. In the stock market, incessant unsettlement is the main reason why financiers give away at the wrong time and frequently fail to get a profit. While financing in the stock market, the stakeholders should not disremember the gamble of payment rule and reveal their assets to greater dangers. Discovering economic time series data and exhibiting the relationship between the stock trend and past data is the main method to resolve the issue. Machine learning (ML), a conventional technique, has also been considered for its ability to predict financial markets. This manuscript proposes a new Predicting Stock Price Movements with Combined Deep Learning Models and Two-Tier Metaheuristic Optimization (PSPMCDL-TTMO) method. The PSPMCDL-TTMO methodology employs an optimal deep learning model to forecast stock price movements, determining whether prices will rise or fall. At the primary stage, the PSPMCDL-TTMO model utilizes data pre-processing using Z-score normalization to ensure that the input features are standardized for consistent performance. For feature selection (FS), the dingo optimizer algorithm (DOA) is employed to optimize the most relevant and impactful features from historical stock data. In addition, the multi-head attention bi-directional gated recurrent unit (MHA-BiGRU) model is used for stock price movement prediction. Finally, the hyperparameter range of the MHA-BiGRU model is implemented by the design of the equilibrium optimizer (EO) model. The experimentation outcome analysis of the PSPMCDL-TTMO approach takes place, and the results are inspected using various features. The investigational validation of the PSPMCDL-TTMO technique attained a superior CORR value of 0.9999 over existing models.

Agostino Capponi, Garud Iyengar, Jay Sethuraman

Financial markets are undergoing an unprecedented transformation. Technological advances have brought major improvements to the operations of financial services. While these advances promote improved accessibility and convenience, traditional finance shortcomings like lack of transparency and moral hazard frictions continue to plague centralized platforms, imposing societal costs. In this paper, we argue how these shortcomings and frictions are being mitigated by the decentralized finance (DeFi) ecosystem. We delve into the workings of smart contracts, the backbone of DeFi transactions, with an emphasis on those underpinning token exchange and lending services. We highlight the pros and cons of the novel form of decentralized governance introduced via the ownership of governance tokens. Despite its potential, the current DeFi infrastructure introduces operational risks to users, which we segment into five primary categories: consensus mechanisms, protocol, oracle, frontrunning, and systemic risks. We conclude by emphasizing the need for future research to focus on the scalability of existing blockchains, the improved design and interoperability of DeFi protocols, and the rigorous auditing of smart contracts.

Ni Kadek Reinita Andriyani, Putu Gde Arie Yudhistira

Objective: This study examines the relationship between customer experience and brand loyalty mediated by brand credibility and brand trust. Design/Methods/Approach: Primary data was collected using a questionnaire with a purposive sampling technique. A total of 363 respondents who had used one of the conventional travel agent services in Bali participated in this study. The Partial Least Square Structural Equation Model (PLS-SEM) was used to analyze the data through outer and inner models using SmartPLS 4. Findings: This study discovered that customer experience positively has a direct and indirect impact on brand loyalty with the mediating effect of brand credibility and brand trust. Originality: The existing literature supports the direct influence of customer experience on brand loyalty in various industries. However, no other study has investigated the mediating role of brand credibility and brand trust on the relationship between customer experience and brand loyalty. This comprehensive study filled the gap between customer experience and brand loyalty in travel agent services. Practical/Policy implication: This study offered managerial implication. By considering customer experience a competitive advantage, managers can actively evolve several experiential marketing strategies to cultivate brand credibility and trust to impact brand loyalty.

Ruslana I. Zhovnovach, Valentina A. Pavlova, Kostiantyn S. Zhadko et al.

The article is devoted to solving the problem of improving the efficiency of the adaptive management system of an agricultural enterprise on the basis of controlling. The necessity of introducing a management system aimed at ensuring a phased management of enterprise processes, taking into account the specifics of functioning with a high level of efficiency, flexibility and efficiency, has been substantiated. A retrospective analysis of the formation and development of the control system in industrialized countries has bee carried out. The results of the analysis made it possible to highlight the main concepts of controlling in accordance with their orientation. The peculiarities of the organization of the controlling system at agricultural enterprises of Ukraine in the conditions of seasonal market fluctuations have beenare determined. Growing crops, unlike the production of products in other industries, has such a feature as seasonality. The seasonal nature of production of agricultural enterprises determines the parameters of the activities of industries that produce and maintain agricultural products, harvest, preserve, process and sell agricultural products. Seasonal fluctuations have a direct impact on the intensity of the use of financial, material and technical, labor, energy and other types of resources of agricultural enterprises in certain periods of time during the calendar year. Thus, they impede the effective planning of the financial and economic activities of agricultural enterprises. This requires the improvement of the management system of an agricultural enterprise based on controlling to balance financial flows between all links of the agroindustrial complex. A mathematical model describing time parameters has been presented. Within the framework of the presented model, controlling actions aimed at ensuring the basic conditions for the functioning of an agricultural enterprise and preventing the phenomenon of shortage of funds during the “low” market period have been proposed. The basis for the construction of the model is the structure and objective proportions that determine the ratio between monetary funds and flows of funds of the enterprise of certain periods of its production and sales activities in the short term. The model allows timely detection of problems and making appropriate corrections in management decisions in order to minimize the destabilizing influence of environmental factors and eliminate unwanted deviations. Controlling actions are formed in the form of reports for the purpose of further use in the process of implementing the developed business processes.

Israa Muwafaq Noori, Khalil Ismail Aziz

يهدف البحث بشكل أساسي إلى تحليل اتجاهات اجمالي الائتمان المصرفي والناتج المحلي الاجمالي وتحري اتجاه العلاقة بينهما بالاستناد الى البيانات السنوية للمدة 2004-2021، دراسة اجمالي الائتمان المصرفي وتحليل اتجاهاته ومعرفة مدى تأثيره في المتغيرات الاقتصادية لاسيما النمو الاقتصادي، يعد من أحدث الموضوعات المتجددة ومن بين أهمها والتي تحتاج للبحث والتقييم الدائم، وما يؤكد ذلك هو الخطط الاقتصادية والاصلاحات الدائمة التي تتبناها البلدان وذلك لضمان نجاعة اجمالي الائتمان المصرفي المقدم وتحقيق الآثار الايجابية له في المتغيرات الاقتصادية الكلية بشكل عام والناتج المحلي الاجمالي بشكل خاص، كل ما سبق يبرر أهمية وضرورة البحث في مثل هكذا موضوعات.           تتمحور إشكالية هذا البحث في السؤال الرئيس الآتي: ما مدى تأثير الائتمان المصرفي في النمو الاقتصادي في العراق للمدة 2004-2021؟، للإجابة عن هذه الاشكالية فقد اعتمد البحث على المنهج الوصفي التحليلي في وصف والادبيات النظرية وتحليل اتجاهات كل متغيرات البحث والعلاقات فيما بينها خلال المدة وصولا لنتائج يمكن من خلالها الخروج بمقترحات وتوصيات مهمة.           توصل البحث إلى نتائج عدة أهمها العلاقة الطردية بين اجمالي الائتمان المصرفي الممنوح والناتج المحلي الاجمالي خلال مدة البحث، فضلا عن تزايد الناتج المحلي الاجمالي خلال مدة البحث بغض النظر عن الظروف الاقتصادية وبنية الناتج وحاجة الأسواق المحلية بسبب ارتباطه الكبير بتصدير النفط الخام، ومن أهم المقترحات التي قدمها البحث هو تهذيب قرارات منح الائتمان للتقليل من نسبة القروض الاستهلاكية غير المنتجة وزيادة القروض الممنوحة للقروض الصناعية المنتجة، فضلا عن ضرورة العمل على فك ارتباط الناتج المحلي الاجمالي بتصدير النفط الخام وريعية الاقتصاد العراقي من خلال تنويع عمليات الانتاج المحلي وتنشيط فروع الاقتصاد، والقيام بتوجيه تمويل مصرفي كافي لإحياء الصناعة والزراعة المحلية.