Modern Finance Research Articles

The Future of Block Chain Technology in Modern Financial Policy: Exploring Its Applicability in Iraq

The Future of Block Chain Technology in Modern Financial Policy: Exploring Its Applicability in Iraq

Credit Scoring Model Weighted by Investment and Financing Decisions: Case Study in a Building Supply Store

Current literature provides various models for credit risk analysis, called Credit Scoring Models (CSMs). However, these models are not suited to the majority of Micro and Small Enterprises (MSEs). This is compounded by a lack of technical knowledge of microentrepreneurs linked to the high costs and complexity of the CSMs. These issues are significant as 99% of Brazilian companies are MSEs. Therefore, this paper aims to propose a CSM for an MSE that commercializes construction materials in São Paulo, Brazil. This research is quantitative and characterized as a case study whose CSM is based on the Naive Bayes algorithm implemented in Microsoft Office Excel 2016. This model calculates the probability of default and adherence by weighting the results based on the Modern Finance Theory with the Cost of Denying and the Cost of Granting. The application of the model demonstrates that the successes in approvals (70%) and disapprovals (66%) of Credit Sales were significant with a result of R$ 32.20 thousand and an increase in net profit by 124.2%. We have evidenced that the proposed CSM is able to weigh the risk in investment and financing decisions for the Credit Sales of an MSE. This paper provides a low-cost CSM, adapted to reality and easy to handle and implement in MSEs. This research is a reference to the development of CSMs focused on the credit concessions conducted by MSEs.

Artificial Intelligence in Finance: Applications and Implications

The definition and uses of Artificial Intelligence, often abbreviated as AI, has come a long way since its inception. This technology is rapidly changing some business operations in the areas of trading, identification of frauds, and customer service in particular, the modern finance sector, and this article highlights the importance of technology in these processes. Significant Applications of Artificial Intelligence in Finance: From Algorithmic Trading to Robo-Advisors. Explaining, the paper analyzes the current trends and practices in the industries, addressing the positive and negative aspects of AI in the finance sector, with respect to the visions of the future. One that relates to the socio-technical implications that include the use of such technology in finance and the associated risks of data breach, availability, and loss of control and trust in processes.

Unveiling the Concealed Risks: The Influence of Digital Finance on Cyber Financial Crime in China

Digital finance has emerged as a pivotal catalyst for economic growth in China, profoundly altering social structures and behaviors. Despite its benefits, it has also engendered financial risks, thereby complicating social governance. This study utilizes a panel dataset from China’s provinces spanning 2011 to 2021 to analyze the influence of digital finance development on cyber financial crime rates. The results reveal a positive correlation: an increment of one unit in the digital finance index is associated with a 0.058 unit increase in cyber financial crime rates. These findings are robustly supported by tests for endogeneity and robustness. Variations in impact are observed across urbanization levels and average wage brackets. And the study identifies income distribution and traditional financial demand as the mechanisms of the effect. Furthermore, this study presents strategic recommendations to enhance financial inclusion, facilitate income redistribution, strengthen cyber crime governance, and integrate digital and traditional finance for the advancement of financial modernization.

MANAJEMEN KEUANGAN KELUARGA PEDAGANG DI PASAR SIBANG GEDE

The income of traders depends on uncertain sales income every day. To be able to manage family finances, management, and planning are needed to ensure that expenses are not greater than income. This study aims to examine the understanding of financial management, the application of financial management, and the obstacles to financial management for traders in the Sibang Gede market. This study's data and information collection methods included interviews using questionnaires and documentation. The analysis method used is qualitative descriptive analysis. The results of this study indicate that traders in the Sibang Gede market do not understand financial management. Traders choose to record income daily, do not apply long-term planning, apply short-term planning, and own assets in the form of houses and motorbikes. Traders in the Sibang Gede market do not make financial plans, do not record family finances in books, and do not compile budgets. Traders do not have investments, and all traders in the Sibang Gede market choose to use banks as a place to save. The obstacles Sibang Gede market traders face are income instability caused by fluctuations in consumer demand, traders not being accustomed to making financial reports, and traders not being familiar with modern finance.

An adaptive financial trading strategy based on proximal policy optimization and financial signal representation

Trading strategies play a crucial role in financial trading. However, due to the significant amount of noise present in financial signals, traditional trading strategies and those based on price prediction often fail to achieve optimal results in real market conditions. Nowadays, with the pervasive noise in financial signals, developing successful trading strategies to achieve high returns has become one of the most prominent and challenging research areas in modern finance. Therefore, an adaptive financial trading strategy, called proximal policy optimization based on financial signal representation trading strategy (FSRPPO), is proposed. This strategy employs financial signal representation technology (FSR), which combines complete ensemble extreme-point symmetric mode decomposition with adaptive noise (CEEMDAN) and modified rescaled range analysis (MRS), to accurately and robustly represent dynamic market states, enhancing profitability. Additionally, the designed reward function reduces trading frequency to lower costs, while the designed action space increases trading flexibility to reduce risks. The experimental results on real stock data demonstrate the outstanding profitability and good risk avoidance ability of our proposed trading strategy, which means that the proposed model can effectively filter out noise from financial signals, extract valuable information, and provide reliable decision support for investors.

Application of Human-Computer Interactive Modern Financial Technology in Agricultural Supply Chain Finance Based on Artificial Intelligence Powered CAD

Application of Human-Computer Interactive Modern Financial Technology in Agricultural Supply Chain Finance Based on Artificial Intelligence Powered CAD

Generational differences in payment habits and solutions in the digital space

With the rise of digitalisation, payment habits and solutions have undergone a significant transformation, which is also driven by generational differences. Our study aims to explore how the payment preferences and habits of each generation differ in the digital space. Older generations tend to prefer more conservative payment methods, such as credit cards or cash, while younger generations are more open to modern, innovative solutions such as mobile payments and cryptocurrencies. Our paper highlights the extent to which technological developments and digital literacy are influencing the payment habits of different generations, and how the role of security and convenience is shaping the payment choices of different age groups. The study focuses on the financial literacy of each generation, including how to navigate 21st century finance. It is no exaggeration to say that not everyone has the same knowledge or understanding of modern finance. However, effective financial decision-making requires not only an understanding of traditional finance, but also the ability to keep abreast of the financial changes of the modern age. This study will hopefully shed light on the importance of the subject and the importance of developing financial literacy. We can strongly assume that financial literacy can be characterised at very different levels when looking at different generations, so it seems worthwhile to look more closely at the level of financial literacy among different generations. However, the present study focuses on financial habits and solutions.

A Binomial Tree-Based Empirical Study on the Biases of American Call Option

The application of financial derivatives, particularly options, has become pervasive in modern finance, offering effective tools for risk management. Among these, American options, allowing flexibility in exercising rights prior to expiration, dominate the derivatives market. Accurate pricing of American options is crucial for informed investment decisions and risk assessments. While various pricing models exist, the binomial tree model is welcomed for its simplicity and accessibility. However, inherent biases in pricing models raise questions about their efficacy across different sectors of the market. Based on the binomial tree model, this study empirically examines the pricing accuracy of American call options in both the technology sector and the industrial sector, by collecting data from top five largest companies in each sector. For the technology sector, samples are mainly from semiconductor manufacturers, software companies, etc., which are all situated in thriving fields; while those five companies from the industrial sector mainly belong to traditional industries, such as energy and engine suppliers. By analyzing prediction biases between two sectors, this research tries to give out reasons behind it and grasps a deeper understanding of option pricing nuances between sectors, and also the limitations of the binomial tree model.

Harnessing RegTech for Financial Inclusion: Enhancing Compliance and Security in the Modern Financial Landscape

Purpose: The integration of Regulatory Technology (RegTech) into the financial inclusion landscape addresses the complex compliance and security challenges faced by financial institutions in an increasingly ambiguous technological environment. Methodology: The study is conducted through a comprehensive analysis of current patterns, advantages, and disadvantages, supported by real-world case studies, Findings: This study reveals how RegTech, powered by machine learning, artificial intelligence, and blockchain, significantly enhances compliance efficiency, reduces operational costs, and safeguards sensitive financial data. Additionally, a strategic framework for implementing RegTech solutions is presented, offering practical recommendations to ensure compliance while fostering a secure and inclusive financial environment. Unique Contribution to Theory, Practice and Policy: The findings contribute new insights into RegTech's role in advancing financial inclusion, provide guidance for financial institutions on effective adoption strategies, and inform policy by emphasizing the need for regulatory support to fully leverage RegTech for broader financial inclusion goals.

Swiping Our Way to Trouble? The Rise of “Credit Card Dependence”

Credit cards, once seen as a tool for emergencies or planned purchases, have become ingrained in our daily lives. The convenience of this plastic has undeniable benefits, but a growing trend of rising dependence on credit cards raises concerns about financial stability and potential economic risks and financial pitfalls. In recent years, the dependence on credit cards has soared to unprecedented levels. This trend, while indicative of certain economic advantages, also brings with it a host of challenges that could have far-reaching implications for individuals and economies alike. As we navigate through the complex landscape of modern finance, it’s crucial to understand the factors driving this dependence, the benefits it offers, and the potential pitfalls it presents.

Innovative approaches to financial analytics education: Kolb's theory in SEC IPO analysis

AbstractThis study presents an innovative educational approach, integrating David A. Kolb's Experiential Learning Theory (ELT) into financial analytics education, with a focus on the analysis of Initial Public Offerings (IPOs) via the SEC's EDGAR database. Through the application of ELT, this article explores the effective melding of experiential learning methodologies with advanced analytical tools such as Python, SQL, and various data analytics platforms. The teaching exercise outlined emphasizes the importance of hands‐on activities—ranging from data scraping and analysis of SEC filings to conducting sentiment analysis—aimed at fostering not only technical proficiency among students but also enhancing their critical thinking and ethical decision‐making skills. By detailing the execution and outcomes of utilizing ELT in the context of financial analytics, this work contributes to the discourse on modern finance education, advocating for a model that prepares students for the challenges of an increasingly complex and ethical finance industry. This approach underscores the need for educational practices that are both theoretically robust and practically relevant, ensuring students are well‐equipped for dynamic roles within the finance sector.

Portfolio Selection Based on Mean-Generalized Variance Analysis: Evidence from the G20 Stock Markets

Modern finance theories have been increasingly paying attention to nonlinear and asymmetric features of stock returns. In this paper, we extend the concept of covariance to generalized covariance by using Generalized Measure of Correlation (GMC). Based on the generalized covariance which is capable of catching the nonlinearity and asymmetry in stock (index) returns, we propose a mean-generalized variance portfolio selection model which considers the gross-exposure constraint. Furthermore, we propose the corresponding nonparametric estimation approach and the global optimization algorithm to enhance the applicability of our new model. Empirical studies on G20 stock markets support that a portfolio considering nonlinear and asymmetric features among the international markets would outperform traditional ones based on mean-variance optimization and equal weighting strategy in terms of return and flexibility.

Evolution of option pricing models: From Black-Scholes to Heston and beyond

This essay explores the evolution of option pricing models, tracing their development from the foundational Black-Scholes model to more advanced frameworks such as the Heston model and beyond. Beginning with an introduction to option pricing theory, the essay discusses the origins of the Black-Scholes model and its assumptions, as well as the challenges and limitations it faces. It then examines the extension of the Black-Scholes model, so-called the Black-Scholes-Merton model. It incorporates dividends, and lays the groundwork for further research into options pricing and financial derivatives. Then various stochastic volatility models emerge, and the essay chooses the Heston model as a typical example for analysis, highlighting its advantages and applications in option pricing. Furthermore, the essay compares the Heston model with other option pricing models, including the SABR model and Bates model. At the end of the essay, recent advances and future directions in option pricing are introduced and discussed. Through this comprehensive exploration, readers can gain a deeper understanding of the evolution of option pricing models and their significance in modern finance.

Unlocking the Future: Exploring the Dynamics and Implications of Cryptocurrency in the Modern Financial Landscape

Abstract: This study examines the complex world of cryptocurrencies, highlighting its dynamic character and significant ramifications for the modern financial system. Key themes including decentralization, security, blockchain technology, and regulatory issues are explored through in-depth examination. This study highlights the revolutionary potential of cryptocurrencies and emphasizes their role in altering existing financial systems and boosting financial inclusion by combining current theory and empirical evidence. The study also looks into how the adoption of cryptocurrencies affects different stakeholders, such as governments, companies, investors, and society at large. This study aims to provide insight into the future direction of cryptocurrencies and their importance in opening up new opportunities for innovation and economic growth through a forward-looking viewpoint. KEY WORDS: Keywords: Cryptocurrency, Decentralization, Security, Regulatory, Challenges, Transformation, Financial Inclusion

The Legacy of M.M. Speransky in Modern Financial Law

The article is devoted to the study of the ideas of the famous statesman and public figure of the XIX century M.M. Speransky. His merits in the development of legal science and legal education in Russia are noted, as well as his achievements in the field of financial law are especially emphasized. The project of financial reforms prepared by M.M. Speransky, called the “Finance Plan”, is analysed. The economic situation that caused the financial transformations is considered, and the measures proposed by the great Russian reformer in the field of budget revenues and budget expenditures are examined in detail. The authors note the significant contribution of his idea to the formation of the principles of the budget system of Russia – the principle of budget balance, the principle of efficiency in the use of budget funds, the principles of budget independence and the delimitation of revenues, expenses and sources of financing budget deficits between the budgets of the budget system of the Russian Federation. The classification of budget expenditures proposed by M.M. Speransky for necessary, useful, excessive and excessive (useless) helps to reduce government costs and is very relevant for modern Russia. However, for its legislative consolidation, a clear system of criteria is required. It is also proposed to implement this idea by establishing the category of “priority expenses”, referring to them the so-called necessary expenses to be implemented in any state of affairs. The article indicates significant attention in the works of M.M. Speransky to the issues of proper order and the correct, transparent organization of a particular activity. It is also emphasized the relevance for the modern economy of Russia of its ideas on the reform of budget revenues, their unification, the consolidation of the principle of certainty of taxation and fees. The authors conclude that it is possible to further reform Russian financial legislation through the development of the ideas of M.M. Speransky.

Aggregate Lending and Modern Financial Intermediation: Why Bank Balance Sheet Models Are Miscalibrated

Aggregate Lending and Modern Financial Intermediation: Why Bank Balance Sheet Models Are Miscalibrated

A review on Gharar dimension in modern Islamic finance transactions

Purpose This study aims to systematically review the literature on modern Islamic finance transactions pertaining to Gharar dimensions. Gharar is defined as uncertainty that potentially leads to ambiguities and conflicts in contracts. Design/methodology/approach The articles reviewed in this study consisted of 13 articles related to Gharar published between 2013 and 2022. All selected articles were empirically and descriptively searched using specific keywords and strings. The main sources for this study were Scopus and Web of Science (WoS), whereas Google Scholar was a supportive database. Findings The review found that the dimensions that discussed previous research were trying their best to elaborating Gharar in modern financial transactions. They also demonstrate that rigorous study and deployment of the definition remain in the context defined by jurisprudence scholars. The focus of recent studies pertaining to Gharar is derivatives products that indicate high possibility of uncertainty in its operation. Research limitations/implications This method relies heavily on the accessed database, namely, Scopus and WoS, also referred to the articles as recommended by the databases. Furthermore, the criteria of inclusion and exclusion of papers outlined by the authors deemed as an intrinsic limitation in writing systematic literature review. Originality/value To the best of the authors’ knowledge, this paper is original in its nature whereby the scholars had different comprehension on how Gharar exists in transaction but they still centred in its original meaning of uncertainty. As a result, this paper also realized how Gharar were interpreted differently relied on the contract’s nature and behaviour. In addition, this paper is expected to contribute to understand how Gharar been interpreted in modern finance transactions and finally reached to the point that further research is needed in establishing Gharar parameter for each contract in Islamic commercial law.

Exploring the dynamic landscape of environmental, social, and governance literature by using bibliometric analysis

The incorporation of Environmental, Social and Governance considerations into investment decisions has experienced substantial acceleration in the dynamic landscape of modern finance. In this study, the author explores the dynamic landscape of ESG literature using bibliometric analysis. In conducting the bibliometric analysis of 764 articles for this study, RStudio& VOSviewer software were employed. The authors started by reviewing the ESG domain from 2013 to 2023. Following a thorough review of concerned literature, this paper pinpointed the most influential authors, journals, documents, and countries. Our data revealed a progressive growth in publications for the field of study. Most of the publications are from the United States. The current research focuses on three main aspects: ESG aspects, performance analysis, and science mapping analysis. Further, word cloud analysis of 723 research articles published in the last four years was conducted, for providing future insights

OPTIMIZATION OF SHOPPING STRATEGY IN CRYPTOCURRENCY MARKETS BASED ON ARTIFICIAL NEURAL NETWORKS

This study is relevant in the context of modern finance, where neural networks play a key role in the analysis and forecasting of price dynamics. The research focuses on identifying buy signals for the BTCUSDT and ETHUSDT trading pairs in the cryptocurrency market. To construct a neural network model that can automate the identification of moments beneficial for purchasing selected assets, various technical analysis indicators and convolutional neural networks (CNN) were used. The research includes an analysis of scientific literature, data collection, indicator selection, signal algorithm development, and the construction of neural network models. The main contribution of this work lies in the development and testing of models capable of predicting buy signals with a high accuracy, confirmed by accuracy indicators of over 92%. The findings of this study can be useful for private investors and financial institutions in forming investment strategies based on machine learning.