Financial World Research Articles

Global Derivatives Market with Special Preference to Indian Derivatives Market

The subject matter of the study is related to the progression as well as the pattern of the stock index derivatives market in India in the last seven years. Firstly, it is to observe how much as well as in which way the market is flourished by the investors. The past decade has witnessed the multiple growths in the volume of international trade and business due to the wave of globalization and liberalization all over the world. Today, the financial derivatives have become increasingly popular and most commonly used in the world of finance. This has grown with so phenomenal speed all over the world that now it is called as the derivatives revolution. In India, the emergence and growth of derivatives market is relatively a recent phenomenon. Since its inception in June 2000, derivatives market has exhibited exponential growth both in terms of volume and number of contracts traded. The market turnover has grown from Rs.2365 Cr. in 2000-2001 to Rs. 55606453.39 Cr. in 2014-15. Within a short span of fourteen years, derivatives trading in India has surpassed cash segment in terms of turnover and number of traded contracts. The present study encompasses of global derivatives market with special preference to Indian derivatives market.

Protecting Borrowers and Securing Stability: A Critical View of Debt Recovery Processes in India

India's lending landscape is marked by its central role in economic growth, so it is important to look closely at how debt is recovered. This study goes into detail about the laws that govern collecting debts, which is important for keeping the economy stable. Loans are often backed by collateral or financial assets, so strict supervision is needed to make sure the terms are followed. For example, financial institutions use internal debt recovery teams and legal options like the Debt Recovery Tribunal (DRT) set up by the "Recovery of Debt Due To Banks and Financial Institutions Act, 1993" to lower the risk of default. By looking at the reasons why lenders take these extreme steps, this study sheds light on the levels that require legal action and the complicated steps that are needed. It also looks at things from the point of view of borrowers, pointing out the consequences of not paying and possible ways to ease debt loads. This study shows how lending practices, regulatory frameworks, and debt recovery mechanisms all work together in a complex way by looking at legal safeguards and borrower protections. In the end, it aims to help people understand better how to handle debt in today's financial world.

Socialization of Introduction to Understanding The World of The Financial Profession for Students At State 9 Vocational High School, West Jakarta

Purpose of activity PkM with title Socialization introduction Understanding the World of Professionals in the field finance For students at vocational schools for increase interests, hopes and interests For dream So expert profession field finance . Constitution Number 4 of 2023 concerning Financial Sector Support and Support (PPSK) consists of from Public Accountant (AP), Accountant Practicing (AB), Actuary, Public Appraiser, Tax Consultant , Notary , Legal Consultant , Financial Services Sharia Expert ; and other professions determined by the Ministry, Institution, or supervisory and supervisory authority profession related , necessary introduced my friend interest .Problems : 1). Many Accounting Vocational School students However only few have​ objective become expert Profession Finance like Accountant besides profession finance other. 2). Current PPPK statement Socialization of Professional Services Finance in Bandar Lampung delivered No A little students who have low interest, and yet understand How become a profession reliable finances.​ 3). Guard sustainability need power profession field finance in accordance development. The method used is : 1). Extension methods, 2). Question and answer method, 3 ). Demonstration Method . Devotion public This held especially for student School Intermediate Vocational School (SMK) Negeri 9, West Jakarta. Activity results: With extension method past presentation, Q & A, Demonstration​ past the practitioner explains experience his profession direct to student /I has be a Solution: 1). Produce interest and motivation vocational school students who have objective For become power expert profession finance, 2). Vocational school students who want to working in the professional world Finance understand better​ major studies his lectures, 3). give knowledge in practice about profession finance including required competencies​ owned To use add interest interest take Major Accounting, 4). Participant more know method For become a power expert / profession in the field of Financial Services, 5). Activity This has give contribution input to party government, academics, and parties other.

Stock feature dimensionality reduction for closing price prediction using unsupervised machine learning technique (case study of Nigeria Stock Exchange)

Stock data offers invaluable insights into the world of finance. It encourages investment and savings for both individuals and the nation as a whole. To monitor and predict stock, many stock variables are collected which in turn leads to curse of dimensionality because they occupy much storage space and take more computational time. In order to avoid this, there is a need to reduce the dimensionality of the stock features. Since stock is an unlabeled data with a Gaussian distribution (the features are normally jointly distributed), an unsupervised machine learning technique was applied to discover, establish an association and extract the most important features that have the entire generality of the original dataset for predicting the next day’s closing price. The dataset (daily price list) of Dangote Sugar Refinery Plc was randomly selected from the 27 blue chip companies in Nigeria Stock Exchange. 4 stock features were discovered and extracted from the 9 features in the original dataset.

Penyelesaian Masalah Pergantian Akad Pembiayaan Murabahah Ke Mudharabah Dalam Menangani Nasabah Yang Bermasalah Pada BMT-UGT Sidogiri

Problems in the world of finance are a very complex aspect that requires more thorough research. The aim of this research is to determine the completion of the murabahah contract. The research method in this research uses field research with a qualitative approach. The data collection technique uses observation, interviews and documentation. Field data was analyzed descriptively. The research results show that problematic murabahah financing will be transferred to another contract, if the customer on two calls is not present. Then the murabahah object that is not sold is then used as the object of the mudarabah financing contract.

The Impact of Demographic Factors on Financial Literacy among Farmers: Insights from Rural Tamil Nadu

Financial literacy is becoming more widely acknowledged as an essential skill for navigating the intricacies of the financial world. Governments globally are proactively working to improve financial literacy among their populations, understanding that a lack of proficiency in this domain can result in persistent difficulties that greatly affect a person’s financial health. In our country, the convergence of limited literacy and restricted access to financial services poses a barrier for smallholders in acquiring these essential skills. Given the diverse needs within different demographic groups, tailored financial education becomes imperative. This research, focusing on the specific demographic of “farmers,” serves as valuable insight for the government to customize financial literacy content. The study employs the Chi-square test to analyze the association between literacy levels and demographic variables, offering a comprehensive understanding of the dynamics at play.

FORECASTING THE EURO EXCHANGE RATE USING DEEP LEARNING ALGORITHMS AND MACHINE LEARNING ALGORITHMS

Given that time series forecasts are of great importance in the financial world, the main objective of this study is to forecast Euro prices and examine the contribution of these forecasts to financial decision-making processes. Since the Euro is an important component of international trade and investment, accurate price forecasts are of strategic importance for many financial institutions and investors. In this study, we compare the performance of deep learning algorithms and classical machine learning methods for forecasting Euro prices: support vector machines (SVM), Extreme Gradient Boosting (XGBoost), long short-term memory (LSTM), and gated recurrent units (GRU). These methods represent different algorithms that are widely used in financial forecasting and give successful results. The dataset used in the study was divided into two parts: 80% training and 20% testing, and it is also indicated how each algorithm behaved during the training process and which parameters were chosen. The results are presented by comparing the performance of these algorithms, and it is found that the GRU algorithm provides better accuracy than the others. Therefore, the GRU algorithm was chosen to forecast Euro prices for the next 12 months, and the forecasting process was carried out. The results of this study are expected to provide an important perspective to financial decision-makers by comprehensively comparing the performance of deep learning and traditional approaches in Euro price forecasting. It also includes potential research avenues for future work and suggestions for the development of new methods in this area.

Dynamic relationship between the XRP price and correlation tensor spectra of transaction networks

The emergence of cryptoassets has sparked a paradigm shift in the world of finance and investment, ushering in a new era of digital assets with profound implications for the future of currency and asset management. A recent study showed that during the bubble period around the year, 2018, the price of cryptoasset, XRP has a strong anti correlation with the largest singular values of the correlation tensors obtained from the weekly XRP transaction networks. In this study, we provide a detailed analysis of the method of correlation tensor spectra for XRP transaction networks. We calculate and compare the distribution of the largest singular values of the correlation tensor using the random matrix theory with the largest singular values of the empirical correlation tensor. We investigate the correlation between the XRP price and the largest singular values for a period spanning two years. We also uncover the distinct dependence between the XRP price and the singular values for bubble and non-bubble periods. The significance of the time evolution of singular values is shown by comparison with the evolution of singular values of the reshuffled correlation tensor. Furthermore, we identify a set of driver nodes in the transaction networks that drives the market during the bubble period using singular vectors.

Financial risk profiling of millennials

Purpose This study aims to assess the risk profile of millennial investors residing in the Delhi NCR region. In addition, the relationship between the risk profile and demographic traits of millennial investors was also analyzed. Design/methodology/approach Data was collected using a structured questionnaire segregated into two sections. In the first section, millennials were asked questions on socio-demographic factors, and the second section contained ten Likert-type statements to cover the multidimensionality of financial risk. Factor analysis and one-way ANOVA were used to analyze the primary data collected for this study. Findings The findings indicate that the risk profile of millennials is mainly affected by three factors: risk-taking capacity, risk attitude and risk propensity. Except for educational qualification and occupation, all other demographic features, such as age, gender, marital status, income and family size, seem to significantly influence the factors defining millennials' risk profile. Originality/value Uncertainty is inherent in any financial decision, and an investor’s willingness to deal with these variations determines their investment risk profile. To make sound financial decisions, it is mandatory to understand one’s risk profile. The awareness of millennials' distinctive risk profile will come in handy to financial stakeholders because they account for one-third of India’s population, and their financial decisions will shape the financial world for the decades to come.

Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets

In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between paired assets for formulating trading strategies. However, AI models typically exhibit less precision in regression tasks compared to classification tasks, presenting a challenge in refining the accuracy of pair trading strategies. In pursuit of high-performance labels to elevate the precision of classification models, this study advanced the Triple Barrier Labeling Method for enhanced compatibility with pair trading strategies. This refinement enables the creation of diverse label sets, each tailored to distinct barrier configurations. Focusing on achieving maximal profit or minimizing the Maximum Drawdown (MDD), Genetic Algorithms (GAs) were employed for the optimization of these labels. After optimization, the labels were classified into two distinct types: High Risk and High Profit (HRHP) and Low Risk and Low Profit (LRLP). These labels then serve as the foundation for training machine learning models, which are designed to predict future trading activities in the cryptocurrency market. Our approach, employing cryptocurrency price data from 9 November 2017 to 31 August 2022 for training and 1 September 2022 to 1 December 2023 for testing, demonstrates a substantial improvement over traditional pair trading strategies. In particular, models trained with HRHP signals realized a 51.42% surge in profitability, while those trained with LRLP signals significantly mitigated risk, marked by a 73.24% reduction in the MDD. This innovative method marks a significant advancement in cryptocurrency pair trading strategies, offering traders a powerful and refined tool for optimizing their trading decisions.

Climate change, ESG criteria and recent regulation: challenges and opportunities

The application of environmental, social and governance (ESG) criteria has now become a more than essential requirement in the financial world. Therefore, it is necessary to understand, select and assess the risks of these ESG criteria and evaluate how they can impact a product or investment decision. Thus, the main objective of this article is to analyze ESG (Environmental, Social and Governance) indicators and their potential impacts in the framework of non-financial information. Current regulatory developments, such as the European Corporate Sustainability Reporting Directive (CSRD), are pushing to make ESG indicators (within this triple perspective: social, environmental and governance risks) a key set of information to be used for reporters and users of information. This article will study in further detail the main implications these regulations will have in how corporations will reflect social and ecological footprint information in their external reporting. Since these ESG indicators could have relevant financial impacts on the financial drivers of a corporation, stakeholders will be concerned on how enterprises are dealing with these ESG risks. Therefore, this ESG data will increase transparency and would mean a better understanding on how companies and investors have a sustainability compromise to evolve to a neutral carbon economy. In order to understand a company’s commitment with these ESG criteria, stakeholders would have to assess different aspects of the information reported. In this sense, this article will focus on how credit rating agencies incorporate these risks in their assessments. Credit rating agencies are becoming important actors in the sustainability criteria, as they incorporate ESG risks in their assessments, transmitting the importance of these indicators to investors and to markets. This study will look into the different time horizons between financial profitability and sustainability indicators. Current tendency and huge demand of non-financial indicators do not have the same profoundness, framework and tradition as financial indicators. This could lead to a situation in which it would be necessary a period to adapt both worlds and make them join and connect together in a sense in which one need the other one.

Peningkatan Literasi Keuangan: Pendidikan Perencanaan Keuangan Personal di Kalangan Komunitas Guru SMK “Musyawarah Guru Mata Pelajaran (MGMP)” Kabupaten Malang

Financial literacy has been proven to have a positive effect on increasing economic welfare. Apart from that, financial literacy is also needed to mitigate the risk of crime or fraud which often occurs in the world of financial investment. Considering the importance of financial literacy, service activities through the personal financial planning education program are held to create people who are aware of the importance of financial planning. The activity was held for the community of vocational school teachers in Malang Regency (MGMP Malang). The results of the activity showed good enthusiasm from the participants. Participants felt they had gained new knowledge in the world of finance and planning. Changes in behavior and views about finances were also visible from what participants said after the training program

Weighted Signed Networks Reveal Interactions between US Foreign Exchange Rates.

Correlations between exchange rates are valuable for illuminating the dynamics of international trade and the financial dynamics of countries. This paper explores the changing interactions of the US foreign exchange market based on detrended cross-correlation analysis. First, we propose an objective way to choose a time scale parameter appropriate for comparing different samples by maximizing the summed magnitude of all DCCA coefficients. We then build weighted signed networks under this optimized time scale, which can clearly display the complex relationships between different exchange rates. Our study shows negative cross-correlations have become pyramidally rare in the past three decades. Both the number and strength of positive cross-correlations have grown, paralleling the increase in global interconnectivity. The balanced strong triads are identified subsequently after the network centrality analysis. Generally, while the strong development links revealed by foreign exchange have begun to spread to Asia since 2010, Europe is still the center of world finance, with the euro and Danish krone consistently maintaining the closest balanced development relationship. Finally, we propose a fluctuation propagation algorithm to investigate the propagation pattern of fluctuations in the inferred exchange rate networks. The results show that, over time, fluctuation propagation patterns have become simpler and more predictable.

Stock Market Volatility Prediction Based on Robust GBM-GRU Model

In this study, a corrective learning technique is proposed to enhance the time series forecasting accuracy of traditional intelligent algorithms and address issues with engineering data adaptability. The financial world makes substantial use of volatility, and forecasting stock market volatility has great significance. Because stock price time series are nonstationary and nonlinear, predicting stock market volatility is a significant difficulty. In this study, a simulation experiment using an approach for corrective learning is carried out. Over a 3-year period, a LightGBM-GRU model is created to predict the volatility of 126 equities from diverse industries over the length of the subsequent 10 min, utilizing LightGBM and GRU as base models and correctors, respectively. Even traditional and effective ensemble learning models can benefit from the corrective learning technique, which simultaneously improves accuracy and generalization, according to performance metrics like RMSPE, MAE, MSE, and RMSE. The research emphasizes how the contradiction within intelligent algorithms has changed from one of restricted generality to different engineering difficulties in the era of algorithm enrichment and large data. The corrective learning strategy offers a fresh method for simulating data.

Akad Qardh dan Wakalah Bil Ujrah dalam Transaksi Financial Technology Syari’ah Peer to Peer Lending: Perspektif Fatwa DSN-MUI Nomor 117/DSN-MUI/II/2018

In order to prepare the technological infrastructure of developing countries like Indonesia. Information and transactions that can be accessed directly by the general public requires effectiveness and efficiency in feature items, especially technology development. Financial Technology (Fintech) has gone viral because it is one of the best technology options in the financial sector. The conveniences that are obtained by the actors or consumers are aimed at creating independent satisfaction but it is necessary to have a valid contract that will be used and this concerns prospects which have very large implications for the world of financing. Besides these changes, there are various kinds of problems that have arisen, especially the concept of implementing qardh contracts in peer to peer lending syari'ah, applying wakalah bil ujrah contracts in peer to peer lending syari'ah and applying qardh contracts and wakalah bil ujrah in peer to peer lending. syari'ah in the perspective of the DSN-MUI fatwa number 117/DSNMUI/II/2018 concerning Information Technology-Based Financing Services Based on Sharia Principles. So that the objectives achieved can analyze the concept of the validity of the application of the contract. As well as the final analysis of the DSN-MUI fatwa on sharia peer to peer lending.. The type of research used by researchers is qualitative to obtain data using library research (library research). The nature of the research is descriptive. DSN-MUI fatwa number 117/DSNMUI/II/2018 concerning Information Technology-Based Financing Services Based on Sharia Principles which forms the basis is correct, but requires specific fatwas so that the certainty of the application of contracts to service products is more certain and there is no usury and gharar in invoice financing productive and consumer This regulation deserves to be studied, understood and revised by the Government, in this case the Financial Services Authority, DSN-MUI, in issuing regulations and fatwas that accommodate the legitimacy of how fintech products work. This requirement is a form of responsibility of the two regulators so that the risk of occurrence which is prohibited by Syar'I cannot be realized (safe to use). Product contract clauses must be spelled out directly when making a financing contract. So as to be able to support sharia by investing legally, safely, lawfully and can help those in need, especially in funding needs. For this reason, the DSN-MUI fatwa No. 117/DSNMUI/II/2018 must explain in detail the design and achievements determined through collective ijtihad (ijtihad jama'i) so that a National Sharia Council - Indonesian Ulema Council rule is issued which is competent and can fulfill simple elements. , clear, and can be used and understood by startups.

Quantum Uncertainty and the Black-Scholes Formula

The publication of the Black-Scholes formula in 1973 appeared for the first time to put the pricing of financial options onto a rational and objective basis. Its adoption transformed the perception of option pricing from a form of gambling to scientific risk management, and led to a huge increase in options trading. However while the formula revolutionised the world of finance, and remains the industry-standard pricing model today, its proof relies on a number of assumptions about price behaviour which are often contested, such as that log prices follow a random walk with constant volatility, and that one can constantly buy or sell stocks and options without incurring transaction fees. This paper presents an alternative approach to option pricing, based on a quantum oscillator model of stock prices. In the quantum model, the bid/ask spread between buy and sell prices is treated as a fundamental measure of uncertainty, and volatility is not constant but exhibits a smile-like dependence on strike. We show how the Black-Scholes model and its assumptions lead to a systematic mispricing of commonly-traded options, while results can be improved by adopting the quantum model.

The Implementation of Investment Risk Management in Sharia Capital Market

The Sharia Capital Market has recently increased as an investment vehicle that complies with Islamic sharia principles. However, as with all investment forms, some risks must be managed carefully. This paper describes the context, urgency and process of investment risk management in the Sharia Capital Market. The Sharia Capital Market complies with sharia principles, including the prohibition of usury and haram business. The risks investors face include business, market, sharia, and liquidity risks. Risk management supports sharia compliance, protects investments and maintains portfolio sustainability. The risk management involves identifying, assessing, developing strategies, implementing, and monitoring risks. By understanding and implementing risk management well, investors can achieve their investment goals more successfully in the Sharia Capital Market. In the financial world, investors must pay attention to investment risks before deciding to avoid losses. The meaning of investment risk also needs to be studied and understood by investors so that investors do not feel cheated and believe in 'profits and losses' when choosing the type of business to invest in and then be able to learn from the business they are running. We will discuss the technical aspects of investment risk management below later. Apart from understanding investment risks, investment stages can also be known from the financial institution investors choose. For this reason, ask about the stages of investing before you disburse funds. One type of investment that is interesting for investors is peer-to-peer. Unlike traditional financing methods, Peer-to-peer (P2P) lending uses lending marketplace and scoring technology. This means that investment risk becomes measurable and suitable for investors. The funds that have been collected will be distributed to micro entrepreneurs and SMEs who need financing with investors who want to fund these businesses. Investors who have invested their funds using a peer-to-peer lending system will not need to worry about investment risks because later investors will get appropriate returns based on consideration of the investment risk profile.

Gender Differences as a Specific Factor in the Origin of Family Conflicts

This article reveals the essence of the theory based on gender differences, the history of its development, the essence of the research conducted in this direction abroad and in our country. In addition, the young generation's ideas about the financial world, social life, points of view, understandings, worldviews, and the specific mechanisms of oriental etiquette are widely covered. At the same time, the article also talks about the unique socio-psychological features of interpersonal relations in eastern families.

RECENT ADVANCES AND APPLICATIONS OF DEEP LEARNING (DL) IN THE ACCOUNTING PROFESSION

In recent years, the accounting profession has undergone a transformative revolution with the help of rapid advancement in Deep Learning (DL) technologies. Deep Learning, a subset of Artificial Intelligence (AI) has emerged as a powerful tool in the world of finance and accounting, offering unprecedented opportunities for automating tasks, improving accuracy and uncovering valuable insights. This groundbreaking technology has found a multitude of applications in accounting, ranging from automating repetitive data entry tasks to enhancing fraud detection, financial forecasting and risk management. Deep Learning driven by Artificial Intelligence Neural Networks (AI-NN), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and more is reshaping the landscape of financial analysis, auditing, prevention of frauds, risk management and much more. Through these advancements, we gain not only efficiency but also a new perspective on the role of accountants in an increasingly data-driven and digitally interconnected world.

Modern finance through quantum computing-A systematic literature review.

Human intellectual restlessness originates from the need for knowledge of the modern world. The financial world is struggling to prototype accurate and fast data at low risk. The quantum approach to finance can support this desire. The goal of this paper is to provide a comprehensive review of the literature on how quantum computing can be used in finance. This research aims to expose an architecture of the state of the art in quantum finance. In terms of methodology, the PSALSAR framework was used to conduct this systematic literature review. The selection procedure followed the PRISMA guidelines and was applied in two databases (Web of Science and Scopus) without time limit. In total, 94 out of 1646 articles were included for data extraction and assessment of content evaluation covering the period 2001-2023. The current review of quantum finance literature is structured around the following themes: journals, research methods, tested data series, research topics in quantum finance, and future research directions. Within the financial sector, quantum computing is used in three main areas: simulation, optimization, and machine learning. These areas are supported by algorithms that have been created in recent years. Finally, we propose to highlight the benefits and the applications of quantum finance and to stimulate the interest in the future prospects of the debates.