Hasil untuk "Revenue. Taxation. Internal revenue"

Zander Kotze, Jules Clement Mba

This study addresses the challenge of improving investment portfolio performance in emerging financial markets, where high volatility and structural instability often limit the effectiveness of traditional forecasting approaches. The main objective is to enhance stock return prediction and portfolio allocation by applying advanced machine learning techniques to the Johannesburg Stock Exchange, Africa’s largest and most liquid equity market. The Johannesburg Stock Exchange is selected due to its representative role among emerging markets and its exposure to sectoral concentration, market inefficiencies, and macroeconomic shocks. The analysis covers the period from 2004 to 2024, allowing the model to capture multiple market cycles, including periods of stress and recovery. The empirical analysis is based on daily stock price and trading volume data for nineteen highly liquid firms, complemented by firm-level financial indicators obtained from established financial databases. The research employs a recurrent neural network framework designed for sequential data, incorporating both market-based indicators and firm fundamentals, alongside rigorous data preprocessing and rolling-window validation. The findings confirm that the proposed hybrid modeling approach improves return predictability and leads to superior risk-adjusted portfolio performance compared with conventional benchmark strategies. In particular, portfolios constructed using the model exhibit higher cumulative returns, improved risk–return trade-offs, and reduced downside risk during volatile periods. The study demonstrates clear scientific novelty by integrating diverse financial information within a unified predictive framework tailored to emerging markets. The results have practical relevance for portfolio managers, institutional investors, and policymakers seeking data-driven tools for investment decision-making in volatile market environments.

Bhaskar Ray Chaudhury, Jugal Garg, Eklavya Sharma et al.

We study revenue-optimal pricing in data markets with rational, budget-constrained buyers. Such a market offers multiple datasets for sale, and buyers aim to improve the accuracy of their prediction tasks by acquiring data bundles. For each dataset, the market sets a pricing function, which maps the number of records purchased from the dataset to a non-negative price. The market's objective is to set these pricing functions to maximize total revenue, considering that buyers with quasi-linear utilities choose their bundles optimally under budget constraints. We analyze optimal pricing when each dataset's pricing function is only required to be monotone and lower-continuous. Surprisingly, even with this generality, optimal pricing has a highly structured form: it is piecewise linear and convex (PLC) and can be computed efficiently via an LP. Moreover, the total number of kinks across all pricing functions is bounded by the number of buyers. Thus, when datasets far outnumber buyers, most pricing functions are effectively linear. This motivates studying linear pricing, where each record in a dataset is priced uniformly. Although competitive equilibrium gives revenue-optimal linear prices in rivalrous markets with quasi-linear buyers, we show that revenue maximization under linear pricing in data markets is APX-hard. Hence, a striking computational dichotomy emerges: fully general (nonlinear) pricing admits a polynomial-time algorithm, while the simpler linear scheme is APX-hard. Despite the hardness, we design a 2-approximation algorithm when datasets arrive online, and a $(1-1/e)^{-1}$-approximation algorithm for the offline setting. Our framework lays the groundwork for exploring more general pricing schemes, richer utility models, and a deeper understanding of how market structure -- rivalrous versus non-rivalrous -- shapes revenue-optimal pricing.

Rasheed, Parth Desai, Yash Chaurasia et al.

In the context of blockchain, MEV refers to the maximum value that can be extracted from block production through the inclusion, exclusion, or reordering of transactions. Searchers often participate in order flow auctions (OFAs) to obtain exclusive rights to private transactions, available through entities called matchmakers, also known as order flow providers (OFPs). Most often, redistributing the revenue generated through such auctions among transaction creators is desirable. In this work, we formally introduce the matchmaking problem in MEV, its desirable properties, and associated challenges. Using cooperative game theory, we formalize the notion of fair revenue redistribution in matchmaking and present its potential possibilities and impossibilities. Precisely, we define a characteristic form game, referred to as RST-Game, for the transaction creators. We propose to redistribute the revenue using the Shapley value of RST-Game. We show that the corresponding problem could be SUBEXP (i.e. $2^{o(n)}$, where $n$ is the number of transactions); therefore, approximating the Shapley value is necessary. Further, we propose a randomized algorithm for computing the Shapley value in RST-Game and empirically verify its efficacy.

Gustavo Bergantiños, Leticia Lorenzo

The International Air Transport Association (IATA) states that the revenue from interline tickets must be shared among the different airlines according to a weighted system. We analyze this problem following an axiomatic approach, and our theoretical results support IATA's procedure. Our first result justifies the use of a weighted system, but it does not specify which weights should be applied. Assuming that the weights are fixed, we provide several results that further support the use of IATA's mechanism. Finally, we provide results for the case in which all flights can be considered equivalent and no weighting is required.

Larysa Hrytsenko, Olena Pakhnenko, Aleksandra Kuzior et al.

The article is aimed at the current issues of using smart technologies and innovative approach during evolution and transformation processes in banking. The study identifies the special place of this topic for achieving a high level of efficiency and competitiveness of banks and characterizes the impact of the introduction of technological approaches on the customer base and its perception of banking products. The main functions of banking innovations in this area are analyzed and the justification of their feasibility at the present stage of economic development is provided. A number of the most promising technologies and approaches to banking activities are allocated, namely: contactless payment, digital wallets, biometric identification, person-to-person payments, collective financing, omnichannel banking, interaction with FinTech companies, blockchain, big data, artificial intelligence, smart machines, Internet of Things, behavioral banking, retail bank, application programming interfaces, multi-component bank, open banking, augmented reality, robotic automation, hybrid clouds. The relevance of the identified areas is proved based on their perception by analyzing the popularity of the identified topics in Google search queries using the Google Trends tool. The perception of smart technologies in banking by Internet users in the world and specifically in Ukraine is investigated, which gave grounds to conclude that there is a significant interest in them, and therefore the expediency of further study and implementation in the activities of banks. It is identified that the most perspective technologies are biometric identification, blockchain, Internet of Things, big data analysis, artificial intelligence, etc. Several technologies have been identified, namely, collective financing (crowdfunding), application programming interfaces (APIs) and digital wallets, which are less popular in Ukraine than in the world in general, and therefore require detailed research and study of the relevance of their application in the domestic banking market. Possible directions for further innovative development of banking institutions based on the use of smart technologies are proposed. Based on panel data for 60 banks of Ukraine for the period 2014-2022, the author analyzes the correlations between the indicators of the use of digital technologies and the financial performance of banks and builds regression dependencies of financial indicators of banks on the indicator of the number of electronic means of payment in active circulation. The theoretical value of the study is to identify the most promising smart technologies and innovative approaches to banking business in modern conditions. The practical value lies in studying the level of perception of high-tech innovations in the field of banking services by the active public and identifying further directions for the development of this process. We consider it advisable to direct further research in the context of a detailed study of the possibilities of applying the identified technologies in specific banking products or business processes.

Mahlous Zakia, Baggat Hanene, Aouini Samir et al.

Given the pivotal role of the Association of Southeast Asian Nations (ASEAN) in global trade and the region’s increasing integration within international supply chains, this study investigates the impact of logistics performance on export market penetration among ASEAN countries. Using the Generalized Method of Moments (GMM) one-step estimator, the study analyzes dynamic panel data comprising annual observations on logistics performance indicators, export market penetration metrics, and macroeconomic variables such as inflation rates and intertrade volumes from 2000 to 2022. The results reveal that logistics infrastructure, including international sea container throughput and international sea cargo throughput, significantly enhances the ability of ASEAN countries to access export markets. Findings in improving the liner shipping connectivity index can increase export capacity by up to 2.82%, underscoring the importance of investing in robust maritime infrastructure. Additionally, the study demonstrates that inflation negatively affects export market penetration, emphasizing the necessity for effective inflation control to enhance export competitiveness. The study concludes with recommendations for strengthening logistics infrastructure and fostering regional trade integration in ASEAN to boost export performance in global markets. These insights offer valuable guidance for policymakers and industry stakeholders in formulating strategies to improve trade logistics and elevate export outcomes across the region.

Achintya Ray

The Supplemental Nutrition Assistance Program (SNAP, also known as food stamps) offers tens of millions of American beneficiaries a crucial lifeline. This welfare benefit has been associated with better nutritional outcomes and has been linked to an efficient tool to fight hunger, improve labour market outcomes for the beneficiaries, achieve higher birth weight for children born to the beneficiary mothers, improve height and health outcomes for the beneficiaries. SNAP has been found to be an essential tool to ensure the availability of vital resources during times of need, etc. Despite the program's numerous advantages, serious doubts exist about its viability and stability of the program especially, given the steep rise in program costs over the years. Over the past 50 years, SNAP has experienced tremendous growth both in terms of the number of beneficiaries and amount of spending. From over $1.82 billion in 1969 to over $113 billion in 2022, the total cost of the SNAP initiative has increased over 62 times in 53 years. This research uses data from the US Department of Agriculture to investigate the time series properties in the rise in the total expenditure devoted to the SNAP between 1969 and 2022. With and without trends, Augmented Dickey-Fuller tests are run with carefully chosen lag lengths. The existence of a unit root cannot be rejected in all specifications pointing to the possibility that the program might have grown in an unstable manner over time. An overwhelming amount of evidence points to an unstable and unstable growth in the overall amount spent on SNAP recipients. This unchecked growth may present substantial difficulties for policymakers especially since the program competes with other welfare programs in an environment of rapidly rising national debt and persistent budget deficits. The report does not attempt to estimate program fraud or abuse which may partially contribute to higher expenditures.

Narayan Prasad Aryal, Gobind Kumar Singh

This study examines the determinants of credit risk in Nepalese commercial banks, emphasizing macroeconomic and bank-specific factors. The study utilizes a random effects regression model to investigate the impact of various factors on non-performing loans using panel data from 10 commercial banks in Nepal from 2013–2022. The study's theoretical framework draws on established economic theories, including Kalecki's business cycle theory and Diamond & Dybvig's banking theory. It aims to contextualize the relationship between credit risk and various influencing factors. The theory sets the stage for analyzing credit risk determinants in Nepalese banks. The findings demonstrate that non-performing loans are significantly and positively associated with bank size and return on assets, whereas asset quality and bank age have a negative and significant impact. The capital adequacy ratio exhibits a positive but insignificant impact. Among macroeconomic variables, the inflation rate has a positive and significant impact on non-performing loan, whereas real gross domestic product growth reveals a positive but insignificant relationship. These findings are of utmost importance for bank managers and policymakers in Nepal, as they provide valuable insights to enhance credit risk management practices and maintain financial stability in the banking sector.

Arjun Kumar Dahal, Ganesh Bhattarai, Prem Bahadur Budhathoki

This study examines the impact of net interest margin, non-interest income, non-performing loans, and Political Stability Index on the banks’ profitability regarding return on assets in developing Asian countries, focusing on how political stability affects the bank’s profitability. The secondary data from 14 developing countries used in this study are collected from the World Bank and World Financial Studies Report. The dataset includes 154 data points spanning from 2012 to 2022, focusing on 14 underdeveloped countries of Asia. Exploratory and analytical research designs are utilized. Data are analyzed by using EViews 12. Econometric tools such as descriptive statistics, correlation analysis, panel unit root testing, Fisher-Johnsen combined co-integration test, panel vector error correction model, and Wald test investigate the relationship between response and predictor variables. The net interest margin, non-interest income, non-performing loan, and Political Stability Index are jointly integrated to determine the bank’s profit of developing countries of the Asia continent. Interest margin, non-interest revenue, non-performing loans, and the Political Stability Index all show a 38.6 percent variance in bank profit. It has been discovered that for every unit that rises in net interest margin and Political Stability Index, the bank profitability of developing Asian countries increases by 0.4867 and 0.2221 percent, respectively. Non-interest income has little bearing on the profitability of banks. Meanwhile, non-interest income exhibits minimal influence, suggesting a need for a strategic focus on interest margin enhancement and fostering political stability to optimize banking sector profitability in the region.

Huiling Meng, Ningyuan Chen, Xuefeng Gao

Intensity control is a type of continuous-time dynamic optimization problems with many important applications in Operations Research including queueing and revenue management. In this study, we adapt the reinforcement learning framework to intensity control using choice-based network revenue management as a case study, which is a classical problem in revenue management that features a large state space, a large action space and a continuous time horizon. We show that by utilizing the inherent discretization of the sample paths created by the jump points, a unique and defining feature of intensity control, one does not need to discretize the time horizon in advance, which was believed to be necessary because most reinforcement learning algorithms are designed for discrete-time problems. As a result, the computation can be facilitated and the discretization error is significantly reduced. We lay the theoretical foundation for the Monte Carlo and temporal difference learning algorithms for policy evaluation and develop policy-gradient-based actor-critic algorithms for intensity control. Via a comprehensive numerical study, we demonstrate the benefit of our approach versus other state-of-the-art benchmarks.

Giovanni Occhiali, Jalia Kangave, H. Khan

This paper gives preliminary insights into the challenges surrounding the taxation of high net worth individuals (HNWIs) in Nigeria – first in general terms, and then with a specific focus on Borno State. The need to diversify revenue sources has become increasingly apparent against the backdrop of Nigeria's historical reliance on the export of crude oil, and is the reason why President Tinubu created a committee to harmonise the fiscal system. However, the committee has not yet touched upon the taxation of HNWIs. Drawing from key informant interviews from north-eastern Nigeria, and a two-day workshop with officials from State Boards of Internal Revenue Service from various part of the country, we shed light on the complexities of increasing the compliance of HNWIs. The study highlights a series of legal, administrative, and political obstacles faced by State Boards of Internal Revenue Service, which have developed dedicated compliance strategies. Many of these are similar across states that otherwise share few characteristics. The paper ends with some tentative suggestions for future research.

Joana Andrade

Using recently published country-by-country reporting data released by the United States Internal Revenue Service, we assess United States multinationals’ activity in the single market, aiming to contribute with data-based evidence to the ongoing political debate about the potential changes in the European corporate tax system. Our findings show evidence of artificial profit shifting across member States under the current method to allocate profits of multinational enterprises, with the Netherlands, Luxembourg and Ireland appearing to be the countries showing a higher degree of complicity with these activities. Such actions challenge fair international taxation in the European Union, distorting European internal competition and hampering tax revenue collection. Although it may not be (yet) the time for a worldwide unitary taxation approach, the analysis highlights the urgency for the European Union to adopt a formulary apportionment approach, overhauling a century-old set of global tax rules based

Sivanathan Sivaruban

The main purpose of this research is to examine the impact of corporate governance practices on the financial performance of listed companies in Papua New Guinea from 2018 to 2022. The corporate governance practices are established to monitor and evaluate the conduct of the management of the public listed companies. The Corporate governance charter is established in Papua New Guinea as part of the best practice to assist decision-making on the board of directors. The attributes of corporate governance practices of this study are at the board size, board diversity and the frequency of board meetings. The literature review of the study covers the theoretical framework and research attributes. This study has selected a list of 7 companies out of the twelve companies in Papua New Guinea, where the capital market has very limited growth in the country. A quantitative research approach was employed to conduct this study and using the secondary data for this research. The secondary data were collected from the respective listed companies’ websites and those data are error-free since the financial statements of the listed companies were audited by external auditors. A total of 35 annual reports of the listed company were collected for the research and a convenient sample method is employed for the study. The overall findings of this research have established positive relationships between the board size and the board diversity with the return of equity. The negative relationship was established between board size, board diversity with the return of assets and the Tobin-Q respectively. Furthermore, the negative relationships were identified between the frequency of board meetings with return of assets, equity and the Tobin-Q respectively. The study has indicated that the frequency of board meetings had increased substantially during COVID-19 to mitigate contingency risk. The findings of this study can be used by the regulatory body and listed companies to enhance the effectiveness and efficiency of corporate governance practices so that the overall corporate governance practices could be improved in the future.

Hassiba Hadouga

This study aimed to demonstrate how financial technology tools can be used to achieve financial inclusion by shedding light on the reality of financial technology and financial inclusion in Algeria, specifically in the remote areas of Algeria, as financial inclusion represents one of the main areas that economists and governments are trying to focus on to eliminate poverty. To reach the goal of the study, a statistical analysis method was adopted for the various questions asked to 200 participants in the survey during the period 2022–2023. A set of quantitative and qualitative data was used. The study population represents 200 individuals to whom the questionnaire was distributed. A data mining tool was used to analyze the study data, and the survey participants' K algorithm was later used to predict the behavior patterns of people from a similarly contextualized community regarding financial activities. The study concluded that financial technology, through its multiple tools, changes the structure of comprehensive financial services, in addition to the diversity and style of financial services provided to individuals, which has enhanced and increased their availability to a broader social group that did not have access to them. It was also shown that there is a significant impact of financial technology tools on enhancing financial inclusion indicators. It is recommended to adopt effective and modern financial and technological strategies that provide marginalized social groups with reasonable access to financial services and products that meet their needs, including transactions, payments, savings, credit, and insurance. Therefore, obtaining the added value of data and investing it will increase financial inclusion indicators.

Matthew Stuart, Cindy Yu, David A. Hennessy

Crop yields and harvest prices are often considered to be negatively correlated, thus acting as a natural risk management hedge through stabilizing revenues. Storage theory gives reason to believe that the correlation is an increasing function of stocks carried over from previous years. Stock-conditioned second moments have implications for price movements during shortages and for hedging needs, while spatially varying yield-price correlation structures have implications for who benefits from commodity support policies. In this paper, we propose to use semi-parametric quantile regression (SQR) with penalized B-splines to estimate a stock-conditioned joint distribution of yield and price. The proposed method, validated through a comprehensive simulation study, enables sampling from the true joint distribution using SQR. Then it is applied to approximate stock-conditioned correlation and revenue insurance premium for both corn and soybeans in the United States. For both crops, Cornbelt core regions have more negative correlations than do peripheral regions. We find strong evidence that correlation becomes less negative as stocks increase. We also show that conditioning on stocks is important when calculating actuarially fair revenue insurance premiums. In particular, revenue insurance premiums in the Cornbelt core will be biased upward if the model for calculating premiums does not allow correlation to vary with stocks available. The stock-dependent correlation can be viewed as a form of tail dependence that, if unacknowledged, leads to mispricing of revenue insurance products.

Brendan K. Beare, Alexis Akira Toda

This article concerns the optimal choice of flat taxes on labor and capital income, and on consumption, in a tractable economic model in which agents are subject to idiosyncratic investment risk. We identify the tax rates which maximize welfare in stationary equilibrium while preserving tax revenue, finding that an increase in welfare equivalent to a permanent increase in consumption of nearly 7% can be achieved by only taxing capital income and consumption. The Domar-Musgrave effect explains cases where it is optimal to tax capital income. We characterize the dynamic response to the substitution of consumption taxation for labor income taxation.

Mirza Muhammad Naseer, Yongsheng Guo, Xiaoxian Zhu

The existing literature on stock performance has focused on the viability of asset pricing theories, macroeconomic and microeconomic variations, and institutional disparities. Yet, whether any additional factors influence SP (Stock Performance) remains unanswered. To address this question, the study aims to provide fresh insights into industry factors concerning firm stock performance. The study adds to the existing research literature by focusing on these issues in the context of a developing economy. Data from 80 organizations were evaluated using a multiple regression model for 12 years to study the problem. The findings back up the importance of sector nature in stock performance. According to the results, company size, munificence, and HHI negatively link with financial performance, but growth, GDP, exchange rate, money supply, and oil prices have a positive link. The findings can help firms and individual investors better understand the factors that influence share prices, allowing them to assess their investment options better. Other financial institutions can provide better advice and products to investors seeking funding to finance share purchases.

Mohamed Wadie Lahouirich, Adil El Amri, Salah Oulfarsi et al.

The concept of ‘Performance’ is one of the most used words, both in the academic and professional spheres, due to its importance in all fields. In addition to its very high frequency of use, its definition is polysemous. This paper aims to focus on the surrounding of the performance, by listing several definitions and tracing its evolution over time. This paper also proposes the treatment of performance in all its facets, from the financial one to the global and sustainable one, and by highlighting the complementary aspect of the different approaches of treatment of this concept. To do this, we were interested in articles and books referenced in the Scopus, Cairn, Electre and Google Scholar databases, and we selected the scientific production between 1960 and 2020, which deals with either the definition or the link between the concepts ‘Performance’, ‘CSR’, ‘CSP’ and ‘Sustainable Development’, to synthesize them in this article following a chronological and logical order. This article is intended as a synthetic guide for any researcher or professional interested in the concept of performance, since it traces its evolution and its ramifications through the highlighting of the complementarity and the relevant use of this concept.

Karim Soussou, Abdelwahed Omri

This study contributes to the academic literature on faith-based mutual funds, by offering a comparative investigation of Islamic vs. conventional funds’ performance sensitivity to changes in a list of seventeen relevant funds’ attributes, all in the context of the Saudi market. The performance measures investigated are the excess return, selectivity and timing. The study took place from 2011 to 2015, with a sample of 200 Active Saudi funds, 137 Islamic and 63 conventional. Findings indicated that fund size, management fees, expense ratio cash and price-earnings ratio were irrelevant to both Islamic and conventional fund performances. In addition, we noticed similarities in both Islamic and conventional funds’ performances sensitivities towards turnover, unsystematic risk, investment target, past performance, age and management tenure. They however react differently towards a change in the price-to-book ratio. On the other hand, fund systematic risk, cashflow-to-book ratio and faith factors are exclusively relevant to Islamic funds, while fund growth and objective only affect conventional fund performance. Finally, selectivity and timing appear to be mutually exclusive, suggesting management specialization. This work appears to be the first comparative analysis of its kind. A larger, multi-regional sample, and a longer study period will provide better insights.

Brahim Bouyacoub

This paper analyses the relationship between Inflation Targeting and economic growth in 20 countries in the Middle East and North Africa (MENA) countries region (Algeria, Saudi Arabia, Palestinian Authority, Bahrain, Djibouti, United Arab Emirates, Egypt, Iraq, Iran, Jordan, Kuwait, Lebanon, Libya, Morocco, Mauritania, Oman, Qatar, Syria, Tunisia, and Yemen), using an Autoregressive Distributed Lag (ARDL) model over the period 2000-2020. An autoregressive distributed lag (ARDL) model is an ordinary least square (OLS) based model which is applicable for both non-stationary time series as well as for times series with mixed order of integration. The results show that Inflation Targeting can have several functions. It is a monetary policy framework based on an appropriate institutional architecture. The adoption of inflation targeting is often subject to a change in laws or administrative arrangements relating to the Central Bank. Inflation targeting might support economic growth by lowering inflation and volatility. However, monetary policy alone cannot drive growth. Inflation targeting might support economic growth by lowering inflation and volatility. Moreover, the results of econometric tests lead to convergent conclusions and argue for the existence of unidirectional causal relationships between economic growth and economic policy indicators.