Hasil untuk "Revenue. Taxation. Internal revenue"

Yusuff Awosanya

Islamic banks in Africa have operated for over two decades in an effort to expand and compete with conventional financial institutions, particularly in a region with a significant Muslim population. Despite this potential, the growth of Islamic banking across the continent has been shaped by unique structural and economic challenges that influence how these banks manage financial risks. Among these risks, liquidity risk remains a critical factor affecting the stability and profitability of Islamic banks. This study investigates the impact of liquidity risk on the financial performance of Islamic banks in Africa from 2014 to 2019. Using panel data from 34 Islamic banks across 11 African countries, the research applies a fixed effects regression model to analyze the relationship between liquidity risk and key financial performance indicators. The findings reveal that liquidity risk, measured through the liquidity ratio, has a significant positive relationship with the financial performance of Islamic banks. This suggests that maintaining adequate liquidity enhances banks’ profitability and resilience in the face of regional economic volatility. The study concludes that effective liquidity risk management is essential for improving the financial performance of Islamic banks in Africa. It recommends that policymakers and bank managers adopt stronger liquidity monitoring frameworks and regulatory support mechanisms to ensure sustainable growth and competitiveness within the African financial sector.

Janga Bahadur Hamal, Dilli Raj Sharma, Narayan Prasad Aryal et al.

This study assessed the factors influencing bank lending behavior in Nepal. It is primarily focused on the bank-specific variables such as capital adequacy, profitability, bank size, and liquidity. The study in developing economies like Nepal helps to fill the gap in understanding how these factors affect lending practices in the commercial banking sector. This study used a quantitative approach with a panel data regression model spanning ten years (2013-2022). The data were selected from ten commercial banks purposively. This study used an explanatory research design to examine the causal relationship between banks’ lending and its determinant factors. The investigations concluded that capital adequacy has a positive but statistically insignificant effect on bank lending. Conversely, return on assets has a negative and statistically significant association with lending. Likewise, liquidity has a positive and significant relationship with bank lending behaviors. Finally, size showed a strong and significant positive impact on lending. The study concludes that maintaining adequate capital and larger bank sizes are crucial for enhancing lending capabilities in Nepalese banks. Additionally, while profitability is essential for overall financial health, it may not directly correlate with increased lending activities. The study suggests that policymakers and banks prioritize the enhancement of capital requirements and promote larger banks to cultivate competitive lending environments within Nepalese commercial banks.

Prem Bahadur Budhathoki, Shiva Raj Ghimire, Yeak Narayan Shrama

This study investigates how priority sector lending affects non-performing loans in Nepalese Banks. In addition, this current study examines the influence of capital adequacy ratio, credit-to-deposit ratio, interest rates, and net liquidity, on non-performing loans. This study employed secondary data sourced from the Nepal Rastra Bank covering the period from 2021Q2 to 2023Q4. The analysis utilizing ordinary least square, fixed effect, random effect, and fully modified ordinary least square, indicates that non-performing loans is positively and significantly influenced by priority sector lending, with consistent results across all regression models. In a similar manner, interest rate has a favorable influence on non-performing loans and demonstrates robustness across all regression models. Similarly, non-performing loans has been positively influenced by net liquidity and demonstrates strength in ordinary least square, fixed effect, and random effect models; however, while the sign remains consistent in the fully modified ordinary least square model, it lacks statistical significance. Non-performing loan has experienced negative impacts from both capital adequacy ratio and credit-to-deposit ratio. However, they do not produce consistent results across all regression models. This study demonstrates the need for government-backed guarantee funds to increase bank lending for priority sector to support the balanced economic development of this nation.

Shreehari Anand Bodas, Harsha Honnappa, Michel Mandjes et al.

This paper analyzes a service system modeled as a single-server queue, in which the service provider aims to dynamically maximize the expected revenue per unit of time. This is achieved by constructing a stochastic gradient descent algorithm that dynamically adjusts the price. A key feature of our modeling framework is that customers may choose to balk - that is, decide not to join - when facing high congestion. A notable strength of our approach is that the revenue-maximizing algorithm relies solely on information about effective arrivals, meaning that only the behavior of customers who choose not to balk is observable and used in decision-making. This results in an elaborate interplay between the pricing policy and the effective arrival process, yielding a non-standard state dependent queueing process. An important contribution of our work concerns a novel Infinitesimal Perturbation Analysis (IPA) procedure that is able to consistently estimate the stationary effective arrival rate. This is further leveraged to construct an iterative algorithm that converges, under mild regularity conditions, to the optimal price with provable asymptotic guarantees.

Augustin Dachi, Karina Kasztelnik

This study delves into the pivotal role of directors of microfinance banks in developing countries in implementing governance practices to ensure sustainability. As microfinance institutions strive to balance social impact with financial viability, effective governance emerges as a crucial factor for sustainable operations. The research investigates the strategies and frameworks adopted by directors of microfinance institutions to establish robust governance structures that promote long-term sustainability. Through comprehensive case studies and interviews with key stakeholders, the study identifies best practices and common challenges faced by microfinance bank directors in governance implementation. Key areas of focus include risk management, transparency, accountability, and stakeholder engagement. The findings highlight the critical importance of strong governance in mitigating risks, enhancing institutional credibility, and ensuring the alignment of financial and social objectives. Moreover, the research underscores the necessity for tailored governance models that consider the unique socio-economic contexts of developing countries. It reveals that adaptive governance strategies, which incorporate local cultural and regulatory nuances, significantly contribute to the sustainability and resilience of microfinance institutions. By bridging theoretical governance principles with practical implementation, this study provides actionable insights for directors seeking to enhance the sustainability of microfinance institutions. The implications of this research are far-reaching, offering valuable lessons for policymakers, practitioners, and academic scholars. It emphasizes the need for continuous capacity building and the development of governance frameworks that can evolve with the dynamic landscapes of developing economies. Ultimately, the study contributes to the broader discourse on sustainable development by elucidating how effective governance can empower microfinance institutions to drive socio-economic progress in developing countries.

Stacey L. Morin

Shareholder short-term thinking, driven by a focus on immediate financial returns, poses significant risks to sustainable corporate growth and long-term societal well-being. This paper investigates the mechanisms through which short-term profit maximization fosters corporate decision-making that sacrifices long-term value creation, culminating in organizational greed and systemic inefficiencies. Through a comprehensive literature review and analysis, the paper explored the historical roots of short-termism, regulatory frameworks, and potential solutions to mitigate the adverse impacts of this phenomenon. The relevance of this research lies in the critical role shareholder decision-making plays in shaping corporate strategies, societal outcomes, and the sustainability of the global economy. By addressing how short-term shareholder thinking fosters greed and undermines long-term value creation, this paper highlights the systemic risks and ethical implications of prioritizing immediate financial gains over sustainable growth. This article employs a conceptual analysis grounded in agency theory, stakeholder theory, and the myopic market hypothesis to examine how aligning executive incentives, fostering stakeholder engagement, and countering market pressures can address the negative effects of short-termism. The author leveraged the concepts from these theories. The author's contribution to solving the scientific problem under study involves a conceptual framework application for mitigating short-term shareholder focus and concentrating on long-term value. The findings underscore the need for governance reforms and stakeholder-centric approaches, making the research pivotal for fostering resilient and responsible corporate practices that benefit both businesses and society at large.

Mohamed Chohra

The management of socio-economic infrastructure projects in developing countries, including roads, bridges, tunnels, railways, ports, airports, dams and others, presents multifaceted challenges, particularly in environments marked by complexity and uncertainty. Despite the efforts of governments in these countries, these types of projects still face numerous risks, including political, technological, regulatory, financial, and market risks. The objective of the study was to examine the practices related to the problem of the decision-making process and risk assessment in the management of socio-economic infrastructure projects in developing countries, focusing on the example of the Algerian experience. This research used a qualitative analysis of the decision-making process through 15 semi-structured interviews conducted with representatives of various institutions and administrations responsible for supervising the East-West highway project in Algeria and several focus groups conducted or observations noted at the meetings where the author participated as a principal engineer at the Ministry of Public Works (project owner) during the different phases of the implementation of this mega-project. This made it possible to carry out a global assessment of current practices and to identify the major risks that can impact the decision-making process to improve the management of socio-economic infrastructure projects in Algeria and also in developing countries. The results of the study reveal the risks related to the complexity of the external and internal environment of the projects, in which among the 13 major risks identified, 61.54% have a high criticality, 30.77% have a medium criticality, and 7.69% have a low criticality. The results show that several important aspects need to be improved, including: 1) strengthening financial stability; 2) political risk mitigation; 3) implementing economic stability measures; 4) clarifying contractual aspects; 5) promoting cultural sensitivity and communication; 6) improving cost and quality control measures. This study provides valuable guidance to governments, businesses, managers, and other individuals or groups interested in promoting the management of socio-economic infrastructure projects in developing countries.

Aryan Priyadarshi, Pankaj Singh, Padam Dawadi et al.

The proliferation of FinTech apps has democratized access to financial services, empowering individuals and businesses to take greater control of their finances. These apps have catalyzed financial innovation, disrupted traditional business models, and fostered competition in the financial industry. Moreover, FinTech apps have the potential to drive economic growth, promote financial literacy, and advance financial inclusion on a global scale. FinTech applications have played an important role in the creation of an arena where information is collected, stored, and processed to make the best decisions of investment. This has boosted the investment and increased the activity in capital markets when considered from the investors’ point of view. Capital markets have always been an important place for investors to invest in long-term securities with a maturity of more than one year. Primary as well as secondary markets are part of it. People invest in securities expecting a return in the form of capital gains and/or income. The role of FinTech applications in facilitating various investment decisions has also opened doors to retail investors in capital markets. Traditionally, retail investors have been overlooked because they are perceived not to have enough capital to invest, be too risk-averse, and be too costly to service. Both quantitative and qualitative data have been used. 150 online surveys were used to gather primary data. The outcome from this study showed a major shift in stock market investors towards digital connection due to various reasons. Some cultural and behavioral effects have been found among the investors for the financial transactions.

Craig R. Everett

Despite the prevailing focus on the public markets, private companies represent approximately half of the world economy, if not more. This is a descriptive article presenting a summary of data and results from the 2024 Private Capital Markets Report, which is the product of the annual private capital markets survey. The annual survey was launched in 2009 and examines the behavior and trends of senior lenders, asset-based lenders, mezzanine funds, private equity groups, venture capital firms, angel investors, privately-held businesses, investment bankers, business brokers, limited partners, and business appraisers. Most of the survey panelists are based in the United States of America, but this is not a requirement. The data set also includes many panelists outside of the U.S. There was a total of 1,143 respondents represented in the 2024 survey. An empirical result of this article is that we test the theory that the recent return of warrants to mezzanine deals is associated with a higher interest rate environment. We find evidence supporting that association. These results may be helpful for academics and practitioners forecasting mezzanine activity and deal terms.

Padam Bahadur Lama, Prem Bahadur Budhathoki, Rita Subedi et al.

The purpose of the study is to examine the impact of mergers and acquisitions on financial performance in Nepalese financial institutions. This study investigated specific banks in Kathmandu, Nepal. This study, including six banks, analyzed the impact of commercial banks' pre-merger and post-merger policies on financial performance. Thus, the study consisted of 60 observations with five years of data accumulated for the analysis. Earnings per share (EPS), non-performing loan ratio (NPLR), capital adequacy ratio (CAR), credit to deposit ratio (CDR), cash reserve ratio (CRR), and bank size (BS) are major predictor variables employed in the study and return on assets (ROA) as a response variable. The research design employed in the study is descriptive and casual comparative to investigate the relationship and effect of predictors on return on assets. Therefore, the data analysis process is accomplished through descriptive statistics, correlation, and regression analysis. The findings of the study showed a positive effect of earnings per share on return on assets for pre-merger and post-merger. Next, the non-performing loan ratio found affecting negatively with return on assets. Similarly, the capital adequacy ratio and credit-to-deposit ratio positively influenced the return on assets. Further, the cash reserve ratio found an inverse influence on ROA only for post-merger and acquisition. However, bank size was found to adversely influence the return on assets for the pre-post-merger and acquisition. The findings of this study provide a benchmark for professionals, bankers, and policymakers to adopt strategic fit to enhance the financial performance of banks pursuing merger and acquisition strategies. This paper contributes to the existing literature by assessing the status of pre-merger and post-merger concerning financial performance.

Johnson Olugbenga Agbede

This study investigates the impact of tax audits on revenue generation in Lagos State, Nigeria, focusing specifically on the revenue from Pay-As-You-Earn (PAYE) and Direct Assessment (DA) over a 12-year period (2012-2023). Utilizing a longitudinal research design, data was sourced from the Lagos State Internal Revenue Service (LIRS) audit division. The methodology involved descriptive and inferential analysis, including ordinary least squares regression analysis, to explore the relationships between tax audits and revenue outcomes. Results indicate that tax audits positively and significantly influence both PAYE and DA revenues, with coefficients of 0.557 and 0.580, respectively. The study further highlights the significance of time as a variable influencing direct assessments, underscoring the cumulative effect of taxpayer compliance over years. The findings contribute to existing literature by elucidating the role of tax audits in enhancing revenue collection, providing valuable insights for tax authorities and policymakers. Recommendations for periodic tax audits to optimize revenue generation are offered, along with suggestions for future research to broaden the scope of inquiry in the field of taxation

Xiyuan Ren, Joseph Y. J. Chow, Venktesh Pandey et al.

As an IT-enabled multi-passenger mobility service, microtransit can improve accessibility, reduce congestion, and enhance flexibility. However, its heterogeneous impacts across travelers necessitate better tools for microtransit forecasting and revenue management, especially when actual usage data are limited. We propose a nested nonparametric model for joint travel mode and ride pass subscription choice, estimated using marginal subscription data and synthetic populations. The model improves microtransit choice modeling by (1) leveraging citywide synthetic data for greater spatiotemporal granularity, (2) employing an agent-based estimation approach to capture heterogeneous user preferences, and (3) integrating mode choice parameters into subscription choice modeling. We apply our methodology to a case study in Arlington, TX, using synthetic data from Replica Inc. and microtransit data from Via. Our model accurately predicts the number of subscribers in the upper branch and achieves a high McFadden R2 in the lower branch (0.603 for weekday trips and 0.576 for weekend trips), while also retrieving interpretable elasticities and consumer surplus. We further integrate the model into a simulation-based framework for microtransit revenue management. For the ride pass pricing policy, our simulation results show that reducing the price of the weekly pass ($25 -> $18.9) and monthly pass ($80 -> $71.5) would surprisingly increase total revenue by $127 per day. For the subsidy policy, our simulation results show that a 100% fare discount would reduce 61 car trips to AT&T Stadium for a game event, and increase 82 microtransit trips to Medical City Arlington, but require subsidies of $533 per event and $483 per day, respectively.

Mohit Apte, Ketan Kale, Pranav Datar et al.

This paper explores the application of a reinforcement learning (RL) framework using the Q-Learning algorithm to enhance dynamic pricing strategies in the retail sector. Unlike traditional pricing methods, which often rely on static demand models, our RL approach continuously adapts to evolving market dynamics, offering a more flexible and responsive pricing strategy. By creating a simulated retail environment, we demonstrate how RL effectively addresses real-time changes in consumer behavior and market conditions, leading to improved revenue outcomes. Our results illustrate that the RL model not only surpasses traditional methods in terms of revenue generation but also provides insights into the complex interplay of price elasticity and consumer demand. This research underlines the significant potential of applying artificial intelligence in economic decision-making, paving the way for more sophisticated, data-driven pricing models in various commercial domains.

Mohamed Abdalla Mohamed Ahmed

The paper aimed to study the factors affecting the exchange rate in Sudan during the period from 1992-2022, and to what extent these factors are considered determinants of the exchange rate in Sudan. Statistically significant between the gross domestic product, the monetary reserve, the value of exports and imports, and the exchange rate. The paper reached results, the most important of which is that the direct relationship between the volume of exports and the decline in the exchange rate of the Sudanese pound did not lead to an increase in the volume of exports and a decrease in the volume of imports, and thus did not contribute to a decrease in the deficit in the trade balance and the balance of payments. The most important recommendations of the paper are: the need to build and diversify foreign exchange reserves to increase the effectiveness of implementing exchange rate policies, and this can only be done through increasing real gross product, reducing imports and increasing exports.

Mirza Muhammad Naseer, Tanveer Bagh, Kainat Iftikhar

We explore the impact on firm value by numerous factors in the energy industry using panel data from 2010 to 2020. The analysis employs different econometric methods, including fixed-effects, random-effects, two-stage least squares, and generalized method of moments. Our main variables of interest are firm value, firm-level climate change risk, fixed assets, leverage, dividend yield, market capitalization, and assets tangibility. The result suggests that investors are valuing energy firms less due to their exposure to climate change risk. We found that climate change risk, fixed assets, firm leverage, and assets tangibility are negatively related while market capitalization and dividend yield are positively related to firm value. These findings have important implications for energy firms, policymakers, and investors. Energy firms need to consider climate change risk in their investment decisions to maintain their market value, and policymakers should encourage firms to disclose their climate change risk to improve market efficiency. Finally, investors need to incorporate climate change risk in their investment strategies to mitigate potential financial losses.

Piven Artem

The article aims to develop new algorithms for the automated analysis of financial reports based on machine learning algorithms, which increase the efficiency and accuracy of converting financial information into a text form. In this context, special attention is paid to deep learning methods and neural networks that contribute to automating and analyzing financial reports and their further interpretation. The article examines the problems of generating text data from financial statements, describes the general characteristics of this process, and systematizes the technologies used to solve the task of developing text data and available methods of machine learning. Specific technologies of text generation using neural networks were analyzed, and the potential and prospects of machine learning in the creation of text data based on the analysis of financial reports were investigated. The process of developing a module intended for automated analysis of financial statements is described in detail, a technical task is created, which is necessary to solve the given task, and the structure and functionality of the developed module in the automated system are described. The result is a developed module for automated analysis of financial reports. Given that the module is created using Python, it can be easily integrated into different systems or function as an independent system, for example, a website or an application for a personal computer. The results of the developed automated module are demonstrated in the example of the analysis of financial reports of the companies Microsoft, Alphabet, and Apple.

Gustavo Bergantiños, Juan D. Moreno-Ternero

We study the problem of sharing the revenues raised from subscriptions to music streaming platforms among content providers. We provide direct, axiomatic and game-theoretical foundations for two focal (and somewhat polar) methods widely used in practice: pro-rata and user-centric. The former rewards artists proportionally to their number of total streams. With the latter, each user's subscription fee is proportionally divided among the artists streamed by that user. We also provide foundations for a family of methods compromising among the previous two, which addresses the rising concern in the music industry to explore new streaming models that better align the interests of artists, fans and streaming services.

Maria-Florina Balcan, Hedyeh Beyhaghi

We advance a recently flourishing line of work at the intersection of learning theory and computational economics by studying the learnability of two classes of mechanisms prominent in economics, namely menus of lotteries and two-part tariffs. The former is a family of randomized mechanisms designed for selling multiple items, known to achieve revenue beyond deterministic mechanisms, while the latter is designed for selling multiple units (copies) of a single item with applications in real-world scenarios such as car or bike-sharing services. We focus on learning high-revenue mechanisms of this form from buyer valuation data in both distributional settings, where we have access to buyers' valuation samples up-front, and the more challenging and less-studied online settings, where buyers arrive one-at-a-time and no distributional assumption is made about their values. We provide a suite of results with regard to these two families of mechanisms. We provide the first online learning algorithms for menus of lotteries and two-part tariffs with strong regret-bound guarantees. Since the space of parameters is infinite and the revenue functions have discontinuities, the known techniques do not readily apply. However, we are able to provide a reduction to online learning over a finite number of experts, in our case, a finite number of parameters. Furthermore, in the limited buyers type case, we show a reduction to online linear optimization, which allows us to obtain no-regret guarantees by presenting buyers with menus that correspond to a barycentric spanner. In addition, we provide algorithms with improved running times over prior work for the distributional settings. Finally, we demonstrate how techniques from the recent literature in data-driven algorithm design are insufficient for our studied problems.

Ezgi C. Eren, Zhaoyang Zhang, Jonas Rauch et al.

Traditional revenue management relies on long and stable historical data and predictable demand patterns. However, meeting those requirements is not always possible. Many industries face demand volatility on an ongoing basis, an example would be air cargo which has much shorter booking horizon with highly variable batch arrivals. Even for passenger airlines where revenue management (RM) is well-established, reacting to external shocks is a well-known challenge that requires user monitoring and manual intervention. Moreover, traditional RM comes with strict data requirements including historical bookings and pricing even in the absence of any bookings, spanning multiple years. For companies that have not established a practice in RM, that type of extensive data is usually not available. We present a data-driven approach to RM which eliminates the need for demand forecasting and optimization techniques. We develop a methodology to generate bid prices using historical booking data only. Our approach is an ex-post greedy heuristic to estimate proxies for marginal opportunity costs as a function of remaining capacity and time-to-departure solely based on historical booking data. We utilize a neural network algorithm to project bid price estimations into the future. We conduct an extensive simulation study where we measure performance of our methodology compared to that of an optimally generated bid price using dynamic programming (DP). We also extend our simulations to measure performance of both data-driven and DP generated bid prices under the presence of demand misspecification. Our results show that our data-driven methodology stays near a theoretical optimum (<1% revenue gap) for a wide-range of settings, whereas DP deviates more significantly from the optimal as the magnitude of misspecification is increased. This highlights the robustness of our data-driven approach.

Harshad Dave

Value is a vital term of economics. Veteran economists of past and present have worked on it to determine its constitution. Passing through the historical works done by many thinkers and philosophers on value, one will learn that there are various theories to explain the profile and nature of value, but each one is with some controversy. Unfortunately debates on it remain endless. Looking to the points of debates on it, it seems that we need to review our path of basic understanding about value. Here an innovative attempt is made to reveal the realistic constitution of value. The use value is framed with three basic conditions. The use value is knowledge in the mind of man. The value is separated and made independent from the forms of value like exchange value, compound value, complex value, and others. Further to this, the use value is designated as prime source of the value. The value is explained with basic four conditions and simultaneous fulfillment of all the four conditions is mandatory for the existence of the value. The value is just a sense and feeling only. The use value and value are independent from the forms of value (exchange value, compound value, complex value etc). This constitution of the value will remove many controversies, arguments and counter arguments that actually are born from the absence of clarity about real constitution of the value in our mind.