Profitability Performance Research Articles

How does market-incentive environmental regulation affect enterprises green growth? The mediating role of R&D investment and innovation output

Manufacturing enterprises is a country's economic mainstay. However, their longtime extensive growth pattern of “high growth and high emission” has brought huge environment pollution and restricted sustainable development. Under the circumstance of carbon reduction and global green development, market-incentive environmental regulation (MER) has attracted the attention of scholars and become a kind of important methods of encouraging manufacturing enterprises green growth. Presently, two fairly distinct viewpoints of “Follow Cost” and “Porter Hypothesis” both have their own supportive research results, and the explanation for the role of MER is completely opposite. What's more, empirical research at the enterprise level is scarce. Therefore, this study makes a further analysis from the perspective of enterprises heterogeneous innovation ability. Guided by the classic theory of “Follow Cost” and “Porter Hypothesis”, this study aims to evaluate the applicable conditions of MER's environmental improvement effect, and testing the differential impact mechanism of MER on enterprise Green Total Factor Productivity (GTFP), enterprise Green Technological Change (GTC) and enterprise Green Efficiency Change (GEC). All these give theoretical and empirical supplementation for the rationality of related theories. This study examines the hypotheses and mechanism according to 1220 Chinese manufacturing listed companies data 2011–2020. The empirical results indicate that: (1) In short term, MER has a significant positive impact on GTC, and a significant negative impact on GTFP and GEC. (2) As innovation driven factors, both enterprise R&D investment and innovation output play the mediating role. (3) Heterogeneity analysis indicates that the impact mechanism varies depending on enterprise industry-type, location and digital level. Thus, policymakers should develop appropriate MER policies, and manufacturing enterprises should strengthen technological innovation to help achieve environmental sustainability and profit performance.

How does Chinese stock market react to breaking news about COVID-19? Evidence from event study

Although the COVID-19 pandemic is no longer considered a public health emergency of international concern, research is ongoing to determine whether and how financial markets have been affected. Using an event study methodology, we examine Chinese stock reactions to breaking news about COVID-19 during 2020–2023, covering nine important events from the first declaration of the novel coronavirus to classifying COVID-19 from top category A to B in China. The empirical results show that the Chinese stock market reacts negatively to bad news, such as the breakout of COVID-19 and the dynamic zero-COVID strategy, and positively to good news, such as COVID-19 vaccine approvals, ending the zero-COVID strategy, and reopening boards. The overall intensity of reactions to bad news is stronger than that to good news. Small-sized stocks react more positively to good news and more negatively to bad news. Different industries have different reactions to different events; however, the sector that responds strongly and negatively to bad news does not have to respond strongly and positively to good news, which, to some extent, reflects their profitability performance during the pandemic. In China, consumer discretionary, utilities and real estate are the most negatively affected by the pandemic, while materials, health care, and energy are the most positively affected. This study has important implications for reminding market participants to pay attention to breaking news announcements about COVID-19.

Nodal price-based demand response management via orderly time-of-use strategy for an unbalanced isolated microgrid considering voltage stability constraints

This paper scrutinizes the compatibility of voltage stability criteria with price-based demand response management (DRM) for a large-scale isolated microgrid considering severe dependency of constant-impedance loads (CILs) on nodal voltage magnitude. Meanwhile, a hybrid nodal pricing (HNP) scheme is developed to determine clearing prices for resources and time-of-use (TOU) tariffs for price-responsive loads. The proposed framework is composed of three ingredients including two optimization molds with the objective of actual net profit and recast operation expense sequentially solved in an iterative process together with one pay-off module. The energy schedules, unbalanced voltages and nodal prices are determined considering intermittency of renewable resources, unbalances, circuit limitations, and reactive power requirements. Two bus and line voltage stability indices as well as voltage profile management criterion are used to prevent voltage instability. Thus, load profile is corrected such that voltage stability can be upheld as well. The proposed method is implemented via a modified CIGRE benchmark. The simulation results demonstrate that load factor improves at least 4.30% under desirable stability margins. Moreover, total energy loss and line voltage unbalance rate (LVUR) decrease at least 1.02% and 10.03% respectively.

Pengaruh Profitabilitas dan Kompleksitas Operasi Perusahaan Terhadap Audit Delay: Studi Pada Perusahaan Pertambangan Terdaftar di Bursa Efek Indonesia Peiode 2017-2021

This research is used to determine the effect of profitability and complexity of company operations on audit delay with company size as a moderating variable. This study-based quantitative research on mining companies aims to determine whether these factors have an effect on audit delay. The population in this study are mining companies listed on the Indonesia Stock Exchange (IDX).The sample used was purposive sampling, there were 49 mining companies listed on the Indonesia Stock Exchange (IDX), the authors used 10 companies that match the sample criteria in this study. The analysis used in this study includes descriptive statistical analysis, classic assumption test, hypothesis testing using regression analysis of the difference in absolute value.The results of this study indicate that profitability has a negative effect on audit delay, the complexity of the company's operations has no effect on audit delay. Firm size is unable to moderate the relationship between profitability and audit delay and firm size is unable to moderate the complexity of the firm's operations on audit delay.

Reducing Greenhouse Gas Emissions in Beef Cattle Farming through the Implementation of Animal Welfare Principles, as Part of Sustainable Rural Area Development

The beef cattle farming industry is facing a dual challenge - the need to reduce greenhouse gas emissions to combat climate change and the imperative to enhance the daily weight gain of cattle to meet growing global demand for meat. The integration of animal welfare principles stands out as a crucial and multifaceted solution that not only mitigates greenhouse gas emissions but also improves the daily weight gain of beef cattle. The results of this study indicate that the implementation of animal welfare guidelines plays a pivotal role in reducing greenhouse gas emissions. By providing cattle with appropriate nutrition, minimizing stress, and ensuring a healthy and stress-free environment, the emission of methane, a potent greenhouse gas, can be significantly lowered. This approach is not just ecologically responsible; it is essential for meeting international climate commitments and preserving the health of the planet's ecosystems. In addition to its environmental benefits, the integration of animal welfare practices has a profound economic impact. Healthy and content cattle are more likely to exhibit efficient digestion and increased feed utilization, leading to higher daily weight gains. This directly improves the productivity and profitability of beef cattle farming operations. Furthermore, the production of higher-quality meat from well-cared-for animals can drive market demand and revenue, strengthening the industry's economic viability.

Implementation of Training Strategies, Financial Management, Human Resource Management, Business Management, Layer Chicken

This article explores the implementation of financial management, human resource management (HRM), and business management training strategies in the context of layer chicken farming in Indonesia. Using a case study method, this research investigates best practices and challenges faced in implementing these strategies. The findings indicate that training in financial management, HRM, and business management significantly enhances operational efficiency and profitability of layer chicken farming businesses. However, the implementation of these strategies also encounters several obstacles such as limited resources, resistance to change, and a lack of understanding of the importance of effective management. This article provides valuable insights for layer chicken farming business owners and other stakeholders to strengthen management practices in the poultry industry. Further steps are also suggested to address the challenges encountered in implementing these strategies.

IMPACT OF COVID 19 WITH REFERENCE TO BSE & NSE OF SELECT COMPANIES - PRE AND POST LOCKDOWN

The Research Paper focuses on the impact of COVID 2019 on Top 5 blue chip companies in India, in different words the article analyses the profitability and financial performance of Top 5 blue chip companies in the course of pre and Post COVID 2019. The sample item used on this are 5 companies listed on Bombay & National stock change in the course of the duration from 2020-2021 to 2022-2023.For this secondary data is used. The data changed into being collected for 4 years before and after COVID 2019. This Study founds out the general effect of the COVID 2019 of top 5 blue chip companies in India which have been positive for maximum of the parameters for all of the companies. On the premise of this COVID 19 activities are fully favorable to top 5 blue chip companies.

IMPACT OF NPA (NON-PERFORMING ASSETS) ON THE PROFITABILITY PERFORMANCE OF THE SELECTED PUBLIC AND PRIVATE SECTOR BANK

The banking sectors performance is crucial for any economy as it reflects the countrys financial health. Banks play a crucial role in mobilising funds from various sectors of the economy, including both priority and non-priority sectors, to finance diverse economic activities.For many years, the banking industry has faced significant challenges due to unrecovered loans.The presence of Non-Performing Assets (NPAs) has significantly hindered the development and growth of the economy.This study delves into the impact of NPAs on the profitability performance of selected public and private sector banks in India from 2018-19 to 2022-23. Analysing key ratios like Gross NPA, Net NPA, and Return on Assets (ROA), sheds light on the relationship between NPAs and bank performance. The findings highlight higher NPA levels in public sector banks, adversely affecting their profitability. Moreover, a significant correlation between NPAs and profitability underscores the imperative of effective NPA management for enhancing bank profitability. These insights are crucial for bank management, policymakers, and regulators to devise strategies for mitigating NPAs and fortifying the banking sector.

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

The power grid presents several obstacles for demand forecasting and resource scheduling, such as a substantial amount of data, a growing number of factors influencing the demand profile, uncertainty in the generation profile of distributed energy from renewable sources, and a shortage of historical data. Here, we provide a unique market-oriented energy storage method based on artificial intelligence (AI) that aims to optimize operational profit in the electricity market between consumers, energy storage, and grid service providers. The approach is divided into two parts; the first is the Residual Network-50 (GR-ResNet-50) algorithm, which is used to overcome future uncertainties related to load needs and power pricing. The second uses deep learning based on the Radial Heap Basis Algorithm (RHBA) to determine the most effective charging or discharging operation considering the grid peak states, load demand, and the state of the batteries. The performance of the proposed method in this study is compared with the conventional method in terms of 0.007% of low MAPE and 20 seconds of less execution time. The research shows that the proposed strategy significantly lowers on-peak power, improves operational profit, and improves effective performance.

EVALUATING THE IMPACT OF DEPRECIATION ON THE PROFITABILITY: UK FASHION COMPANIES

This study investigates the sophisticated dynamics of depreciation and its influence on the profitability of fashion companies in the United Kingdom. The UK's fashion industry has gained substantial growth recently and witnessed escalated sales in both new and used apparel. However, the fashion industry faces sustainability challenges due to the predominantly resulting rapid expansion of the fashion sector. In this competitive business environment, understanding the role of depreciation in shaping profitability becomes pivotal. Examining data collected from 17 UK-based fashion companies spanning the 2018 to 2022 period, this research precisely analyses how depreciation influences key financial indicators: return on assets, return on equity, and operational profit. The methodological approach involves the application of quantile regression analysis, and it offers an in-depth exploration of the relationship between depreciation and these vital financial variables. The findings of this study reveal that there is no statistically significant relationship between depreciation and return on equity or return on assets within the purview of UK fashion companies. However, a positive and statistically significant relationship exists between the depreciation and operating profit. This positive relationship potentially arises from various factors such as enhanced asset utilization, long-term investments, involvement in capital-intensive industries, sustained revenue growth, and improvements in asset quality. These findings have significant implications for stakeholders within the UK fashion industry. While depreciation may not strongly influence return on equity or return on assets, its correlation with operational profit reveals a nuanced aspect of financial performance within these companies.

Evaluasi Efisiensi dan Profitabilitas Asuransi Syariah Melalui Analisis Laporan Keuangan

In the literature study related to evaluating the efficiency and profitability of Islamic insurance through financial statement analysis, previous studies highlighted various methods and approaches used to measure the performance of Islamic insurance companies. Financial statement analysis is the main focus in identifying factors that affect the operational efficiency and profitability of Islamic insurance companies. Some studies have used traditional financial ratios such as solvency, liquidity, and profitability ratios to evaluate the performance of Islamic insurance. However, due to the unique characteristics of Islamic insurance, studies have also proposed the development of specific metrics that take into account sharia principles, such as sharia compliance ratios and fairness ratios. In addition, non-financial approaches such as technical efficiency and allocative efficiency analysis have also been used to evaluate the operational efficiency of Islamic insurance. This research shows that factors such as scale of operations, business diversification, and managerial efficiency can contribute to the efficiency performance and profitability of Islamic insurance companies. Although there have been many studies conducted in this domain, there is still a need for more in-depth follow-up research to better understand the factors that influence the efficiency and profitability of Islamic insurance. With a better understanding of the performance of Islamic insurance companies, regulators and practitioners can develop more effective strategies to improve the stability and growth of this industry.

Evolution and role of virtual power plants: Market strategy with integration of renewable based microgrids

The use of distributed energy resources (DER) programs, which include separate renewable energy (RE) programs, has expanded in residential as well as commercial structures as an outcome of recent technological developments. The virtual power plant (VPP) may improve the security and reliability of an electricity grid's operations through including energy storage, changeable loads, and distributed energy resources (DER), among other characteristics. Consequently, a growing number of scholars tend to focus on VPP and providing recommendations for its improvement. In order to facilitate the incorporation of distributed generation into electricity distribution systems, cutting-edge management ideas for facilities have emerged in the last few years. As a result, it is now more crucial than ever to improve management abilities for the combining of dispersed energy output and consumption using various virtual power plants (VPPs). To increase operational profits, it's crucial to make use of their capacity to play a role in power markets. This work provides a classification scheme for, and an in-depth analysis of, recent studies that provide VPP models featuring interactions with various forms of electricity markets. The objective of this article is to find out which VPP strategy will yield the greatest possible return in each legislative scenario. These consist of the model's design, strategies for resolving complex mathematical problems, involvement in various marketplaces, and the application of the suggested models to real-world case studies. The research has been evaluated, and it has been concluded that contemporary models are more comprehensive and realistic. This article additionally elaborates on and emphasizes the VPP concept from other researchers' findings. Moreover, a number of prevalent VPP projects from throughout the globe are included. The VPP research also addresses some potential challenges and recommendations for prospective growth.

Bi-objective ship speed optimization based on machine learning method and discrete optimization idea

With the introduction of increasingly stringent carbon dioxide (CO2) emission regulations in the shipping industry, shipping companies face significant pressure to reduce emissions while ensuring profitable ship operations. A practical bi-objective speed optimization (BSO) algorithm for container ships is developed based on the variable weight idea, the discrete optimization idea, and the ideal point method to meet the urgent needs of shipping companies to improve operating profits and reduce emissions. By constructing two BSO models of ship main engine fuel consumption-ship sailing time and ship operating costs (SOC)-ship CO2 emissions (SCE), the effectiveness of the BSO algorithm is confirmed through extensive optimization experiments. The results show that compared to the historical sailing results, the BSO reduces the SOC by 4.43 % without causing a significant increase in the SCE, and reduces the SCE by 0.25 %. Moreover, the results demonstrate the effectiveness and feasibility of the BSO in reducing the cost of ship operations while reducing the impact on the environment. The results provide valuable references for shipping companies in the trade-off analysis between economic and environmental benefits and optimization decision-making.

Do European fintech benefit from bank-affiliated VCs?

In the current study, we scrutinize the efficacy of bank-affiliated venture capital (BVC) in augmenting the performance of European FinTech firms, with a specific sample size of 105 firms. The investigation is anchored in a resource-based view (RBV) and examines the strategic comportment of banks vis-à-vis FinTech enterprises. Utilizing a dataset that encompasses pre-Brexit European FinTech firms from 2006 to 2019, we implement logistic regression models for our primary analysis. Propensity score matching is invoked to mitigate selection bias, and machine learning techniques are applied to address data imbalance. The empirical evidence demonstrates that FinTech firms backed by BVC exhibit superior profitability and asset performance compared to their non-BVC-backed counterparts. The robustness of these findings persists even after accounting for selection bias and data imbalance. The study posits that bank-affiliated venture capital serves as a pivotal criterion for FinTech entrepreneurs in investor selection, thereby enriching the scholarly discourse on the symbiotic relationship between traditional banking institutions and emergent financial technologies within a European context characterized by information asymmetry and resource-based complementarities.

A logistics provider’s profit maximization facility location problem with random utility maximizing followers

We introduce a strategic decision-making problem faced by logistics providers (LPs) seeking facility location decisions that lead to profitable operations. The profitability depends on the revenue generated through agreements with shippers, and the costs arising when satisfying these agreements. The latter depends, in turn, on the service levels and characteristics of the shippers’ customers. However, at a strategic level, the LP has imperfect information thereof.We propose a stochastic bilevel formulation where a given LP (leader) anticipates the decisions of shippers (followers) arising from a random utility maximization model. Using a sample average approximation and properties of the associated optimal solutions, we propose a heuristic that can compute high-quality solutions. It is based on a reduced single-level mixed integer linear programming formulation that can be solved by a general-purpose solver after a preprocessing phase. We can quickly identify situations that lead to zero expected profit for the LP. Experimental results show that expected profit highly depends on shippers’ price sensitivities. Underestimating price sensitivities can lead to an overestimation of the expected profit.

Dividend cuts and a firm's investment opportunity set

AbstractThis paper re‐examines corporate dividend payout policy by focusing on how investors evaluate dividend cuts. Our focus is to understand why despite the documented on average negative market response, many dividend‐cutting firms experience a positive market response. We juxtapose a well‐established literature on corporate payout policies with the literature on the role of prior information in investor evaluation of newly arrived information and document that the market responds positively to dividend cuts when firms have positive investment opportunity sets. Our results support the primary thesis that not all dividend cuts are bad news—it depends. We validate this result by documenting that firms cutting dividends in the presence of investment opportunities do make significant investments such as capital expenditures and merger and acquisition activities in future years. Furthermore, we also find that future operating profitability is higher for dividend‐cutting firms with relatively higher investment opportunities. We also document that among dividend‐cutting firms, there is a statistically significant difference in the buy‐and‐hold returns for up to 2 years, between firms with high versus low investment opportunities. This provides support for the economic significance of our results. These results provide new evidence on how investors interpret corporate payout policies to distinguish among dividend cuts.

Multi-objective optimization of production system with staggered production

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers.

The Impact of the Effectiveness of Smart Quality Management on the Profitability Enterprises of Major Asian Companies

This study aims to identify the extent of the influence of smart quality management practices and their impact on the profitability performance of major Asian companies. This paper used a sample of 143 major companies for the purpose of testing the relationship from the years 2020 to 2023. The study concluded that there is a positive and important link among smart quality management and profitability performance. Smart quality management is considered important in modern concepts that represent a new technical method for managing business processes and institutions in general. Smart quality management aims to improve profitability and productivity. This study considers smart quality management to be an integrated management system that aims to improve the firm’s reputation and growing its profits and market share, and then confirm its existence and link in the field of effort.

EV Charging Scheduling Under Demand Charge: A Block Model Predictive Control Approach

This paper studies the online scheduling of electric vehicle charging by a service provider subject to a demand charge in a distribution system. Demand charge imposes a penalty on the peak power consumption over each billing period, representing a substantial cost for the service provider with a large number of clients. Because the demand charge is calculated at the end of the billing period, it poses challenges in real-time scheduling when energy demand forecasts are inaccurate, resulting in either overly conservative power consumption or substantial demand charge. We propose a block model predictive control approach that decomposes the demand charge into a sequence of stage costs. Optimality conditions on demand patterns are also presented and analyzed. Numerical simulations demonstrate the efficacy of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper addresses a significant practical problem of minimizing the demand charge on the real-time scheduling of deferrable demands. In particular, we consider a setting where a commercial electric vehicle (EV) charging service provider has to manage the online scheduling of a large number of arriving EVs at a charging facility subject to a maximum charging power constraint and a tariff with the demand charge. A major practical challenge is to balance the tradeoff between maximizing profit in scheduling as much EV charging as possible and the need to minimize penalty on the peak charging power. We propose a model predictive control strategy that decomposes the overall demand charge into a sequence of terminal costs. Also addressed is the practical constraint arising from the mismatched EV charging decision period and the power measurement period used to compute the demand charge. Using real data collected at the Adaptive Charging Network (ACN) testbed in simulations, the proposed approach yields 8-12% improvement in operational profit over existing benchmarks, while it has yet been tested in actual charging systems. In the future research, we will address the charging scheduling under demand charge over multiple charging stations.

Urban Air Mobility Profitability and Operational Sensitivity to Battery and Charging Technology

Designs for electric vertical takeoff and landing (VTOL) aircraft deviate from traditional aircraft designs and include a wide variety of different configurations. Significant uncertainty also exists regarding the status of future battery technology, including energy density and charge rates. This paper presents a simple analytic model to estimate the profitability of an urban air mobility electric VTOL aircraft for a variety of vehicle configurations, battery technology parameters, and economic factors. Five main elements are considered by the framework: aircraft performance, battery technology, mission profile, mission economics, and electrical grid parameters. A sensitivity analysis is provided, comparing the operational performance and profitability of three electric VTOL concepts (lift plus cruise, quadcopter, and side by side), with respect to electrical grid and battery technology factors. The takeoff weight to maximize the number of completed routes or the overall profitability is also examined. Interestingly, results show that these two values do not coincide across the entire design space due to the nonlinearity of the battery life cycle with respect to the depth of discharge, which strongly affects battery replacement cost.