Substantial Profits Research Articles

Development of wind power plants to increase the fulfilment ratio of renewable energy in Taiwan using system dynamics modelling

ABSTRACT This research aims to improve wind energy development by addressing environmental challenges and market dynamics. To achieve this, a system dynamics model was used to analyze the complex relationships between various factors, including wind energy production, electricity demand, costs, and government policies. The study focuses on increasing the fulfilment ratio and reducing the price of wind energy. By simulating different scenarios, researchers explored the potential impact of expanding wind farm capacity through the addition of onshore and offshore turbines. The research also investigates the role of government incentives, particularly the feed-in tariff (FIT), in stimulating investment in wind energy. The findings suggest that a higher FIT can significantly boost profitability, attract more investment, and accelerate the expansion of wind farm capacity. The optimal FIT level of 5.5 NTD/KWh is projected to lead to a 6.7% annual growth rate and substantial profits by 2040.

Analysis of Price Transmission Chili at the Level from Producer to Consumer in Tapin Regency

The fluctuating prices of chili peppers can significantly impact farmers positively and negatively. Farmers cultivating bird’s eye chili can reap substantial profits with high prices. However, they earn less when bird’s eye chili prices are low. Currently, market integration refers to the evolution of costs across different market sectors and the extent to which market sectors in various geographical locations are informed about the supply and demand occurring in a market. This study aims to: 1) analyze the price developments of bird’s eye chili at the producer, wholesale, and consumer levels in Tapin Regency; 2) examine the price transmission of bird’s eye chili from the producer level to the consumer level through prices at the wholesale level and the production of bird’s eye chili in Tapin Regency. The data used in this research are secondary in the form of monthly time series data. The study utilized the documentation method, with data collected from January 2017 to December 2022, totaling 72 data points. The data analysis method employed is the Vector Error Correction Model (VECM), with tests including the Stationarity Test, Optimal Lag Length Test, Stability Test, Cointegration Test, VECM Estimation, Impulse Response Function (IRF), and Forecast Error Variance Decomposition (FEVD). The findings indicate that the prices of bird’s eye chili exhibit monthly fluctuations. The higher price fluctuations at the producer level indicate that farmers have weak bargaining power in setting the prices of bird’s eye chili. The stationarity test shows that the prices of bird’s eye chili at the consumer (HK), wholesale (HG), and producer (HP) levels, along with bird’s eye chili production (PC), are stationary at the first difference. The optimal lag test, with the smallest Akaike Information Criterion (AIC), is lag 2. The cointegration test shows that the trace statistic and max-eigen values are greater than the critical value, thus accepting H1, indicating a long-term relationship. In the short term, the increase in bird’s eye chili prices is influenced by the two previous periods. The analysis results demonstrate asymmetric price transmission from the producer level to the consumer price level.

Pendapatan dalam Hubungannya dengan Perencanaan Ekonomi Kabupaten Muaro Jambi dilihat dari Sisi Perkembangan Indeks Gini Kabupaten Muaro Jambi

Analysis of the Gini index based on education level shows significant inequality in the distribution of education in society. Higher levels of education tend to be accessed by groups with higher incomes, while groups with lower incomes often face barriers to obtaining quality education. This is reflected in the Gini index, which shows that the gap between groups with good and limited access to education is widening. This inequality has an impact on economic opportunities and social mobility, suggesting that improvements in a more inclusive education system and equal access to education across all levels of society are essential to reducing inequality and achieving greater social justice. The secondary sector in Muaro Jambi also shows significant levels of inequality. In the manufacturing and processing industries, large companies that produce goods on a large scale often make substantial profits, while small and medium enterprises struggle to compete. Inequality is also reflected in the construction sector, where the income gap between skilled and unskilled construction workers, and between large and small contractors, is quite striking. This contributes to the high Gini index in this sector, reflecting income inequality among workers and employers. In the tertiary sector, which includes services, retail, finance, and insurance, income inequality tends to be higher. In the service sector, the difference in income between workers in customer service and managers or professionals is very large. In the finance and insurance sector, income inequality is also significant because wages in this sector are often very high compared to other sectors, creating a large gap between workers in these fields and those in lower-wage sectors. This unequal distribution of income leads to a high Gini index in the tertiary sector. The quaternary sector in Muaro Jambi, which includes technology, information, and research and development, shows high levels of inequality. This inequality is caused by the large differences in wages between technology professionals and workers in non-technology sectors, as well as between workers in large technology companies and small businesses. Specialized skills and high levels of education in this sector are often directly related to high incomes, while those in less skilled or educated positions earn much lower wages. This creates a clear inequality in the distribution of income in this sector.

Remanufacturing construction and demolition waste and incineration ashes into eco-blocks: Quantitative sustainability assessment

This study performed a sustainability evaluation of a proposed business for remanufacturing concrete materials from construction and demolition waste (CDW) and ash from the combustion of municipal solid waste (MSW) to produce eco-blocks for construction applications. Six possible industrial remanufacturing alternatives are proposed based on commercially available industrial machinery and their economic sustainability was compared by a cost and profit feasibility study. Furthermore, the technical feasibility was evaluated by considering the maturity of the selected machinery based on the relationships among the cost–productivity, power–productivity, and number of workers–productivity pairs. The proposed remanufacturing business achieves a maximum profit of 17,643 USD/day with a minimum remanufacturing cost of 30.65 USD/t for the production of 529 t of eco-blocks in an 8-h factory shift. A quantitative environmental sustainability assessment was performed using the remanufacturing cost, profit, concrete savings, energy savings, CO2 emission savings, and eco-cost savings as criteria based on demographic statistics from the literature for current and predicted CDW and MSW generation. CDW and incineration ash are widely available, so the proposed process is viable worldwide. Based on the technical, economic, and environmental sustainability results, a final sustainability index of 0.874 was calculated, which exceeds literature thresholds for a sustainable business, indicating that the proposed eco-block remanufacturing process has the potential to generate substantial profits, while positively contributing to reducing waste and energy usage by recovering valuable resources and producing value-added products. The remanufactured eco-blocks are prepared in the same way as typical concrete blocks and can be used for all of the same residential, commercial, and industrial construction and landscaping applications. The remanufactured eco-blocks have several advantages over typical concrete blocks, including a much lower manufacturing cost (∼50 % lower), significant environmental and resource savings by avoiding the use of new sand and gravel resources, and improved mechanical properties.

The Primacy of Commercial Gain over Ideology in Wars and Armed Conflicts

The paper provides a brief analysis of the causes and reasons for military conflicts and wars in the past and present and makes a forecast for the future. By comparing ideological differences and commercial goals, the author concludes that economic priorities are the primary causes of military conflicts and wars. Analyzing historical examples, the author shows that economic priorities have always been the main reasons for unleashing military actions. The main objectives that the aggressor solved by unleashing war were: expansion of the territory of its habitat; plundering of the defeated; transformation of the defeated population into slaves (cheap labor). These priorities, in the author’s opinion, are the main ones in all wars and armed conflicts in history. Similar tasks are currently being solved by the industrial and banking oligarchs of the United States and the United West in the Middle East, Africa, Latin America and Europe. Other goals and objectives of wars and armed conflicts are secondary and can be realized as additional ones. In this regard, the question of the primary and secondary objectives of commercial benefit and ideology is resolved in favor of commercial benefit. In the paper, on the basis of scientific and research works of the author, it is concluded that the current situation in the world is a well thought out and carefully calculated by the financial oligarchs of the United States and the united West, when a commercial project and its realization should bring them substantial profits, and the population of the planet Earth — destruction, grief and suffering.

How the Macroeconomic and Financial Structure Affects the Firm Value of a Large Telecommunication Company in Indonesia

TELKOM's strategic initiative, referred to as 5BM, acknowledges the company's considerable assets but notes that it has not yielded substantial profits. To address this, TELKOM has devised this strategy to improve its firm value. This study seeks to contribute to this goal by conducting research focused on analyzing the company's financial structure and evaluating the effects of financial structure, considering macroeconomic factors like interest rates, to enhance its overall value. The research employs a quantitative approach, using TELKOM’s financial data from 2014 to 2024, with variables such as firm size, liquidity ratio, leverage ratio, investment opportunity, efficiency level, profitability, and interest rates. The study applies multiple linear regression to assess the relationships between these independent variables and firm value, using Tobin’s Q as a measure of firm value. Descriptive statistical analysis is also conducted to summarize the characteristics of each research variable. Findings reveal that leverage negatively affects firm value, while firm size, liquidity, investment opportunity, efficiency level, and profitability have positive impacts. Although interest rates and dividend policy also contribute to firm value, their effects are not statistically significant. These results provide insights into the importance of TELKOM's financial structure in enhancing its market valuation

Raspberry Production Opportunity to Develop an Agricultural Business in the Context of the Circular Economy: Case Study in South-West Romania

This paper presents a study on the establishment and the capitalization of a remontant red raspberry crop, the Polka variety, on a privately agricultural land area of 0.2 ha in a crop with a support system in V using a geotextile membrane for soil mulching and the method of micro-irrigation by drip. It has been shown that the annual gross profit is advantageous for diversifying the population incomes of rural areas, and the red raspberry is economically profitable regarding cultivation because the recovery of the invested sum is achieved in a maximum of 5 years after the establishment of the culture. The aim of this paper is to explore the growth and commercialization of red raspberry cultivation on privately owned arable land in rural Romania, emphasizing its potential for productivity and sustainability in the context of the circular economy. This initiative not only delivers substantial profits for investors but also fosters rural development and boosts local income levels. The study demonstrates that this cultivation method of red raspberry, aligned with the principles of the circular economy, enhances sustainability by reducing waste, optimizing resource use, and involving local communities in production cycles.

Solo Raya Young Muslims Generation Decision to Invest in Sharia Stocks and Green Investment

The younger generation, particularly Muslims, faces concerns about an uncertain future, particularly regarding job opportunities. The unemployment rate in Indonesia has shown an increase, predominantly among the younger generation. This study aims to examine the impact of financial literacy, perceived risk, and high self-confidence on the decision to invest in stocks, particularly Islamic stocks. The research was conducted in the Solo Raya area with 125 Muslim youth investors as respondents, consisting of millennials and Generation Z. The analysis technique used was multiple regression, conducted using SPSS software version 24. The regression results, with a significance value below 0.05, indicate a significant influence of financial literacy, perceived risk, and high self-confidence on the investment decisions of young Muslim investors. Good financial knowledge encourages the younger generation to invest in stocks with confidence, anticipating substantial profits in the future. Perception of risk remains a consideration, leading young investors to delay investment decisions until they feel highly confident.

DEALING WITH CRYPTOCURRENCIES AND THEIR PERSPECTIVE IN THE FUTURE

The advent of cryptocurrencies has revolutionized the financial landscape, offering a new and innovative way for individuals to invest and manage their wealth. Investing in cryptocurrencies, such as Bitcoin, Ethereum, and others, has numerous advantages that make it an attractive option for those seeking to diversify their portfolios. One of the primary benefits of investing in cryptocurrencies is their potential for high returns. As the popularity and adoption of cryptocurrencies continue to grow, their value can fluctuate significantly, providing investors with substantial profits. Additionally, cryptocurrencies offer a level of liquidity and accessibility that is unmatched by traditional assets, allowing investors to buy and sell quickly and easily. This article aims to explore the advantages of investing in cryptocurrencies and their perspective in the future. We will examine the benefits of diversifying investment portfolios with cryptocurrencies, the potential for high returns, and the potential risks involved. Furthermore, we will discuss the current state of the market and the future prospects of cryptocurrencies.

A consensus-based solution for cryptocurrencies arbitrage bots in intelligent blockchain

Intelligent blockchain is an emerging field that integrates Artificial Intelligence (AI) techniques with blockchain networks, with a particular emphasis on improving the performance of blockchain, especially in cryptocurrencies exchanges. Meanwhile, arbitrage bots are widely deployed and increasing in intelligent blockchain. These bots exploit the characteristics of cryptocurrencies exchanges to engage in frontrunning, generating substantial profits at the expense of ordinary users. In this paper, we address this issue by proposing a more efficient asynchronous Byzantine ordered consensus protocol, which can be used to prevent arbitrage bots from changing the order of the transactions for profits in intelligent blockchain-based cryptocurrencies. Specifically, we present two signal asynchronous common subset protocols, the more optimal one with only constant time complexity. We implement both our protocol and the optimal existing solution Chronos with Go language in the same environment. The experiment results indicate that our protocols achieve a threefold improvement over Chronos in consensus latency and nearly a tenfold increase in throughput.

The Predictive Ability and Profitability of Moving Average Rules in the Saudi Stock Market

Abstract Following the implementation of capital market reforms, Saudi Arabia has opened up its stock market to international investors. This liberalisation has led to the inclusion of the stock exchange into leading global indices, which is expected to lead to an influx of foreign investment. Thus, it is important and timely to examine the efficiency of the Saudi stock market. To that end, this paper conducts a comprehensive investigation of the predictive ability and profitability of a popular trading rule, the moving average rule, using firm-level data for the Saudi stock market over the period 1st January 2008 to 31st December 2017. The results show that the moving average rule has predictive ability; the active rules for a majority of the sample companies outperformed the corresponding passive strategy. Furthermore, the results were economically significant as the rules were profitable even after the consideration of transaction costs. The results also showed that the most profitable moving average rules were those with short-moving average lengths, and that the introduction of a bandwidth, which serves to eliminate weak trading signals, had a positive impact on rule profitability. Importantly, the analysis showed that the most profitable component of the rule related to short selling as these trades resulted in higher profits than the long positions. A disaggregated analysis of sectors showed that the trading rules outperformed the buy-and-hold strategy in all seven industries considered, while the analysis of covariance revealed the importance of careful selection of filter size. Overall, the analysis documents significant inefficiencies in the Saudi stock market and suggests that the employment of a simple trading rule, based on past price data, can yield substantial profits across companies and sectors even in a costly trading environment. These findings suggest that the recent reforms that were implemented to improve the efficiency of the Saudi stock market have been suboptimal, and that further regulatory reform is required.

Analyzing Composite Stock Price Index Volatility in Response to Changes in Data Structure Using Bayesian Markov-Switching GARCH

Rapid economic growth was observed to have motivated economic actors and investors to maximize business performance using different methods. An example of these are stocks or shares securities showing that a person or entity has a stake in a company. Investors are primarily drawn to stocks due to their potential for substantial profits closely tied to the volatility in the stock market. The prevalence of this volatility has long been addressed using the ARCH/GARCH model, but it is not ideal for datasets experiencing structural changes. Therefore, a model was developed using the Bayesian Markov-switching GARCH approach to effectively capture the heteroscedastic component and structural changes in data and mitigate certain limitations, especially those associated with a small sample size. This study adopted the composite stock price index (CSI) data from March 2020 to April 2021 to model and understand the volatility. The results showed that the Bayesian Markov-switching GARCH model with the most negligible variance provided the best fit. It was also discovered that a more minor error variance corresponded to lower data volatility. Moreover, the concept of value at risk was used to assess the investment risk based on the criterion that a decrease in the CSI investments led to a reduction in the level of risk faced by the investors.

Cost and Benefit Analysis for Hydroponic System to Increase Women's Empowerment and Food Sustainability

This research aims to analyze the costs and benefits, the break even point (BEP), and the payback period (PP) for installing a hydroponic system in Jongbiru village. From the results of this benefit-cost analysis it appears that the hydroponic organic vegetable business is a very potential small business both economically and socially. The economic potential of small hydroponic organic vegetable businesses can generate substantial profits in the short term. The social potential of small hydroponic vegetable businesses is to empower housewives who previously had no income, and have the ability to support the family needs and economy. In the future, we hope that each family can build a simple hydroponic vegetable installation. The results were as follows, 1) the benefits were much more than its costs, 2) BEP can be reached in Rp. 2,700,000 sales, and 3) the payback period is more than a year. The hydroponic installation can also be planted with a wide variety of vegetables and fruits to meet the nutritional needs of the family, and also fulfill market demands. The more varied the plants used in the hydroponic installation, the wider the benefit-cost analysis. In this way, more harvests are likely each year, with faster BEP and higher selling points. Keywords: cost-benefit analysis, break even point (BEP), payback period (PP), hydroponic vegetable

Digital Marketing Strategies and Profitability in the Agri-Food Industry: Resource Efficiency and Value Chains

Agriculture is essential to any country’s economy. Agriculture is crucial not only for feeding a country’s population but also for its impact on other businesses. The paradox of agri-food companies generating substantial profits despite seemingly high product prices is explored in this article, focusing on the role of digital marketing within the agri-food industry. Enhanced digital marketing performance leads to efficient advertising campaigns, through reduced advertising costs and increased resource efficiency. To do so, the authors collected web analytical data from five established agri-food firms with the highest market capitalization. Then, linear regression and correlation analyses were used, followed by the utilization of fuzzy cognitive mapping (FCM) modeling. The analysis revealed that increased traffic through search sources is associated with reduced advertising costs. Additionally, enhanced website engagement contributes to lower advertising expenses, emphasizing the optimization of the user experience. However, it has been discovered that allocating funds for social media advertising eventually results in higher expenses with higher website-abandoning rate. Ultimately, successful management of the balance between product costs and profitability in the agri-food sector lies on the increased use of search sources and greatly reducing the use of social media sources.

Utilizing Genetic Algorithms in Conjunction with ANN-Based Stock Valuation Models to Enhance the Optimization of Stock Investment Decisions

Navigating the stock market’s unpredictability and reducing vulnerability to its volatility requires well-informed decisions on stock selection, capital allocation, and transaction timing. While stock selection can be accomplished through fundamental analysis, the extensive data involved often pose challenges in discerning pertinent information. Timing, typically managed through technical analysis, may experience delays, leading to missed opportunities for stock transactions. Capital allocation, a quintessential resource optimization dilemma, necessitates meticulous planning for resolution. Consequently, this thesis leverages the optimization attributes of genetic algorithms, in conjunction with fundamental analysis and the concept of combination with repetition optimization, to identify appropriate stock selection and capital allocation strategies. Regarding timing, it employs deep learning coupled with the Ohlson model for stock valuation to ascertain the intrinsic worth of stocks. This lays the groundwork for transactions to yield favorable returns. In terms of experimentation, this study juxtaposes the integrated analytical approach of this thesis with the equal capital allocation strategy, TAIEX, and the Taiwan 50 index. The findings affirm that irrespective of the Taiwan stock market’s bullish or bearish tendencies, the method proposed in this study indeed facilitates investors in making astute investment decisions and attaining substantial profits.

인공지능의 민사책임에 대한 소고

The recent development of artificial intelligence (AI) technology is bringing about changes at a faster pace and on a larger scale than any other period in human history. With technological advancements overcoming the limitations of medical AI through training with databases, AI technology has made remarkable progress since the inception of deep learning for image processing with convolutional neural networks (CNN) in 2012. The recent advancements in natural language processing (NLP) have accelerated the utilization of AI through sophisticated natural language processing, enabling machines to identify and understand data regardless of the complexity of the language. This has laid the foundation for the rapid and precise development of generative AI. In the era where generative AI is being utilized without pausing in its developmental speed, we considered the civil liability of AI in our civil law principles, taking into account the inherent characteristics of AI such as unpredictability, opacity, and the black box effect. To do this, we first examined the legal liability considering the stages of AI technology development in discussing the tort liability caused by AI. Even “Weak AI,” created by AI developers, may fall under “Gefahr,” and while not all types, some may apply to strict liability in terms of risk liability. Furthermore, while reviewing civil liability applicable to AI under fault-based and no-fault liability, we also looked at the trends in the EU comparatively. In discussing no-fault liability, particularly under the Product Liability Act, we examined the possibility and implications of applying risk liability to pharmaceutical manufacturing using generative AI technology as a representative example to overcome the limitations of the existing Product Liability Act. Humanity currently lives in an era of rapid technological development and exploding big data, enjoying numerous benefits due to these advancements. As user convenience improves and massive added value is created through technological progress, the meaning of risk liability in the realm of civil liability can gain more significance. Generative AI has already drastically reduced the costs and time required for new drug development, providing substantial profits to pharmaceutical companies. However, even if the existing Product Liability Act is applied, it may be difficult to adequately remedy the harm to victims due to the reasonable alternative possibility defense regarding design defects. In the era of generative AI, we examined the possibility of applying enhanced risk liability by assuming the case of pharmaceutical manufacturing.

Increasing school building usage through adaptable building design: A quantitative sustainability assessment

While school buildings have the potential to form the hearth of a local community, they currently remain unused for longer periods of time out of school hours in several regions. To exploit this large sustainability potential, school buildings must be able to allow for a flexible building usage. First, this study explores whether letting school infrastructure to external users can be economically profitable. Second, it tackles the knowledge gap on how ventilation systems can facilitate a short- and medium-long-term flexible building usage in a sustainable manner, which is essential to achieve a more extensive school building usage. To answer these research questions, a case study is carried out which includes a flexible building usage that based on the needs of educational stakeholders. The results of the profitability analyses indicate that it is possible for schools to yield substantial profits over a period of 20 years and that the financial risks are low. Next, a life cycle assessment (LCA) and life cycle cost assessment (LCCA) are carried out on three different ventilation strategies. It is found that a decentralized balanced mechanical ventilation system has the lowest environmental impact and life cycle cost, since this system requires few adaptions during the study period. The mechanical exhaust ventilation system has the highest environmental impact, while the centralized balanced mechanical ventilation system has the highest life cycle cost. An important insight of this sustainability assessment is that the material-related impact increases in a flexible context and that it sometimes outweighs the energy-related impact.

Hybrid wavelength selection strategy combined with ATR-FTIR spectroscopy for preliminary exploration of vintage labeling traceability of sauce-flavor baijiu

The allure of substantial profits has perpetuated the illicit trade of counterfeit vintage labels for baijiu. While various approaches have been employed to intelligently ascertain the vintage of baijiu, many of them are both cost-intensive and time-consuming. This work pioneered the use of Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric analysis, offering a non-destructive and economically viable method for discriminating sauce-flavor baijiu across different aging periods (1-, 2-, and 3-year). In this research, principal component analysis (PCA) was first conducted to explore clustering trends among distinct vintage groups. Subsequently, the effect of spectral pre-processing on modeling performance was explored. For wavelength selection, four wavelength selection methods (ReliefF, random forest variable importance (RFVI), variable importance in projection (VIP), and Venn) were first used to identify the subset of candidate features that potentially best mapped the vintage labels. Immediately following this, to explore the possibility of further improving the identification capabilities of the model as well as to reduce the redundant data that may still be present, sequential backward selection (SBS) was utilized for secondary feature reduction within the subset of candidates. The amalgamation of these two techniques is termed a “hybrid wavelength selection strategy.” Additionally, the dimensionality reduction effects of PCA and kernel principal component analysis (KPCA) were compared to demonstrate the robustness of the proposed method. Finally, classification models such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM) were developed. The results show that the spectral data need not be pre-processed, and the proposed hybrid wavelength selection strategy can further improve the identification ability of the model. Among the many models developed, ReliefF-SBS-GOA-SVM emerged as the most proficient classification model, yielding accuracy, sensitivity, and specificity rates of 94.44%, 95.23%, and 94.44%, respectively. This method not only holds promise for the discrimination of baijiu class attributes such as brand, origin, flavor, and vintage but also exhibits potential applicability in other non-targeted identification studies involving spectroscopy methodologies.

Revolutionizing Forex Trading: Harnessing AI for Seamless Market Automation

The article explores the integration of AI and automated trading in the Forex market, focusing on the development of a custom Expert Advisor using FXDreema. To improve trading success, the approach includes a custom indicator and a martingale mechanism for hedging. Backtesting results show clear trade opportunities and substantial profits. Iterative testing procedures are highlighted while discussing optimization and integration challenges. The significance of automated trading methods and the part FXDreema plays in democratizing the creation of expert advisors are emphasized in the references. The potential for consistent profitability and future optimization tactics of the Expert Advisor are emphasized in the conclusion.

Profitability Analysis of Markets Traded on Borsa Istanbul

Supermarkets significantly increased product prices in response to the sudden and sharp rise in the exchange rate in July 2018. A similar situation unfolded in 2021 when interest rates dropped, and the exchange rate experienced a steep increase. The surge in demand for foreign currency and subsequent interest rate cuts led to an escalation in the exchange rate. Consequently, production costs rose due to inflated raw material prices, resulting in higher product prices. Additionally, supermarkets allegedly engaged in cartel-like behavior, purposely withholding products from sale, further contributing to price hikes between 2021 and 2022. This study evaluates whether supermarkets garnered substantial profits during the period. Financial statement data from 2018 to 2022 was analyzed using vertical, trend, and ratio analyses to assess the financial position and profitability of five companies listed on Borsa Istanbul. Throughout the 2018-2022 period, except for 2022, BIM demonstrated the highest return on assets ratio. The return on assets ratios for 2022 and 2021 exhibited an upward trend, indicating an increase in profitability during 2022. SOK recorded the highest return on equity ratio in the 2018-2022 period, while CarrefourSA reported the lowest. Furthermore, the companies witnessed notable growth in revenues, gross sales profitability, and net profitability in 2022, surpassing previous years. This suggests they earned more profit by increasing prices at a greater rate than their costs in 2022. The operating profitability of the companies also experienced an upswing during this period. BIM held the largest share of operating profit in revenues in 2022, while CarrefourSA possessed the smallest share. In terms of net profitability, the companies fared better in 2022 compared to 2021, with net profitability exhibiting a more substantial increase during 2022.