Profitability Risk Research Articles

Potensi Keuangan Umkm Jasuke: Mendalami Hasil Penjualan di Kabupaten Bartim

This journal aims to analyze the results of UMKM operations in the Bartim area, which operates Padang Padang fresh snacks made from corn, milk and cheese or also called jasuke. Financial analysis is carried out based on sales results for the past year using several financial risks such as profitability risks, liquidity risk and activity risk, the data used in this study were obtained from UMKM jasuke's financial reports.

Heat-assisted extraction of phenolic-rich bioactive antioxidants from Enantia chlorantha stem bark: multi-objective optimization, integrated process techno-economics and profitability risk assessment

This present study investigates the heat-assisted extraction (HAE) of bioactive antioxidants from Enantia chlorantha stem bark (ECSB). The effects of process parameters of operating temperature (OT: 35–55 °C), extraction time (ET: 100–200 min) and solid: liquid ratio (S/L: 1:20–1:50 g/mL) are determined on the total polyphenol content (TPC), antioxidant activity (AA) and extract yield (EY) using the D-Optimal design. Multi-criteria optimization is also conducted to determine the HAE optimum conditions. The integrated process for E. chlorantha stem bark extracts (ECBEs) production is thereafter designed with the optimum HAE conditions and techno-economically analysed. Three industrial ECBEs production setups (5, 100 and 1000 L extractor capacities) are analysed for probable scale-up and the risk & sensitivity analyses are conducted via Monte Carlo simulation. The HAE parameters affect the extraction process significantly. The OT, ET and S/L that maximize the TPC, AA and EY simultaneously are: OT = 54.10 °C, ET = 120.16 min and S/L = 1:50 g/mL. The 1000 L plant extractor setup is the most economical capacity having the least unit production cost (UPC) of 763.52 US$/kg ECBEs. The certainty of achieving 763.52 US$/kg ECBEs for the 1000 L capacity is 55.43% and the only variable with negative contribution (− 37.9%) on ECBEs UPC is the extract recovery variable.

Microwave-assisted extraction proof-of-concept for phenolic phytochemical recovery from Allium Sativum L. (Amaryllidaceous): Optimal process condition evaluation, scale-up computer-aided simulation and profitability risk analysis

Allium Sativum L. (ASL) extract is one of the medicinal and aromatic products used in pharmaceutical, food, cosmetic and perfumery industries. The scientific literature bibliography regarding process scale-up simulation and techno-economics of phenolic extract from ASL Microwave-Assisted Extraction (MAE) is seldom reported. This study presented optimization and techno-economics of phenolic recovery from ASL extraction. Box-benken design extraction experiment was conducted at irradiation power (520 W – 1040 W), extraction time (2- 10 min) and solid-liquid ratio (0.4 – 1 g/100 ml). The extraction conditions that optimized experimental extractible yield and Total Phenolic Content (TPC) evaluated in Box-benken desirability of Response Surface Methodology. Bioactive finger printing of the extract was analyzed using High Performance Liquid Chromatograph (HPLC). Aspen Batch Process Developer was used for the process techno-economic analysis; profitability uncertainty was analyzed using Monte-Carlo Simulation (MCS). Optimal extractible yield (28.62%) and TPC (76.8 mgGAE/g dry extract) were obtained at irradiation time 7.62 min, microwave power 826.67 Watt (63.6%) with solid-liquid ratio 0.55g/100 ml. The HPLC fingerprint revealed bentulinic acid, galic acid, chlorogenic acid, ellagic acid and ferulic acid. Cost of Manufacturing (COM) for 5L, 50L and 500L extractors gave US$ 1,480, US$ 220 and US$ 56 respectively. Techno-economic results of 500L extraction capacity with 900 kg/year design gave batch size (0.25 kg), production rate (2 kg/min), cycle time (125 min), discounted net present value (38,133 US$), rate of return (34%) and pay back time (2.94yrs). MCS results show high certainties of achieving base case profitability indices. The study shows that MAE of garlic powder is economically feasible.

Polyphenolic Extract Recovery from Azadirachta indica Leaves via Green Microwave-Assisted Extraction Technology: GA-ANFIS Prediction, Cuckoo Search Algorithm Optimization and ASPEN-Based Scale-Up Techno-economics

This study presented hybrid genetic algorithm adaptive neuro-fuzzy inference system (GA-ANFIS) modelling, cuckoo search algorithm optimization (CSAO), and scale-up techno-economics of Azadirachta indica leaves’ microwave-assisted extraction (MAE). Box-Behnken design (BBD) of Design-Expert software was used to design MAE experiment using microwave power (520–1040 watts), extraction time (2–10 min), and solid–liquid ratio (0.4–1 g/ml) with two dependent variables: total phenolic content (TPC) and total extractible yield (TEY). Predictive GA-ANFIS and CSAO models for the MAE were implemented in Matlab 2019 environment. ASPEN base-case simulation and economic analysis for the phenolic extract recovery were achieved using CSAO optimal conditions. Process profitability uncertainty analyses were conducted with Monte Carlo simulation (MCS). BBD model results gave coefficient of determination (R2) 0.949 and 0.952 for TEY and TPC, respectively. GA-ANFIS results gave training R2 0.989, testing R2 0.999 and training R2 0.995, testing R2 0.992 for the TEY and TPC prediction, respectively. CSA optimal region for the extract recovery gave microwave power 1040 W, extraction time 4 min, S/L ratio 0.4, TEY 33.3%, and TPC 10 mg GAE/g dw. ASPEN base-case techno-economic results gave batch size (1.49 kg), batch time (135 min), total capital investment ($211,172), annual production cost ($42,386), net present value ($150,298), payback time (5.84 years), and rate of return (17%). The MCS profitability risk analysis revealed that the associated uncertainty for the process is less than 1%. Therefore, this work shows that Azadirachta indica leaves extract production using green technology is feasible.

To Recognize Indication of Financial Distress and Or Bankruptcy of Five Textile Company for Five Years Period Using Five Financial Distress Models

Background: In a business, risk of profit and loss is an inevitability. Every company have their own ways to mitigate those risk, prepare a proper treatment, and other efforts get the predetermined purpose, that is profit. Serious risk and its consequences can bring company into financial distress, and in the next step can become bankrupt. The Indications of it can be seen from the financial performance of the companies. Aim: To find out whether a company engaged in the textile and textile products sector is in serious financial difficulty which could have implications for bankruptcy, several theories have been developed. This study is to analyze indication of financial distress and its possibility to become bankrupt. The study is using financial data of five textile and textile product companies listed on the Indonesia Stock Exchange for five years of 2017 – 2021, using five models of Analysis of Financial Distress, that are Zmijewski Model, Fulmer Model, Grover Model, Altman Z-Score Model, and Springate Model. Method: This study uses secondary data on the textile companies listed on the Indonesia Stock Exchange. The sample used in this study were five companies. The sample selection uses purposive sampling. This type of research used in this research is quantitative descriptive, namely research on problems in the form of the current facts of a population. Testing research data using data analysis of model financial distress. Findings: This study uses secondary data on the textile companies listed on the Indonesia Stock Exchange. The sample used in this study were five companies. The sample selection uses purposive sampling. This type of research used in this research is quantitative descriptive, namely research on problems in the form of the current facts of a population. Testing research data using data analysis of model financial distress.

To Recognize Indication of Financial Distress and Or Bankruptcy of Five Textile Company for Five Years Period Using Five Financial Distress Models

Background: In a business, risk of profit and loss is an inevitability. Every company have their own ways to mitigate those risk, prepare a proper treatment, and other efforts get the predetermined purpose, that is profit. Serious risk and its consequences can bring company into financial distress, and in the next step can become bankrupt. The Indications of it can be seen from the financial performance of the companies. Aim: To find out whether a company engaged in the textile and textile products sector is in serious financial difficulty which could have implications for bankruptcy, several theories have been developed. This study is to analyze indication of financial distress and its possibility to become bankrupt. The study is using financial data of five textile and textile product companies listed on the Indonesia Stock Exchange for five years of 2017 – 2021, using five models of Analysis of Financial Distress, that are Zmijewski Model, Fulmer Model, Grover Model, Altman Z-Score Model, and Springate Model. Method: This study uses secondary data on the textile companies listed on the Indonesia Stock Exchange. The sample used in this study were five companies. The sample selection uses purposive sampling. This type of research used in this research is quantitative descriptive, namely research on problems in the form of the current facts of a population. Testing research data using data analysis of model financial distress. Findings: This study uses secondary data on the textile companies listed on the Indonesia Stock Exchange. The sample used in this study were five companies. The sample selection uses purposive sampling. This type of research used in this research is quantitative descriptive, namely research on problems in the form of the current facts of a population. Testing research data using data analysis of model financial distress.

TrustBlkSys: A Trusted and Blockchained Cybersecure System for IIoT

Industrial Internet of Things (IIoT) has emerged as a new paradigm in the era of smart systems where interactions and transmission of messages among various entities in an industrial ecosystem are done autonomously. This includes manufacturing of products, shipment process, storage of records, and counting the data, to name a few. However, the involvement of multiple cyber threats that accompany the smart devices may lead the organizations on a heavy risk of profits. Even though the number of cybersecurity approaches have been proposed to ensure a secure and efficient communication mechanism in IIoT systems, the identification of cybersecurity issues in IIoT applications while exchanging the data is still at its early stage. The aim of this article is to propose an efficient cybersecurity communication mechanism using the trust evaluation system of each device to make a correct decision during communication. In addition, the evaluated trust of each device is further analyzed and monitored through a blockchain-based mechanism. The proposed approach is validated against an existing scheme in terms of various security metrics, such as sensitivity of devices while communicating, convergence time of transmitting information and percentage, and probability of malicious devices in terms of data delivery rate, false authentication, and trust value computation.

Financial Risk Early Warning System for Coal Mining Company in Facing Disruption of Supply Chain

Business environment has changed a lot lately. Most of the factors that fuelled a surge in commodity prices are supply chain disruptions, Covid-19, and have been exacerbated by the war in Ukraine and the resulting sanctions on Russia. As a result, prices of commodities have soared and will remain high for most of the year. This global supply chain disruptions can negatively impact companies in a way of higher operational cost and the risk of low supply of the materials. In order to be able to face these risks, the company needs to understand the current financial risk of the company, build the early warning indicator for the risk, and decide what actions could be taken to minimize the impacts of the risks. This study is mainly composed of 4 parts: risk assessment, z-model analysis, financial ratio analysis, and risk prevention formulation. After conducting the risk assessment of internal and external factors, found that the main risk sources are supply chain disruptions, difficulties in getting financing, weather challenge, major accidents, and coal market volatility. Z-score model analysis from last 5 years data resulting the company pose great risk of profitability. Further analysis of financial ratios of profitability ratio is carried out resulting that mostly the profitability ratio of the company is below the industry average. Combining these qualitative and quantitative analysis leads to the company should focusing on supply chain disruption risk. Thus, the early warning system is constructed and the risk prevention formulation is defined.

Optimal offering strategy for wind-storage systems under correlated wind production

This paper formulates the offering problem for a cluster of wind-storage systems in the day-ahead energy market using a risk-constrained stochastic programming approach that anticipates different operating conditions in the real-time energy market. Wind-storage systems can be jointly operated as a cluster so as to achieve higher profitability. However, a meaningful positive correlation among the production provided by wind farms located in the cluster results in a higher level of uncertainty that imposes additional risk. A key issue is how this correlation influences the operation of the cluster in the energy markets. In order to study this subject, this paper presents the uncertainties involved by means of a number of correlated scenarios including: (i) the correlated prices in the day-ahead and the real-time markets, and (ii) the correlated wind power production of multiple wind farms jointly generated using an innovative scenario generation methodology. The comparative statistical analysis validates the good accuracy of the method proposed in order to capture the spatio-temporal correlation among the wind farms. The results of a realistic case study are, moreover, compared with those obtained by considering that the scenarios are generated individually for each wind farm. Upon considering the latter, the variability of wind power production is underestimated, which has a negligible impact on the expected profit; however, the profit risk modeled using the conditional value-at-risk is significantly overestimated. The overestimation error particularly concerns a less risk-averse operator of the cluster in the case of low wind power production.

Essence of the Company’s Current Assets, Strategies for Financing

The article focuses on the present-day economic situation, which is characterized by instability and high dynamism of the external environment, mainly affecting the formation and use of assets by companies. General approaches to the allocation of the essence of circulating assets have been formed. In this context, their interpretation, main groups and characteristics are considered. It is determined that the essence of current assets is expressed through the relationships between the following groups of indicators: inventories and return on assets, accounts receivable and return on assets, as well as relations between cash together with cash equivalents and return on assets. On this basis, a structural and logical scheme of the policy of managing the current assets of the company has been built. This allowed to define that the policy of management of current assets of enterprise is a multiaspectual process that concerns the main directions of the policy of formation and financing of current assets. It is substantiated that the policy of managing current assets should ensure a compromise between the risk of liquidity loss and profitability. Three strategies for financing current assets are considered. A conservative strategy for managing current assets can correspond to a moderate or conservative type of current liability management policy, but not to an aggressive one. Any type of current liability management policy can be matched by a moderate current asset management policy. An aggressive current asset management strategy can be consistent with an aggressive or moderate current liability management policy, but not a conservative one. Understanding all the advantages and disadvantages of the strategies for financing current assets allows the company to improve production capabilities, financial position, work with certain types of current assets and ensure the dynamic growth of the company in the strategic perspective.

A study of bank credit strategy based on logistic model

This thesis is based on logistic model and multivariate linear programming, using MATLAB to study the lending strategies of banks to MSMEs. We first analyzed the factors that can affect the credit risk of enterprises, and studied the influence of four factors on the credit risk of enterprises, namely, profit, development potential, supply and demand, and credit rating, and then used the data to measure the above four factors, and predicted the default risk index of each enterprise through logistic model, and to make the results more intuitive, we introduced the credit score A=100 × (1 - default risk index), thus providing a quantitative analysis of the credit risk of the enterprise. Then, we determined the interest rates of bank loans to enterprises with different credit ratings, and finally, we determined the credit strategy by considering the credit score. At the same time, we considered the case where there is no credit rating of enterprises, and to develop credit strategies for different enterprises, we first need to derive the credit score of each enterprise using the logistic model above, and then adjust the credit strategy by dividing the rating of each enterprise according to the ratio of each credit rating and the credit score above. The loans given to the enterprises of grades A, B, and C are taken as unknowns, and the total number of 100 million and each enterprise can only lend 100~1 million yuan are taken as restrictions, and the maximum value of the profit is solved by using multivariate linear programming to derive the loan amount for each grade of enterprises. Finally, the credit strategy is determined by ranking the enterprises in terms of their development potential and credit score, and lending the loans to each enterprise in turn.

Optimal strategy of taxi drivers at airports by a stochastic programming approach: Evidence from airports in China

It is applicable to address how taxi drivers make optimal choices at airports by mathematical modelling owing to its impacts of service quality. This study formulates such a problem by a new stochastic 0-1 integer programming model such that the research gap of the literature is filled. Specifically, when net gains of the taxi service in urban areas are random, the downside semi-deviation risk of the total profit is considered in this model, and on the basis of its property analysis, a number of useful decision thresholds are derived. A time threshold associated with price differences is defined and when it is greater than the required actual time of waiting for passengers, waiting is the optimal choice. Appropriate conditions are given to answer when the decision-making mode of the developed model is either risk-taking or risk-averse. The derived expected-profit-maximization and risk-minimization model by expectation method is proved to be risk-neutral. Main findings include: The provided thresholds are of easy use for taxi drivers to earn the maximum profit, while the stochastic model is more applicable; Optimal strategy of taxi drivers at airports depends on the service periods, the needed time of driving on freeway, the relative difference of net gains earned by driving on freeway and in city, and the critical number of queuing taxis at airports; By our model, different optimal decision in line with service periods are provided; Except two earlier periods, shortening expended time of boarding a taxi can greatly attract taxi drivers' waiting at airports.

Joint Decision-Making Model of Power Manufacturer Participation in Green Certificate Market and Medium - and Long-Term Power Market

To effectively guide power manufacturers in the medium and long-term market and green certificate market trading behavior. This paper firstly constructs the model of power manufacturers' participation in the green certificate market and the model of participating in the medium and long-term market of power, and establishes the coupling constraint of power manufacturers' participation in the power market and the green certificate market. Secondly, considering the speculative behavior of power manufacturers and using conditional value-at-risk to depict the profit risk of electricity generation due to the uncertainty of market price, the optimal comprehensive benefit considering the annual expected profit and risk of power manufacturers is taken as the decision-making objective, and the joint decision-making model of power manufacturers participating in the green certificate market and the medium and long term power market is established. It is helpful for power manufacturers to make trading decisions in medium and longterm market and green certificate market.

Profitability and profit risk analysis of corn farming in District of Utan, Sumbawa Regency: A cross-sectional study

The planting area of corn on Sumbawa Island continues to increase, although deforestation is leading to environmental disasters. This raises the question of whether this commodity is profitable enough for farmers and how high the level of profit risk is. Research has been carried out using multi-stage random sampling. This research was conducted in the district of Utan as, one of the high planting areas and amount of corn production in Sumbawa Regency. Then, 3 (three) villages out of 9 (nine) villages in the district were determined as samples by purposive sampling method representing villages with different levels of productivity. Pukat Village for the highest productivity, Orong Bawa Village for medium productivity, and Stowe Brang Village for low productivity with total respondents of 30 people. The result of the study showed that corn farming provides high profitability but with a relatively high-profit risk. This risk is partly due to the variation and relatively high production costs, especially for labor and water machine rent due to different financial capacities among farmers. The limited and scarce availability of seeds, fertilizers, and water, as well as weather uncertainty, were also considered responsible for the high-profit risk of corn farming. It is desirable to continuously provide financial assistance and/or subsidized inputs for farmers to ensure that they have enough money/funding to run good corn farming practices.

Comparison Analysis of ASEAN Banking Financial Performance During the Pandemic

The arrival of the Covid-19 Pandemic outbreak has had an impact on the collapse of the local economy. The macroeconomics used in this theory explains the relationship between the current economic situation and how it affects banking financial performance with the RGEC indicators were used in this study to compare the financial performance of Indonesia's banking sector with other ASEAN countries, such as Brunei Darussalam, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. This research is a component of comparative research. The subject of the study is ASEAN banking institutions. Sampling with the purposive sampling method in 2019 and 2020 obtained 13 banks produced a complete sample. The parametric homogeneity test is used to test hypotheses in statistical models. The findings show that although there were differences in profit risk and risk profiles of ASEAN banks during the pandemic, there was no difference in capital risk.

The beta anomaly in the Australian stock market and the lottery demand

Frazzini and Pedersen (2014) [Betting against beta. Journal of Financial Economics, 111(1), 1–25] report an insignificant performance for the betting against beta (BAB) strategy in the Australian equity market, suggesting that the beta anomaly does not exist in this market. We extend their sample period and find strong evidence for a low-beta effect. The arbitrage portfolio that takes long positions in low-beta stocks and short positions in high-beta stocks generates a significant abnormal return of 5.9% per year. The beta anomaly over 1995–2019 is strong and robust after controlling for risk factors and stock characteristics. Decomposition of the beta anomaly into stock and industry level components shows that the low-beta effect is driven by the firm, and not the industry level beta. We further find that lottery demand explains the low-beta effect. In a series of portfolio and cross-sectional analyses, we show that the abnormal returns related to the beta anomaly become insignificant after controlling for lottery demand. The findings are robust to different proxies for lottery demand, alternative beta estimation and regression analyses. There is no evidence that the beta anomaly can be explained by leverage constraints, coskewness risk or investment and profitability risk factors. We further show that beta anomaly is more pronounced during the upmarket than the downmarket periods and that lottery demand plays a role in its time-variation. The findings have important implications for retail and institutional investors who trade on beta.

Increasing the Investment Capacity of the Indonesian Diaspora

This service aims to provide training to Indonesian diapora residing in China. This is so that the Indonesian diaspora can understand the principles of investment and how to invest. This is important because investing is expected to provide an appropriate return and have an impact on the welfare of the Indonesian diapora. The method used in this service is lecture and discussion. The training begins with material presentations, then continues with questions and answers and discussions based on real problems faced by the Indonesian diaspora. What is obtained in this service is understanding the basic concepts of investment. In addition, it is necessary to know how to invest by understanding investment instruments based on the profile of the level of risk and level of profit. There are three stages that need to be done, namely, 1) compiling and planning life goals; 2) determine the amount of investment; and 3) execution. After running, it is necessary to carry out a review and evaluation stage so that it can invest properly and correctly.

A Heatmap for Monitoring Systemic Financial Stability Risks in Ukraine

This study presents an updated risk map of the Ukrainian financial sector – an analytical tool for identifying and monitoring the buildup and materialization of systemic risks. The risk map methodology that the National Bank of Ukraine used until 2021 has been revised to ensure that risk assessment is based on reliable quantitative indicators rather than expert judgements, as well as to extend the list of risks considered. The instrument allows the stability of the financial system to be assessed across key risks, such as macroeconomic risk, the credit risks of households and non-financial corporations, capital adequacy risk, profitability risk, liquidity risk, and foreign exchange risk. We introduce indicators that capture a wide range of economic and financial vulnerabilities and group them by risks. Each risk category contains from four to seven indicators that combine both actual data and expectations. Statistical checks show that the indicators clearly signal previous crisis episodes, as well as the buildup of vulnerabilities during the research period. We find that macroeconomic risk and foreign exchange risk have the best explanatory and predictive power, while the weaker performance of other risks could result from structural changes in the banking sector over the past decades that have affected the overall risk profile of the financial sector.

Construction of a Digital Platform for Enterprise Financial Management Based on Visual Processing Technology

Along with the continuous socioeconomic growth, more capital injection is needed to meet the daily operating cash flow demand compared to the traditional development model, so it is necessary to study the digital capability of enterprise financial management. Based on the evolutionary logic of financial risk identification, evaluation, and control, this paper summarizes the most suitable feature processing algorithm process for enterprise financial management systems by visual processing technology; for the visual guidance system, it needs to be able to identify artifacts quickly and accurately and compare different visual recognition algorithms experimentally. Considering the accuracy and real time of the visual guidance system, the efficiency of the algorithm is improved as much as possible under the condition of ensuring the accuracy, including the collection of work area features and the filtering preprocessing of data features, edge detection, and feature extraction, and, finally, the coordinate conversion can be carried out effectively to meet the requirements of the system for speed and accuracy. The digital platform for enterprise financial management built in this paper gives risk management strategies and implementation plans in six aspects, including improving the internal environment for risk management, setting control objectives in line with the development of the company, focusing on the response and control of major risks, ensuring efficient communication of information, and strengthening supervision. In terms of profitability risk, we propose to improve asset utilization, establish additional investment risk management departments, enhance capital utilization efficiency, and create core mobile products; in terms of asset operation, we propose to increase asset risk management, expand product sales scale, standardize cash flow system, and use centralized bank accounts.

Data‐Driven Newsvendor Problems Regularized by a Profit Risk Constraint

We study a risk‐averse newsvendor problem where demand distribution is unknown. The focal product is new, and only the historical demand information of related products is available. The newsvendor aims to maximize its expected profit subject to a profit risk constraint. We develop a model with a value‐at‐risk constraint and propose a data‐driven approximation to the theoretical risk‐averse newsvendor model. Specifically, we use machine learning methods to weight the similarity between the new product and the previous ones based on covariates. The sample‐dependent weights are then embedded to approximate the expected profit and the profit risk constraint. We show that the data‐driven risk‐averse newsvendor solution entails a closed‐form quantile structure and can be efficiently computed. Finally, we prove that this data‐driven solution is asymptotically optimal. Experiments based on real data and synthetic data demonstrate the effectiveness of our approach. We observe that under data‐driven decision‐making, the average realized profit may benefit from a stronger risk aversion, contrary to that in the theoretical risk‐averse newsvendor model. In fact, even a risk‐neutral newsvendor can benefit from incorporating a risk constraint under data‐driven decision‐making. This situation is due to the value‐at‐risk constraint that effectively plays a regularizing role (via reducing the variance of order quantities) in mitigating issues of data‐driven decision‐making, such as sampling error and model misspecification. However, the above‐mentioned effects diminish with the increase in the size of the training data set, as the asymptotic optimality result implies.