Average Profit Research Articles

Polynomial Moving Regression Band Stocks Trading System

In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression models to identify the stocks’ trends and two standard deviations from the regression model to generate the trading signals. This way, the MRB was transformed into a momentum indicator designed to identify strong uptrends that can be used by a fully automated trading system. Our results indicate that the behavior of Nasdaq100 stocks can be tracked using all three examined polynomial models and can be traded profitably using fully automated systems based on those models. The best performing model was the model that used a four-degree polynomial MRB achieving the highest average net profit (USD 162.73). Regarding the risks involved, the third model has the lowest loss in dollar value (USD −95.52), and the highest minimum percent of profitable trades (41.51%) and profit factor (0.55) that indicates that this strategy is relatively less risky than the other two strategies.

Microbial Fertilizing Products Impact on Productivity and Profitability of Organic Strawberry Cultivars

Poland is a major producer of various fruits, including strawberries. As growing consumer awareness of food quality, health, and wellbeing is increasing, farmers are receiving a new market opportunity for organic products of good quality. The integration of microbial solutions into agricultural practices can foster the transition of agricultural farms towards more resilient and sustainable production of quality food. The objective of this study was to assess the influence of novel microbial biopreparations (microbial fertilizing products) containing Bacillus sp., humic acids, and other organic compounds on the economic viability of three strawberry cultivars (‘Honeoye’, ‘Vibrant’, and ‘Rumba’) under organic farming conditions. This study was conducted in 2021 as a field experiment. Irrigated and non-irrigated strawberries were treated with five microbial fertilizing products (K2–K6). The single plot area was 16 m2, with a total of 144 plots. The adopted planting density of strawberries was 30,052 per hectare. K3 treatment was found to be the most universal microbial treatment in terms of positive impact on yields, with significant yield increase on both the non-irrigated (yield increase of 3.76 t·ha−1) and irrigated experiments (yield increase of 5.78 t·ha−1). The K4 treatment on the non-irrigated strawberries resulted in a yield increase of 4.96 t·ha−1, which at the same time had no effect on the yield of the irrigated experiment. On average, application of the K2–K6 combinations on the non-irrigated strawberries resulted in a yield increase from 13.4% (K2) to 33.5% (K4). The irrigated strawberries showed a yield increase from 3.9% (K4—non-significant yield increase) to as much as 36.1% (K3). The highest direct surplus for the non-irrigated strawberries was recorded for the K4 treatment (38,603 PLN·ha−1) and for K3 for the irrigated experiment (42,945 PLN·ha−1). The direct surplus for ‘Rumba’ and ‘Vibrant’ was higher than for ‘Honeoye’ on both the irrigated (22% and 53%, respectively) and non-irrigated (19% and 18%, respectively) experiments. The average profitability index for all tested non-irrigated and irrigated varieties improved when treated with microbial fertilizer products, with profitability indexes of 143.3–168.8% on the non-irrigated plantation and 129.2–169.7% on the irrigated plantation. The tested microbial fertilizing products proved to be valuable products to improve the productivity and economic effectiveness of organic strawberry production. At the same time, their use needs to be adapted to local plantation conditions.

The effects of removing gender-based price discrimination on movie demand: Estimates using smartphone location data

Since 2021, an increasing number of movie complex firms in Japan have eliminated long-standing gender-based price discrimination by replacing discounts for only women with discounts for all customers. This study examines the impact of this discount change on movie demand. Using theater-level visit data by gender and a difference-in-differences strategy, I find increased movie demand for both males and females, indicating inter-gender interdependence of movie demand. A profit analysis shows that the average movie theater profit remains at the same level as that before the discount adjustment. Lastly, combining the difference-in-differences estimates with a random utility model reveals that joint movie consumption enhances paired customers' movie-watching utility.

Actionable human–water system modelling under uncertainty

Abstract. This paper develops an actionable interdisciplinary model that quantifies and assesses uncertainties in water resource allocation under climate change. To achieve this objective, we develop an innovative socio-ecological grand ensemble that combines climate, hydrological, and microeconomic ensemble experiments with a widely used decision support system for water resource planning and management. Each system is populated with multiple models (multi-model), which we use to evaluate the impacts of multiple climate scenarios and policies (multi-scenario, multi-forcing) across systems so as to identify plausible futures where water management policies meet or miss their objectives and to explore potential tipping points. The application of the methods is exemplified by a study conducted in the Douro River basin (DRB), an agricultural basin located in central Spain. Our results show how marginal climate changes can trigger non-linear water allocation changes in the decision support systems (DSSs) and/or non-linear adaptive responses of irrigators to water shortages. For example, while some irrigators barely experience economic losses (average profit and employment fall by < 0.5 %) under mild water allocation reductions of 5 % or lower, profit and employment fall by up to 12 % (∼ 24 ×) when water allocation is reduced by 10 % or less (∼ 2×). This substantiates the relevance of informing the potential natural and socio-economic impacts of adaptation strategies and related uncertainties for identifying robust decisions.

Firm Value between the Banking and Property Industry: Evidence of Pandemic Covid 19 in Indonesia

The study aims to find evidence of the impact of profitability and dividend policy on firm value. The population of this study are banking and property companies listed on the IDX (Indonesia Stock Exchange). The findings of this study indicate that in the banking industry, the average value of firm value and dividend policy during the pandemic increased, while profitability decreased. For the property industry, the average firm value and profitability during the pandemic increased, while the dividend policy decreased. There is a difference between the average profitability before and after the Covid-19 outbreak for the banking and property industries. Meanwhile, the average dividend policy and firm value are not statistically different.

Financial Performance Analysis Reviewed from Liquidity, Solvency and Profitability Aspects

This research was conducted to analyze the financial ratios of PT Berlina Tbk during the period 2019 to 2022 see the company’s ability to use ratios. This research uses secondary data by utilizing financial reports and company reports. The analytical tool used in this research is liquidity, solvency, and profitability ratios. Based on the results of this research, it can be seen that the level of PT Berlina Tbk liquidity ratio, which is assessed by the average current ratio of 71,04%, cash ratio of 6,06%, and quick ratio of 44,16%, is considered illiquid. The solvency level of PT Berlina Tbk, which is assessed from the average debt to assets ratio of 59,57%, debt to equity ratio of 147,80%, and long-term debt to equity of 64,38%, is also considered not solvable, where the average calculation results the ratio is in poor condition and does not meet industry ratio standards. And for PT Berlina Tbk profitability level which is assessed from an average gross profit margin of 3,43%, net profit margin of 15,43%, and return on assets of 8,40%, which is considered less efficient, there needs to be a policy so that the profits obtained are more efficient.

SISTEM PENGEMBANGAN USAHA PEMBIBITAN DURIAN (Durio zibethinus)

The sampling method for carrying out this research used the census method, by taking all members of the population as samples from the number of durian breeding farmers (2 people).\ The total cost for the grafted durian seedling business in Sebelimbingan Village is IDR. 56,130,653.81 with an average of Rp. 28,065,326.91//farmers. The production produced by the durian breeding business in Sebelimbingan Village was 24,800 stems with an average of 12,400 per farmer. The revenue obtained was IDR. 421,600,000.00 with an average of Rp. 210,800,000.00/farmer. The average profit obtained is IDR. 172,252,892.50//farmers.

Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties: An Agent-Based Modelling Approach

Despite the anticipated growth in the global demand for energy commodities, the frequently changing market dynamics imposed by environmental regulations and political sanctions create end-user demand uncertainties. This imposes the need for prompt quantitative decision-making approaches to understand how various market structures affect the planning of current natural gas projects. Agent-based modelling (ABM) emerges as a powerful approach to facilitate expedited and well-informed decisions amidst limited timeframes. This study deploys agent-based modelling to investigate natural gas allocation across various utilisation routes under diverse economic and environmental scenarios. Results from four main cases and two sub-scenarios imply that the allocation strategy is driven by utilisation routes considered in each case, followed by the allocation target (i.e., economic or environmental) and the operational bounds. The results reveal that cases prioritising natural gas monetisation for export outperform those meeting power requirements in average annual profitability. In case 4, considering a full network with power, the average annual profitability in the economic scenario reduces by approximately 47% compared to case 3, representing the optimal network configuration with $5.22 billion in average annual profitability. However, the economic scenario of case 3 demonstrates the second-highest rate of emissions (0.66 CO2-eq t/y), following the hydrogen-rich process routes in case 2. Overall, this study presents an innovative data-driven framework for enhancing strategic resource allocation in dynamic business environments. By integrating empirical evidence and technical data with an advanced technical tool (i.e., ABM), the framework provides decision-makers and policymakers with valuable insights for managing uncertainties and shifts in market structures, particularly in existing natural gas projects.

Homeward Stroke Recovery: Results of a Home Rehabilitation Program

Evaluate the feasibility of a homeward stroke recovery (HSR) program by measuring functional outcomes, intervention fidelity, and Profit & Loss. A retrospective observational analysis of 23 stroke survivors discharged to a home healthcare (HHC) program offering early and intensive rehabilitative therapy within 24 hours of index hospitalization. Functional outcomes include timed up-and-go, transfer functional independent measures, and Barthel Index. Clinical outcomes are mortality, all-cause 30-day hospital readmission, and fall rates. Process measures consist of time-to-first HHC visit, number of physical therapy (PT) and occupational therapy (OT) visits from initial evaluation, and total number of visits by clinician type. Financial outcomes include average daily gross HSR revenue compared with an inpatient rehabilitation facility (IRF) stay and average daily net HSR profit. Functional outcomes significantly improved across all measures ( p < .001) without reported falls, deaths, and only one unrelated readmission. On average, HSR began within 1.6 days from hospital discharge including 4.2 PT and 3.7 OT visits within 5 days of respective initial evaluation. Patients received an average of 9.4 nursing, 9.2 PT, 6.4 OT, and 2.2 speech therapy visits. Exploratory analyses demonstrated similar functional recovery despite longer hospitalization (rs = 0.43; p = .039). Revenue per day was lower for HSR than IRF services but costs exceeded the reimbursement rates for this program. The HSR program is feasible given that patients realized significant functional improvement with negligible consequences. Reimbursement reform is necessary to sustain and scale the program. Further studies with larger sample sizes are warranted.

Performance analysis of a medical waste gasification-based power generation system integrated with a coal-fired power unit considering dispatching optimization

This paper collects into a medical waste plasma gasification, a cleaning system, a gas turbine, a syngas storage tank and a coal-fired power unit to establish the medical waste plasma hybrid peak shaving system. In this system, medical waste is transported into a plasma gasification furnace and prepared as syngas by reaction. Then, the syngas is delivered to the gas turbine after cooling and deacidification. Finally, the flue gas from the combustion of the gas turbine is used to heat the condensate of a coal-fired power unit. Compared to the coal-fired power unit, the system has a larger range of peak load capability. Establish the integrated regional power grid dispatching model. The paper selects five typical days, which combine the actual data to comprehensively consider many factors. Then analyze the optimal economy of the regional power grid and propose the optimal dispatch scheme for the regional power grid. After modification, the system energy efficiency is 37.38 %, and system exergy efficiency is 36.19 %. After the dispatching optimization, the average daily profit is 140.78 k$, the profitability can reach initial cost in 6.20 years, and the net present value generated by the medical waste plasma hybrid peak shaving system is 178,410.43 k$.

The New Fishing Technique With Lighted Gillnets in Lake Kivu: Panacea or Pandora's Box?

ABSTRACTA new fishing technique has been introduced in Lake Kivu, across the Congo side. It consists of gillnets on which light‐emitting diode (LED) lamps are attached. Traditional fishermen have introduced the fishing technique without any studies related to the positive and drawback effects of this fishing technique. We sought to understand whether the gillnets with LED lamps fishing technique improve fishing catches and can be regarded as sustainable for the Lake Kivu ecosystem, or is it just only a marketing strategy for gillnets and LED lamp sellers. Two complementary approaches have been used in this study. Experimental fishing campaigns were conducted from June 2021 to June 2022 and monitoring of fishing team in the field coupled with surveys of fishermen about the effectiveness use of this new fishing technique. We observed that (i) 88% of fishermen using gillnet in the Bukavu sub‐basin have already adopted the recently introduced fishing technique; (ii) the average catch per unit effort of gillnets with LED lamps is higher than those without LED lamps (3.84 ± 1.95 kg/net vs. 2.69 ± 2.04 kg/net); (iii) these gillnets retrieve larger specimens (79.88 mm vs. 76.69 mm) and generate monthly a higher average profit to fishermen ($916.35) than the fishing technique lacking the LED ($807). However, (iv) the gillnets with LED lamps generates a lot of plastic trash: around 2130 kg of plastic bags/month under fishing conditions in the Bukavu sub‐basin. So, this new fishing technique is a real panacea for its practitioners, but a veritable Pandora's box generating plastic pollutants in Lake Kivu. Instead of banning this new fishing technique, we need to adapt it by using submersible LED lamps.

A sustainable recycling approach for construction: A case of remanufacturing waste rebar into steel nails

Minimizing waste in the construction industry provides economic and environmental benefits. Since steel has high embodied energy and many applications, recycling rebar waste contributes to a circular economy. This study proposes remanufacturing rebar into nails as a valuable by-product. In an experimental case-study to prove the technical feasibility of the process, short pieces of rebar waste were stripped to remove the ribs, welded into a coil, drawn to reduce the diameter, and formed into nails. The costs and technology of four alternative remanufacturing process portfolios were analyzed, giving an average profit of 576 USD/t of waste rebar. A cement consumption database was constructed based on literature data and regression analysis was applied to analyze the consumption of concrete and new rebar. The potential rebar waste was calculated assuming 3 %, 5 %, 8 %, or 10 % of the total new rebar consumption. Cement consumption data at the global and country scales were used to calculate the consumption rates of new and waste rebar to derive profit–remanufacturing potential curves. Regression analysis of these curves was performed to determine the economic sustainability degree of satisfaction (DSes). A second approach to find DSes applied regression analysis to the remanufacturing profits for the four rates of rebar waste together. The mean DSes values (0.62–0.87) and economic sustainability index (0.33) exceed the literature threshold of 0.6 and 0.26, respectively, indicating that remanufacturing rebar waste into steel nails is an economically sustainable business. The nails can be used in the construction industry to contribute to a circular economy, thereby reducing the environmental impact of steel waste and the production of new steel nails. The overall sustainability was considered by introducing Okun's Law to ensure that profitability is not achieved at the expense of development, and weighting the importance of economic sustainability based on literature averages. Rebar is used as a standard construction material globally, so these findings have broad significance. The proposed remanufacturing process is especially relevant for less-developed economies with higher rebar waste rates to stimulate small businesses and a circular construction industry.

ANALISIS RASIO PROFITABILITAS DALAM MENGUKUR KINERJA KEUANGAN PERUSAHAAN PT. INDOFOOD SUKSES MAKMUR TBK

The purpose of this research is to analyze the Profitability Ratios in measuring the Financial Performance of PT. Indofood Sukses Makmur Tbk. The method used is quantitative analysis in the form of Financial Statements. The technique employed is descriptive analysis using financial ratios. The results of this research indicate that, based on the average profitability ratios from 2019-2023, the company is capable of measuring profit growth with the profitability ratio of PT. Indofood Sukses Makmur Tbk, in terms of Return on Assets (ROA), whereas Return on Equity (ROE) is still considered very low or has not yet met the industry ratio standards. The average ROA ratio is 7.84%, while the industry standard value is less than 20%, and the average ROE ratio is 14.21%, but it has not met the industry standard value of less than 25%. However, the Gross Profit Margin (GPM) exceeds the profitability industry ratio standards, as the GPM has an average ratio of 34.58%, with the industry standard ratio value being more than 30%. This indicates that the company has not yet been able to manage its finances effectively in terms of ROA and ROE, but it has managed its finances effectively in terms of GPM, meeting the operational standards.

OPTIMIZATION AND FORECASTING OF A SECURITIES PORTFOLIO BASED ON MACHINE LEARNING METHODS

The article is devoted to the demonstration and analysis of the possibilities of connecting the Markowitz model, which is a classical model for forming a portfolio of securities, and forecasting methods based on machine learning data. The relevance of the chosen topic is justified by the fact that the use of machine learning methods makes it possible to simplify the activities of financial sector workers and the work of professional stock market players, as well as reduce the complexity of interpreting mathematical models and methods of their construction for private investors. The purpose of this work is to use machine learning methods to solve the problem of building an optimal securities portfolio and predicting its behavior. Tasks: - build a Markowitz model based on the selected data; - to build an optimal portfolio based on the proposed optimality criterion; - offer a time series forecast; - to assess the adequacy of the constructed model; - combine the Markowitz model, which belongs to the class of widely known classical models, with the construction of a prediction based on machine learning. Methods and models: machine learning methods are used in the work, the Markowitz model is built based on the selected data, then the Markowitz model is combined with the construction of a forecast based on machine learning. Research results: the optimal portfolio for AXP has been built and its forecast has been fulfilled. The formation of the portfolio was based on the Markowitz model. The data obtained were tested for adequacy and showed fairly good results. A portfolio was built, the optimality criterion of which is the ratio of average profitability and volatility of profitability (maximizing average profitability while minimizing its volatility). The Markowitz model, which belongs to the class of widely known classical models, was combined with the construction of a prediction based on machine learning.

Analisis Keuntungan dan Efisiensi Usaha Keripik Pisang di Kabupaten Konawe

This study aims to analyze the benefits and efficiency of banana chips business in Konawe Regency. This study was conducted in Konawe Regency with a population of 5 people from Unaaha District, Amonggedo District, and Uepai District. The sample in this study was taken with the same number of respondents as the population, namely 5 people using the census method. Data sources consist of primary data and secondary data. Data collection techniques through interviews, observations, literature studies, and libraries and documentation. The data analysis method used in this study is to use cost analysis, revenue, profit, and Benefit/cost ratio or B/C ratio. The results of this study are that the banana chips business in Konawe Regency makes a profit of IDR 58,328,866/month with an average profit of IDR 11,665,773/month. While the average income is Rp 21,515,000/month and the average total cost is Rp 10,171,954/month. The results of the B/C ratio calculation obtained a B/C Ratio value of Rp 1.15 which shows that the results are greater than >1, meaning that every Rp. 1 cost incurred provides a profit of Rp 1.15. With the criteria of B/C value> 1, the banana chips business in Konawe Regency is productive or profitable and efficient to be worked on and developed.

Supply responsive scheduling for ethylene cracking furnace systems based on deep reinforcement learning

AbstractEthylene is one of the most important chemicals, and scheduling optimization is crucial for the profitability of ethylene cracking furnace systems. With the diversification of feedstocks and the high variability in prices, supply chain fluctuations pose significant challenges to the scheduling decisions. Dynamically responding to these fluctuations has become crucial. Traditional mixed integer nonlinear programming (MINLP) models lack the capability of supply chain response, while receding horizon optimization (RHO) models require parameter prediction and repeated optimization solving. To address this challenge, we propose a deep reinforcement learning‐based framework that includes an ethylene dynamic scheduling environment and a decision agent based on deep Q‐network. Across three test cases, compared to the MINLP and RHO models, this framework significantly minimizes losses caused by supply chain fluctuations, thereby increasing daily average net profits by 9%–27%, demonstrating its significant potential for application in responsive scheduling in the presence of supply chain fluctuations.

Analysis of Allocative Efficiency of Watermelon Farming in Kapuas Regency, Central Kalimantan Province

The objective of this research is to analyze the costs, revenues, and profits of watermelon farming, to examine the impact of the use of production factors in watermelon farming, and to evaluate the allocative efficiency level of watermelon farming in Kapuas Regency, Central Kalimantan Province. The population of this research consists of 113 farmers, from which 55 were selected using simple random sampling techniques. The data processing method used to analyze the costs, revenues, and profits of watermelon farming includes using a multiple linear regression model, specifically the Cobb-Douglas function type. Additionally, to analyze the allocative efficiency of production factors used by farmers in watermelon farming, the comparison of a production factor’s Marginal Product Value (MPVx) with the price of the production factor (Px) is set to be equal to 1. Based on the results of the research and data processing, the average revenue obtained from watermelon farming in Kapuas Regency is IDR 75,619,181.82 per farm, and the average profit earned by watermelon farmers is IDR 45,541,259 per farm. The statistical test results of the Cobb-Douglas production function show that collectively (simultaneously), the production factors (land area, number of seeds, organic fertilizer, inorganic fertilizer, pesticides, and labor) significantly affect watermelon production. However, the partial test results indicate that the production factors of land area, seeds, organic fertilizer, inorganic fertilizer, and labor significantly affect watermelon production, except for the production factor of pesticides. The statistical test results for allocative efficiency (price efficiency) show that three production factors, namely organic fertilizer, labor, and pesticides, are used efficiently, meaning the marginal product equals the price of the production factor. The use of land area, seeds, and inorganic fertilizer is not yet efficient and needs to be increased to reach an efficient point.

Optimizing electric vehicle charging scheduling using enhanced multi-agent neural networks with dynamic pricing

Electric vehicles have gained widespread recognition as an environmentally conscious mode of transportation, primarily due to significant technological advancements and reduced emissions. When seamlessly integrated into the smart grid infrastructure, these vehicles can serve as flexible energy consumers and potential energy storage units. However, the efficient management of charging stations due to dynamic load demand & pricing poses a significant challenge, aiming to reduce wait times for electric vehicle owners and reduce electricity costs. To overcome this challenge, this study proposes a solution based on dynamic pricing policy and the adaptive Markov decision process. The approach utilizes a reinforcement learning-enabled enhanced multi-agent neural network (EMANN) to optimize charging schedules. This adaptive Markov decision process achieved optimal scheduling by minimizing charging costs during off-peak hours while completing charging in the shortest possible time. Furthermore, a constrained policy is employed to train the enhanced neural network using reinforcement learning, allowing the network to learn and solve adaptive Markov decision processes directly. To evaluate the effectiveness of this solution, the study conducted a numerical experiment using real-time data from Tesla electric vehicles. Combined with queuing models, the EMANN yielded impressive results, including a peak savings of 19.05% and an average profit of $376.1 per kilowatt-hour, achieved within a network convergence time of 512 s. The proposed algorithm for scheduling electric vehicles has been proven to be efficient in addressing the complexities associated with managing electric vehicle charging stations.

An Empirical Study of Financial Efficiency and Stability of Crab–Shrimp Farming Model In Mekong Delta, Vietnam

The study is conducted with the goal of analyzing the financial efficiency of 308 crab - shrimp farming households in the Mekong Delta, finding factors that affect financial efficiency and the extent of their influence on farm household income. Research results show that extensive crab-shrimp farming households are highly effective. With the average cost of investment in production during the crab farming process of 2,051 thousand VND/1,000m2, the average revenue brought from farming Crab farming of 5,099 thousand VND/1,000m2, farmers have an average profit of about 3,020 thousand VND/1,000m2. The average value of the financial ratios shows that the production model in the Mekong Delta is financially effective, the average value of the revenue/cash cost is 8.08, profit/total costs is 2.31, net income/cash costs is 7.08, and profit /revenue is 0.37. Factors affecting the profits of extensive mud crab farmers consist of three influencing factors: Area, crab/total crab ratio, and farmers in Kien Giang Province. Additionally, the number of years of farming experience has an influence, but its impact is not statistically significant

Pengaruh Capital Adequacy Ratio, Non Perfoming Loan, Loan To Deposit Ratio dan Biaya Operasional Pendapatan Operasional Terhadap Profitabilitas

In 2019-2023, the average profitability in the banking sub-sector has increased and decreased. This study aims to analyse the Capital Adequacy Ratio, Non Performing Loan, Loan to Deposit Ratio and Operating Cost of Operating Income on Profitability. The research methodology used in this study uses quantitative methods. Data obtained from the Indonesia Stock Exchange website (www.idx.co.id) and www.bi.go.id. The population in this study are banking subsector companies listed on the Indonesia Stock Exchange in 2019-2023. The sampling technique used purposive sampling technique with a sample of 22 (twentytwo) companies. Data analysis for hypothesis testing using Multiple Linear Regression with the help of the Statistical Package for Social Sciences (SPSS) version 22 (twenty-two) programme. The results showed that Capital Adequacy Ratio and Loan to Deposit Ratio had no effect on Profitability, while Non Performing Loan and Operating Cost of Operating Income had a negative effect on Profitability.