Profit Forecasts Research Articles

Социально-этический маркетинг как инструмент реализации концепции устойчивого развития социально ответственной организации

The issues of using the concept of sustainable development in the management practice of modern enterprises and organizations focused on development and permanent strategic growth are updated. The indicators of sustainable development of an economic entity in the trinity of economic, social and environmental indicators are presented. It is determined that the concept of socio-ethical marketing implemented by the organization as a tool of a socially responsible organization maximally contributes to achieving the goals of a progressive modern concept. The analysis of the tools of social and ethical marketing of the EFKO Group of Companies (EFKO Group) of the Belgorod region, one of the largest food companies in the Russian Federation, a leader in the fat and oil industry market, guided in its activities by the principles inherent in the concept of sustainable development. The dynamics of the main indicators of the Group's economic activity against the background of permanent investments in the implementation of social and ethical activities is presented. The correlation of the value of social investments (expenses for social and ethical marketing) and the value of sales (revenue) of EFKO Group indicates a close relationship between the analyzed indicators. The profit forecast using the exponential smoothing method against the background of growing social investments and social and ethical activity of EFKO Group proves the expediency (including economic) of implementing social and ethical marketing and defines this toolkit as contributing to the growth of the organization's competitiveness not only through active marketing activities, but also through the formation and maintaining a positive image and business reputation of the company; strengthening the image of a socially oriented employer, which is confirmed by the results of independent comparative studies and assessment of the investment and credit attractiveness of companies in the market.

Local party committee and labor cost asymmetry

AbstractChinese state-owned enterprises (SOEs) operate under seemingly incompatible logics of economic and noneconomic objectives and thus within a setting of institutional complexity. This complexity manifests in the coexistence of the modern enterprise system and a firm-level Local Party Committee (LPC). LPCs check proposals from a political standpoint before executives decide on economic feasibility. We provide evidence that SOEs with more LPC participation exhibit greater labor stickiness, and this association is more pronounced after the enactment of the Labor Contract Law in 2008, when LPCs became more focused on noneconomic objectives. Additional analyses reveal that regional differences in market-based institutional quality moderate the association between LPC participation and labor cost asymmetry. Our results are relevant for emerging market investors who attempt to incorporate cost asymmetry into their profitability forecasts and for other stakeholders who strive to understand and anticipate the impact of the complex Chinese institutional setting on cost behavior.

Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis

IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective.

Research on Property Insurance Profit Forecasting Model and Response Strategy under Extreme Weather

Research on Property Insurance Profit Forecasting Model and Response Strategy under Extreme Weather

Seeding: Back to the Basics

AbstractWith weak profitability forecasts for soybeans and—especially—corn in 2024, crop advisers and producers might do well to review seeding basics. The following is a survey of CCAs and university specialists about seeding rates, depth, and dates across the Corn Belt. Earn 1 CEU in Crop Management by reading this article and taking the quiz at https://web.sciencesocieties.org/Learning‐Center/Courses.

Оцінювання тенденцій фінансового результату збутової діяльності виробничого підприємства

The article discusses the main principles of forming the financial result of the sales activity of the business entity. At the same time, the main attention is focused on the evaluation of the trends of changes in the gross profit of the manufacturing enterprise. The main factors influencing its change under martial law are outlined. It has been proven that timely monitoring of the results of sales activity requires the use of economic and mathematical methods and models for estimating gross profit. The means of econometric modeling provide for the selection of the most significant factors of its change, assessment of the density and closeness of the connection, the share of influence of the selected factors on the performance indicator, modeling and further forecasting of the gross profit. The trends of changes in the result of the sales activity of the manufacturing enterprise over the last fifteen years were analyzed. The main problems have been identified and the ways to overcome problematic areas of sales activities have been outlined. It is substantiated that in order to overcome the mentioned troubles, it is necessary to apply the correlation-regression analysis of the gross profit of the manufacturing enterprise. At the same time, the most influential factors of its change were chosen: the volume of sales of the main type of products, the cost of goods and retained earnings (uncovered loss). The result of the conducted research is the construction of multifactorial econometric models of the formation of the gross profit of the manufacturing enterprise. It was established that all these models are adequate according to the coefficient of determination and Fisher's test. Therefore, gross profit forecasting for the next two years was carried out based on them. The forecasting process was carried out on the basis of the assumption of a one percent change in the selected factors and on the basis of the trends existing at the production enterprise. The forecasting results are of a recommendatory nature and can be applied in accordance with changing business conditions. Forecasting changes in factors, using the TREND function, leads to a rapid increase in gross profit. Applying an expert assessment of changes in factors proves a more optimistic prediction of the size of the gross profit of the manufacturing enterprise. The work offers several possible options for changing the result of sales activities in accordance with the dynamics of changes in the selected factors. The relevance of the provided information will allow not only to evaluate the existing trends regarding changes in gross profit, but also to choose the best result depending on the dynamism of the internal and external environment of the production enterprise.

Institutional pressure and analyst behavior: Evidence from the financial sector

AbstractThis study examines the impact of employment background on the objectivity of profitability forecasts in the financial sector. We find that investment bank analysts provide relatively biased recommendations and less accurate forecasts than their counterparts at independent research firms (IRFs). The performance discrepancy is greater for bulge bracket banks and for firms involved in syndication with the analysts' employers. A closer look at analysts transitioning between IRFs and investment banks reveals that these biases stem from their affiliation with investment banks rather than personal bias. Our findings emphasize the critical influence of the work environment on analyst behavior.

Financing and Investing in Women-led Businesses: Understanding Strategic Profits and Entrepreneurial Expectations by Analysing the Factors that Determine Their Company Success

This paper examines the strategic profits and entrepreneurial expectations that accompany financing and investing in women-led businesses in Kosovo, India, and United Kingdom. Its main goal was to observe what the strategic profits and entrepreneurial expectations were for these businesses, based on consideration of three factors: Factor 1 (Strategic profits and entrepreneurial expectations), Factor 2 (Financing and investing expectations), and Factor 3 (Strategic profits). Were these factors associated with one another when making financing and investment decisions, and which variables should be considered more carefully by businesses to improve performance, survive as a business and stay ahead of competitors, meet stakeholder expectations and achieve strategic profit forecasts? Through data processing (SPSS program for Windows 16) using tests and econometric analysis (descriptive, factorial, reliability, and multiple regression) the model shows that all three factors play a significant role in determining strategic profits and entrepreneurial expectations. However, it is suggested that if staff cannot quickly adapt to changes in the environment, and the managerial skills and correct leadership are not in place to execute ideas that increase profit, ensure the regular repayment of debts and loans, deploy innovative strategies, maintain a company's legal status and evaluate the work of employees, then businesses will struggle to increase their strategic profit and guarantee their market survival when compared to their competitors.

Financial Analysis and Valuation on Tesla, Inc.

Tesla, the frontrunner in the new energy industry for the past decade, operates primarily in clean energy sectors such as electric vehicles and solar energy storage batteries. However, as the new energy market expands, Tesla faces increased competition and industry challenges. This paper analyzes Tesla's current competitive position by examining three key aspects: im-portant accounting policies, financial indicator analysis, and future profitability forecasts, focusing on the company's financial performance in 2022. The findings reveal that Tesla's competitive advantage lies primarily in its significant market share in the global new energy vehicle market. To sustain its exceptional financial performance, Tesla should promptly in-troduce new products to meet growing demand. This study emphasizes the need for Tesla to refine accounting policies to ensure transparency and compliance. Additionally, a careful analysis of financial indicators will enable the company to identify areas for improvement and make informed strategic decisions. Looking ahead, the paper underscores the im-portance of Tesla's future profitability, urging the company to capitalize on market trends and diversify its product offerings. In conclusion, this paper provides a comprehensive eval-uation of Tesla's competitive ability in the new energy industry. To maintain its leadership position, Tesla must adapt to the evolving market dynamics by refining accounting practices, monitoring financial indicators, and introducing new products to meet consumer demand.

Profit Forecast Accuracy of Time Series Model - A Case Study of Associated British Foods

Accurate earnings per share (EPS) forecasting is crucial for financial decision-making. This study explores the potential of improving EPS forecasting accuracy by integrating economic lead indicators into time-series models. By incorporating macroeconomic factors like GDP growth and interest rates, the models capture the influence of the broader economic environment on a company’s financial performance. Results demonstrate that including economic lead indicators significantly enhances EPS predictability beyond traditional time-series models. This integration offers a forward-looking perspective, comprehensive analysis, and context to the forecasting process, enabling stakeholders to make more informed investment decisions and develop better strategies. Further research can investigate additional lead indicators, assess their impact in different industries, and develop hybrid forecasting models for refined EPS predictions.

Analysis of the Effect of Break Even Points and Margins of Safety on Profit Planning in Hospitality Companies Listed on the IDX 2017-2021

Top management requires a plan to maximise revenues in order to succeed in a competitive business. The study's goal is to assess and evaluate the impact of the break-even point and the Margin of Safety on revenue and profit forecasts for hotel sub-sector IDX companies for the period of 2017 to 2021. Using the IDX and the company's own website, the researchers documented financial data gathered by researchers and given by firms operating in the hotel market. We performed multiple linear regression analyses with SPSS. Only two of the investigated hospitality-related businesses are found to be profitable (or at least break even), with the biggest safety margins. Profitability is heavily influenced by the break-even point as well as the margin of safety. The Break-even Point and the Margin of Safety have a very favourable effect for hotels listed on the IDX between 2017 and 2021.

Analysis of the relationship between temporal and behavioral aspects of the analyst's forecasting accuracy

The study analyzed the relationship between optimism, anchoring, overconfidence, representativeness, realism, commonality and time, with the accuracy in the profit forecast of analysts. Publicly traded Brazilian companies were considered in 2019, and correlation tests, mean differences and multiple regression analyses were applied. The results indicated that accuracy is negatively influenced by optimism and positively by anchoring and commonality. In addition, the uncertainty present in the distance between the forecast issued and the disclosure of earnings per share also negatively influences the accuracy of analysts. Additionally, it was found that fair value, profitability, issuing ADRs and self-regulated sector, are aspects related to greater accuracy. Thus, the research contributes to the literature by linking behavioral and temporal aspects to financial ones, as well as by signaling the importance of analysts' forecasting models to consider behavioral aspects in their information technologies.

Innovation in Company Profit Management: Socialization of Numerical Calculation for Efficiency Improvement at PT Ajisaka

This community service project aimed to enhance the efficiency and effectiveness of PT. AJISAKA's business management through the introduction of a sophisticated software tool for forecasting and profitability estimation. Blending management science with computational science, the project sought to facilitate informed decision-making by leveraging artificial intelligence in profitability ratio prediction. The methods employed involved the execution of Tri Dharma Perguruan Tinggi and extensive socialization of numerical calculation techniques related to the company's profit estimation. The project was well received by the company's management, with the software's potential to enhance business insight and streamline operations being widely appreciated. The result of this endeavor underscores the potential of AI in revolutionizing profitability forecasts and business management. Furthermore, it bolsters the case for technological self-sufficiency in the economic and business sectors, paving the way for future innovation and the broad adoption of artificial intelligence in business management.
 Highlights:
 
 Utilization of artificial intelligence for accurate profitability forecasting and enhanced business decision-making.
 Successful execution of Tri Dharma Perguruan Tinggi, promoting technological independence in the economic sector.
 Broad appreciation and potential for AI application in streamlining operations and fostering business insight.
 
 Keywords: Artificial Intelligence, Profitability Forecasting, Business Management, Technological Self-sufficiency, Numerical Calculation.
 

RANDOM FOREST MODEL ON BIG DATA FOR FORECASTING THE STABILITY OF THE BANKING SYSTEM OF THE RUSSIAN FEDERATION

The article analyzes current trends in the use of artificial intelligence, cognitive technologies, Big Data and other innovations in order to predict the stability of the Russian banking system. The current trends in the development of banking innovations in the context of digitalization of the economy are summarized. Among the research methods used, the Random Forest artificial intelligence system, formed in the Google Collab cloud service in Python, should be noted. A neural network model for predicting the dynamics of bank profits based on the use of the Random Forest (RF – random forest) machine learning model is proposed. The results of the study indicate that, with the average value of the profit of banks for the period 2010-2021. 1126.0416 billion rubles, the mean absolute error (Mean Absolute Error) amounted to 129.96666 billion rubles, or 11.5%, which can be regarded as a good result in conditions of market uncertainty. The analysis showed that current trends are global in nature and lead to qualitative changes in the flow of business processes between consumers of banking services and credit institutions.
 Purpose: is to form a model that predicts the value of the target variable – the profit of the banking system using the Random Forest deep learning model, which is an ensemble of “decision trees”. The scientific novelty is to put forward and prove the hypothesis that with the help of the formed artificial intelligence system “Random Forest”, a profit forecast for commercial banks of the Russian Federation can be obtained.
 Method or methodology of the work. The work used such research methods as: monographic, analytical, artificial intelligence system – “Random Forest”, as well as comparative analysis. Calculations were performed in XL tables, Google Collab cloud service in Python.
 Results. The developed AI-system “Random Forest”, designed to predict the profits of banks, is presented. It is of practical importance to use the results of the Random Forest neural network to predict the development of credit institutions, which makes it possible to solve a major national economic problem - ensuring the stability of the banking system of the Russian Federation. A neural network model for predicting the dynamics of bank profits based on the use of the Random Forest (RF) machine learning model is proposed, which is a machine learning algorithm that consists in using an ensemble (set) of decision trees.
 The scope of the results: the financial sector, the banking sector, forecasting and ensuring the sustainable activity of credit institutions in the Russian Federation.

The Forecasting Analysis of Profit on Astra Companies List on Indonesia Stock Exchange (IDX)

This research aims to analyze the profit forecasting using the weighted moving average method then compare the pattern between Astra companies listed on IDX (Indonesia Stock Exchange). The research method use is quantitative descriptive with secondary data of profit in the 2018-2022 period that provides quarterly. The result for this research shows the highest forecasting of profit on PT Astra International Tbk occurring in the third quarter in 2023 with the amount of 20,970. While the lowest forecasting occurred in first quarter in 2023 with the amount of 9,410. While the lowest forecasting occurred in first quarter in 2023 with the amount of 13,529. The highest forecasting of profit on PT United Tractors Tbk occurs in the third quarter in 2023 with the amount of 13,738,446. While the lowest forecasting occurred in first quarter in 2024 with the amount of 5,584,042. and the highest forecasting of profit on PT United Tractors Tbk occurs in the second quarter in 2023 with the amount of 50,707. While the lowest forecasting occurred in the third quarter in 2024 with the amount of 22,496.

ANALISIS PERAMALAN PENJUALAN DAN KEUNTUNGAN SAYURAN BUNCIS (Studi Kasus Di Pt. Tiga Bintang Sukses Jatiasih Bekasi Jawa Barat)

PT. Tiga Bintang Sukses (PT. TBS) as the holder of the Goedang Mayoer trademark is a company that strives in the field of providing the best fresh vegetables and fruits, by promoting the concept of farm to table or from farmers directly to consumers. One of the vegetables sold is chickpeas. Beans are one of the highest selling vegetables in the company but fluctuate due to uncertain demand from consumers, especially hotels, restaurants, cafes, and offices. In meeting consumer demand, the company cooperates with farmers. The purpose of this study is to find the best forecasting technique for vegetable sales in the company, to make a sales forecast level and to make an average profit from the sales forecast for the next six months for green beans at PT. Three Stars of Success. This study uses a quantitative approach. The method used in this research is a descriptive analysis and case study. Data collection techniques used using primary data and secondary data. Primary data is related to the company and uses a questionnaire. Secondary data comes from the company's internal data, namely sales of green beans from September 2021 to February 2022. The best forecasting technique for green beans is the multiplicative decomposition technique. The results of the level of sales forecasting show that the highest level of sales for green beans is 500.61 kg. The profit forecast for the next 6 months, has an average profit for green beans, which is Rp. 1,596,232.

Profit Forecasting of Automotive and Component Sub Sector Companies using Monte Carlo Simulation

Profit Forecasting of Automotive and Component Sub Sector Companies using Monte Carlo Simulation

IMPRESSION MANAGEMENT THROUGH THE ASSERTIVENESS OF FINANCIAL ANALYSTS’ FORECASTS

Voluntary disclosure of financial accounting information aims to reduce the effects of agency relationship and informational asymmetry. Due to its non-mandatory character, such disclosure is subject to moral hazard, and may become a biased report of management performance. The Management Reports are presented as a space used by managers for voluntary disclosures, instructing the reading of the mandatory statements which they accompany. In this way, the reports become spaces where Impression Management can occur. Thus, the aim of this study was to analyze the relationship of Print Management in management communication with the quality of profit forecasts made by financial analysts of publicly-held companies enrolled in the Associação Brasileira das Companhias Abertas (ABRASCA) Award from 2016 to 2019 and that presented a message to shareholders in their Management Reports. Messages to shareholders were submitted to content analysis to determine the positive and/or negative character of the message phrases. The results obtained contribute to formulators and users of information by indicating that the assertiveness of analysts' forecasts for one year impacts voluntary disclosure in the message from management to shareholders for the following year. Thus, the manager's decision on "what" and "how" to report also takes into account factors external to the companies, using external sources to, in addition to reducing agency problems and informational asymmetry, practice Impression Management, making it clearer the information provided so that the market has an adequate perspective of future gains and losses.

Research on the factors affecting the earning per share (EPS) forecast accuracy

Securities analysts play a vital role in alleviating the asymmetric information in the capital market, through collecting, processing and transmitting information via their expertise. As profit forecasting is gradually beneficial for investors to make decision, increasing number of researchers concentrate on profit forecasting to find out the factors affecting the earning per share (EPS) forecast accuracy. In common, there are three main aspects to influence the EPS forecast accuracy: personal ability of analysts, difficulty of the task and broker size. Since there are still lots of controversies on the influence of broker size on forecast error, this essay will focus on examining broke size by univariate analysis and multivariate analysis with the sample of U.S. automobile industry from 2001 to 2009. This essay hypothesizes that analysts employed by large brokers produce more accurate earnings forecasts than analysts employed by smaller brokers. The results of my article can support my hypothesis.

MODELING THE PROFITABILITY OF TELECOMMUNICATION SUB-SECTOR STOCKS USING PANEL DATA REGRESSION ANALYSIS

The Covid-19 outbreak in Indonesia has wreaked havoc on the financial market, causing turbulence in the stock market. One industry that did not endure instability was the telecommunications subsector due to government initiatives connected to online learning and office tasks performed on a Work From Home (WFH) basis, which increased internet usage during online learning and WFH. Online learning and office activities carried out on a Work From Home (WFH) basis have increased the sector's profitability and breathed new life into the world of stock investment. When purchasing stocks, an investor always seeks to generate a profit. The profitability ratio reveals the profitability of the business. Profitability and investment decisions are linked as a result of a budget plan and profit forecast. This study aims to model telecommunications subsector companies' profitability on the Indonesian stock exchange using panel data regression analysis. The panel data regression analysis mixes cross-section data and time series data in which the same cross-section unit is sampled at various times. Therefore, investors must be aware of the profitability of the subsector of the telecommunications industry to make an investment decision. This research will generate a model of the profitability of subsector enterprises in the telecommunications industry. Based on the R square value of 0.8752, the results of this study indicate that ln equity, inflation, CR, and DER can explain 87.52% of the variation in ROA. In comparison, the remaining 12.48% is impacted by factors not included in the model.