Revenue Forecasts Research Articles

Exploring Key Drivers of Sales Revenues Forecasting: Empirical Evidence from the Aluminium Industry

In the fast-paced and fiercely competitive banking industry and increasing demand for high forecasting accuracy for effective lending decision making, mastering the art of sales revenue forecasting is crucial for banking professionals who are concerned with efficient credit assessment and investment evaluation. This study delves deep into the main independent variables that impact sales revenue forecasting, moving beyond the widely used historical sales lagging approach. Through a comprehensive qualitative analysis utilizing semi-structured in-depth interviews technique with industry experts whereby study model is developed and subsequently validated using Multiple Linear Regression (MLR) analysis. Spanning a period from 2013 to 2022, this empirical research leverages secondary data from sample of 12 selected companies who manufacture primary aluminiumfrom different economics, accordingly with related macro and micro economic figures on quarterly basis. The findings revealed that interviews claimed six independent variables have significant impact on sales revenues forecasting including (Historical Trading metal price at the metal exchange, company strategy) while only four variables were (quantities sold, historical market selling prices, and historical producer price index and historical foreign exchange rates) have significant impact on sales revenue forecasting, empowering banking credit and investment teams with the critical knowledge necessary to make informed lending and investment decisions

The Potential and Distributional Effects of CBAM Revenues as a New EU Own Resource

Abstract Carbon Border Adjustment Mechanism (CBAM) revenues are increasingly being discussed as a potential new EU own resource. The aim of this paper is to quantify the revenue potential of the CBAM for the EU budget until 2035. Furthermore, this paper examines the distributional implications of CBAM revenues for Member States. It proposes a methodology to quantify the revenue potential of CBAM and calculates a number of revenue scenarios. The distribution of CBAM revenue among all Member States is compared with the distribution of the GNI-based resource in order to illustrate the distributional effects. According to these results, the annual revenue potential of the CBAM is of the order of €1 billion in the first years after its introduction, rising to around €14 billion by 2035. The distributional effects compared to GNI proportionality are small. The scenario analysis shows the sensitivity of the CBAM revenue forecasts, as they depend crucially on various parameters.

From Hindsight to Foresight: Understanding and Overcoming Bias in Financial Forecasts

Abstract This study conducts a comprehensive benchmarking analysis on analyst forecasts of revenue and EBITDA for exchange-traded public companies in the U.S. spanning the period from 2009 to 2022. Our findings reveal a consistent and statistically significant upward bias present in both revenue and EBITDA forecasts, on average, as evidenced by various measures of forecast error. This bias is explored at both market sector and market-wide levels of aggregation to check for macro-consistency. Recognizing the significance of these biases, we propose bias-adjustment frameworks aimed at enhancing the accuracy of future analyst forecasts pertaining to revenue and EBITDA. The implications of our study underscore the importance of mitigating such biases for improved forecasting precision in financial markets.

Assessment of the quality of forecasts of seasonal fluctuations of budget revenues as part of effective management of public finances in Ukraine

Seasonal fluctuations in revenues to the budgets of Ukraine at different levels can significantly affect the fulfillment of financial obligations of the state and territorial communities, especially in conditions of martial law. The accuracy of forecasting such fluctuations becomes critically important for the formation of a balanced budget policy and ensuring timely financing of priority state programs. The purpose of the article is to assess the quality of forecasts of seasonal fluctuations in budget revenues of different levels in Ukraine during martial law. The research is aimed at increasing the efficiency of financial management in the crisis, which, in turn, will contribute to strengthening the economic security and stability of Ukraine. It is a continuation of the authors' previous research. Within the framework of the study, the actual indicators of receipts of the largest budget-forming taxes to the state budget of the Volyn region and to the local budgets of the Volyn region for January–May 2024 were analyzed and compared with previously obtained forecast data. To assess the quality of forecasts, a scale was used, according to which the accuracy of forecasts is ranked depending on the value of the mean absolute percentage error (MAPE) of the forecast as follows: high (<=10%), good (10.1–20%), satisfactory (20.1–40%), poor (40.1–50%), unsatisfactory (>50%). It was found that during the analyzed period, the accuracy of forecasts of seasonal revenues to the state budget in the Volyn region for VAT from goods imported into the customs territory of Ukraine was good (the MAPE of forecasts was 18.8%); for customs duties on goods imported by business entities – satisfactory (28.2%); for personal income tax paid by tax agents from the taxpayer's income in the form of wages – high (6.4%); for excise tax on vehicles imported into the customs territory of Ukraine – satisfactory (39.5%). It was determined that the accuracy of forecasts of seasonal revenues to the local budgets of the Volyn region for personal income tax paid by tax agents from the taxpayer's income in the form of wages was high (the MAPE of forecasts was 6.4%); for the single tax on individuals – satisfactory (25.0%); for land tax on legal entities – satisfactory (39.7%); for rent from legal entities – high (3.8%).

Approaches to Modelling Marketing Strategies in E-Commerce

Introduction. Increasing the efficiency of business activities requires developing a marketing strategy using e-commerce to attract a wide range of customers and maximise the company's income. The key marketing metric for most companies is the customer lifetime value indicator, which is why studying approaches to forecasting this indicator in e-commerce requires special attention, considering the changing business operation environment. Aim and tasks. The study aims to develop approaches to modelling the marketing strategy of e-commerce enterprises. The tasks are a comparative characterisation of modern marketing planning tools and an empirical analysis of the possibilities of using the BG/NBD model for marketing planning in e-commerce companies, considering the need to implement scenario marketing planning in unstable conditions. Results. A comparison of the characteristics of the main marketing planning tools was conducted according to features such as content and focus, functional purpose, and planning period, which allows more purposeful use of these tools to solve specific tasks. In order to automatically obtain the required data format for modelling, a Visual Basic program was developed, a forecast of customer behaviour was built, and an indicator of customer transactions was determined for further calculation of customer lifetime value (CLV). During the simulation, four parameters of the BG/NBD model were evaluated, and the maximum value of the logarithmic likelihood function equal to 14460.54 was obtained. The expected number of transactions (forecast time = 26 weeks) was determined for each client from the database. After calculations the information was received, the selected client will make 2.35 transactions during the forecast period of 55-80 weeks. The recommended algorithm for using the BG/NBD model as part of a scenario approach for forecasting the cost of the customer's life cycle allows for more flexible forecasting of digital business revenues and building an optimal marketing strategy. Conclusions. An empirical study of the possibilities of using the BG/NBD model for marketing planning in e-commerce companies showed that, for the simulation of indicators that affect a company's marketing strategy, it is crucial at the initial stage to convert the database of customer purchases into the input format required for modelling. Using a scenario approach allows e-commerce enterprises to carry out variable marketing planning and reduce risks under growing uncertainty. A well-designed marketing strategy that can be adapted to different business development scenarios enables an e-commerce company to increase its sales.

New methods of investment prioritization in the development of cellular networks in Russia

Subject. The article presents a new enhanced method of prioritizing investments in the construction of mobile networks to increase the effectiveness of management decisions. Objectives. The study aims to develop an improved methodology for prioritizing investments in the construction of cellular networks in Russia, taking into account market dynamics. Methods. I apply a comprehensive approach to the exploration of investment prioritization methods, general scientific and special methods of scientific knowledge, like system and functional-structural analysis, observation, classification, analysis of the best practices, synthesis and generalization of existing scientific and practical data. Results. The article offers a new method of investment prioritization based on more accurate forecasting of incremental revenues, considering the impact of the time lag between the change in market share by base stations and the change in market share by revenue. Conclusions. The proposed method relies on the assessment of incremental revenues, considering the impact of the time lag on increasing market share by revenue with an increase in market share for base stations. This factor directly affects the payback period of investments and allows more accurate ranking of regional branches of telecommunications operators, when deciding on investments in network construction for various subjects of the Russian Federation.

REVENUE FORECASTING SCENARIOS FOR INTERNATIONAL HOTEL CHAINS

The purpose of the study is to develop a system of balanced scorecards for forecasting the income of hotels belonging to international chains. The subject of the study is the system processes of evaluating the effectiveness of the management analysis system, which are determined on the basis of calculating key performance indicators, and as a result of the integral indicator through the analysis and aggregation of individual functional criteria based on comparison with the reference values of the revenue management model for pricing, load planning and its redistribution between revenue centres. Methodology. The study uses the methods of theoretical and logical generalisation. The article describes a set of strategies and tactics used by international hotel chains to manage the demand for hotel services. The results of the article are to form a model for conducting a detailed operational and financial analysis of the hotel enterprise by revenue centres, which contributes to the development of a strategy. The authors have selected a set of indicators that are used to ensure a balanced approach to measuring performance through the indicator method and visual representation in a graphical representation. The paper analyses the performance of international hotel chains over twelve years in order to assess the impact of key factors on their revenues and develop forecasts; the estimated indicators were classified into seven groups: assessment of the average daily revenue per room, room cost, occupancy rate, market share, staff productivity, resource intensity and digitalisation costs. The publication uses the example of international hotel chains to clarify the content and importance of revenue forecasting in the revenue management system.

A Soft Voting Ensemble Model for Hotel Revenue Prediction

In recent years, the hotel industry has faced unprecedented opportunities and challenges due to the increasing demand for travel and business trips. This growth not only presents significant opportunities but also brings challenges to resource management and price setting. Accurate hotel revenue prediction is crucial for the hotel industry as it influences pricing strategies and resource allocation. However, traditional hotel revenue prediction models fail to capture the diversity and complexity of hotel revenue data, resulting in inefficient and inaccurate predictions. Then, with the development of the ensemble learning, its application to hotel revenue prediction has emerged as an influential research direction. This study proposes a soft voting ensemble model for hotel revenue prediction, which includes six base models: Convolutional Neural Network, K-nearest Neighbors, Linear Regression, Long Short-term Memory, Multi-layer Perceptron, and Recurrent Neural Network. Firstly, the hyper-parameters of the base models are optimized with Bayesian optimization. Subsequently, a soft voting ensemble method is used to aggregate the predictions of each base model. Finally, experimental results on the hotel revenue dataset demonstrate that the soft voting ensemble model outperforms base models across six key performance metrics, providing hotel managers with more accurate revenue prediction tools to aid in scientific management decisions and resource allocation strategies. This study confirms the effectiveness of the soft voting ensemble model in enhancing the accuracy of hotel revenue forecasts, demonstrating its significant potential for application in strategic planning within the modern hotel industry.

Effects of process parameters on biogas production from food waste using aspen plus simulator

This study explores the potential of converting food waste (FW) in South Africa to biogas via anaerobic digestion (AD) using the Aspen Plus Simulator version 11. The simulation modeled the AD process, focusing on hydrolysis, acidogenesis, acetogenesis, and methanogenesis stages, with unconverted substrate recycling at the acetogenesis stage, employing a Non-Random Two-Liquid (NRTL) model for the polar mixture components. The FW used contained 49.86 % volatile solids (VS) of carbohydrates, 39.31 % VS of proteins, and 10.83 % VS of lipids. The results revealed that biogas yield at mesophilic temperature (37 °C) was 50 % higher than at thermophilic temperature (55 °C) with a residence time of 35 days, and methane composition was 45.98 % higher. Methane and carbon dioxide percentage compositions increased with hydraulic retention time (HRT), varying between 37.5–45.5 % and 34.3–39.9 % at mesophilic conditions. Economic analysis indicated a profitable outlook, with a payout period of 3 years, 5 months, and a 15-year positive revenue forecast. The project exhibited an internal rate of return (IRR) of 59.6629 %, suggesting strong economic viability and potential for investment. This study underscores the potential for optimized AD processes to enhance biogas production from FW in South Africa, presenting a viable, eco-friendly waste management solution.

Do accounting earnings provide useful information for state tax revenue forecasts?

Do accounting earnings provide useful information for state tax revenue forecasts?

KNN model based financial revenue forecast of Guangzhou

KNN model based financial revenue forecast of Guangzhou

The effect of ASC 606 adoption on value relevance of revenues: Early evidence

This study examines the effect of adopting the principle-based revenue recognition standard, i.e., ASC 606, on the value relevance of reported revenues. We hand-collect data on the cumulative effects of ASC 606 adoption on retained earnings to identify materially impacted firms. We find that, compared to firms not materially impacted by ASC 606 adoption, the value relevance of reported revenues has improved for firms materially impacted based on long-window tests, while the informativeness of reported revenues has not improved for firms materially impacted based on short-window tests around quarterly earnings announcements. We also find that analysts' revenue forecast revisions have increased incrementally for firms materially impacted by ASC 606 adoption. Overall, our results suggest that ASC 606 adoption has generally increased the value relevance and informativeness of reported revenues.

Применение байесовского подхода к прогнозированию налоговых поступлений на примере Республики Армения

This paper is devoted to the application of the Bayesian approach to the forecasting of aggregate taxes on the example of the Republic of Armenia. Typically, this approach is used in large-scale BVARs to forecast macroeconomic variables. The objective of this study is to estimate the efficiency of the Bayesian approach to constricting relatively low-scale fiscal VARs. Another objective is to build a specific BVAR model for forecasting tax revenues in the context of actual forecasting rounds. The study is based on seasonally adjusted quarterly aggregate tax data and the corresponding proxy bases. A hierarchical approach to the selection of BVAR’s priors is implemented. It assumes the random nature of variances in the prior values of the coefficients. The hierarchical approach is also characterized by a high level of variability of hyperparameters. To determine the optimal structure of the BVAR model in terms of out-of-sample prediction accuracy, a special algorithm was developed. This algorithm involves a specific procedure for the selection of priors and model parameters, which allows to significantly minimize the prediction error. The Geweke and Gelman-Rubin tests were used/considered to check the convergence of the parameters, and the acceptance rate of the Metropolis-Hastings algorithm was taken into account. It Additional priors, such as the sum-of-coefficients prior and the dummy-initialobservation prior (single-unit-root), are shown to improve the quality of out-of-sample forecasts. These priors allow for the possibility of the existence of a single root and cointegration between variables. The main finding of this study is that the proposed algorithm for selecting parameters in BVAR significantly improves out-of-sample performance compared to traditional frequency VAR.

AI-powered growth hacking: benefits, challenges and pathways

Purpose This paper aims to (1) unveil how artificial intelligence (AI) can be implemented in growth-hacking strategies; and (2) identify the challenges and enabling factors associated with AI’s implementation in these strategies.Design/methodology/approach The empirical study is based on two distinct groups of analysis units. Firstly, it involves 11 companies (identified as F1 to F11 in Table 1) that employ growth-hacking principles and use AI to support their decision-making and operations. Secondly, interviews were conducted with four businesses and entrepreneurs providing consultancy services in growth and digital strategies. This approach allowed us to gain a broader view of the phenomenon. Data analysis was performed using the Gioia methodology.Findings The study firstly uncovers the principal benefits and applications of AI in growth hacking, such as enhanced data analysis and user behaviour insights, sales augmentation, traffic and revenue forecasting, campaign development and optimization, and customer service enhancement through chatbots. Secondly, it reveals the challenges and catalysts in AI-driven growth hacking, highlighting the crucial roles of experimentation, creativity and data collection.Originality/value This research represents the inaugural scientific investigation into AI’s role in growth-hacking strategies. It uncovers both the challenges and facilitators of AI implementation in this domain. Practically, it offers detailed insights into the operationalization of AI across various phases and aspects of growth hacking, including product-market fit, user acquisition, virality and retention.

The Impact of Budget Revenue and Expenditure Forecasting Errors on Inflation in Turkey: An Analysis of the 1975-2021 Period

The Impact of Budget Revenue and Expenditure Forecasting Errors on Inflation in Turkey: An Analysis of the 1975-2021 Period

ECONOMETRIC ANALYSIS OF THE GOLD MINING SECTOR IN THE KYRGYZ REPUBLIC: IMPACT ON THE ECONOMY AND RECOMMENDATIONS FOR ENHANCING EFFICIENCY

The article is devoted to the analysis of gold mining sector in the Kyrgyz Republic. Effective management of this sector is extremely important, as gold is one of the key minerals in the country, which makes a significant contribution to the country's economy. Econometric models were used to assess the performance of the gold mining sector. As a result of the analysis, quantitative indicators of the relationship of gold exports on the economy were obtained, and the impact of activities in the Kumtor deposit on the country's economy was studied. Based on the obtained indicators, the analysis and forecast of revenues to the budget of the Kyrgyz Republic were made. Based on the results, recommendations were made to improve the efficiency of the gold mining sector.

Research on the Models for Forecast of Tax Revenue of Wenzhou City

Research on the Models for Forecast of Tax Revenue of Wenzhou City

Analysis of the influence of emotions on the decision-making of entrepreneurs using neurotechnologies

This study examines the influence of emotional factors on entrepreneurial decision-making, specifically focusing on early-stage entrepreneurs and those with more years of experience in the entrepreneurial process. The aim is to understand how emotional intensity affects business decisions at different stages of entrepreneurship. Neuroqualitative interviews were conducted using a stratified sample of entrepreneurs. The methodology included semi-structured interviews. Galvanic Skin Response (GSR) technology was employed to simultaneously measure participants’ emotional levels during the interviews. The results revealed significant differences in emotional levels between novice and experienced entrepreneurs concerning the analysed emotional factors. Novice entrepreneurs exhibited higher levels of anxiety and fear, especially in questions not previously anticipated, while experienced entrepreneurs showed confidence and a more optimistic outlook, particularly on topics related to long-term consolidation and revenue forecasting. This study highlights originality by demonstrating the importance of emotional factors in entrepreneurial decision-making and their variation over time in the entrepreneurial process. The use of GSR technology in neuroqualitative interviews adds a unique dimension by providing an objective assessment of entrepreneurs’ emotional levels, thus complementing the qualitative richness of verbal responses. These findings contribute to a deeper understanding of emotional complexity in business decision-making and emphasize the need to consider these aspects in entrepreneurship support strategies.

MODERN APPROACHES TO FORECASTING CORPORATE INCOME TAX REVENUES IN THE RUSSIAN FEDERATION

The purpose of the study. The purpose of the study is to determine the optimal model for forecasting corporate income tax revenues. The subject of the study is modern methods of forecasting income tax revenues to the budgets of the subjects of the Russian Federation, based on mathematical and statistical, regression and autoregressive models with a moving average. The article identifies the advantages and disadvantages of existing approaches to forecasting tax revenues, as well as defines the conditions for the applicability of the considered models in conditions of high dynamics of changes in market conditions and macroeconomic uncertainty. The results of the study are presented, which make it possible to establish the optimal methodology for forecasting corporate income tax receipts, taking into account the statistical limitations of the planning horizon, as well as to identify a forecasting mechanism available for use in long-term planning. Conclusions. Based on the results of the study, it was concluded that it is advisable to combine tax forecasting models to achieve optimal predictive and analytical indicators, as well as to apply this approach only within the framework of short-term planning. The final forecast indicator can be identified on the basis of expert assessments, the average value of the indicators obtained, or through a combination of these approaches: an expert assessment of the weighting coefficients to calculate the weighted average value of the forecast indicator.

Effects Influence of Social Media Constructs on Shopping: An Empirical Study on the Prediction of Retail Clothing Sales

AbstractIn a market characterized by frequent new product placement and their rapid exit, accurate forecasting of the sales is an important goal to guarantee the profitability and long-term survival of companies. This paper follows this line of research and explores the sales data of an Italian clothing company. The objective of this research is to improve sales forecasts.The data under study were extracted from the corporate datalake of the clothing company analyzed as well as from the company’s official Facebook page using Facebook’s Graph API. The sales forecasting should try to consider all the possible demand influencing factors and also explanatory variables. In this way, the time series perspective is combined with qualitative indicators on the demand side. The approach taken confirms the influence of sales from exogenous variables: there is a delay of 15 days between social communication and the sale of clothing. Social communication, as well as some regular Italian holiday, negatively affects sales. On the other hand, school holidays and some regular Italian holidays have a positive impact on sales. As a result, the advance knowledge of revenue forecasts allows managers to choose suitable marketing strategies.