Search Results (16)

New approaches to improve organisational performance in firms are evolving in this data-driven age. However, there is lack of studies in examining the relationship between revenue, net profit, cash flow per share, and earnings per share. The energy sector remains under-researched regarding the multi-dimensional drivers of profitability. Existing research shows inconclusive evidence with studies predominantly examining revenue—performance relationship limiting to a single factor and not guiding potential investors regarding future earnings per share in the energy industry. This paper aims to bridge the gap in literature by proposing a data-driven approach to analyse the relationships between revenue, net profit, cash flow per share, and earnings per share. We examine these relationships by conducting an empirical analysis using secondary data derived from published annual reports of the energy firms listed on the Australian Securities Exchange (ASX). Our empirical study uses Pearson correlations and regression techniques to test the hypotheses on the relationships between revenue, net profit, cash flow per share, and earnings per share. Also, we use market capitalisation as a control variable and predictor of earnings per share in the energy industry. The data analysis results in four findings: (i) revenue positively influences earnings per share because higher revenue expands the firm’s earnings capacity within the financial performance, (ii) net profit has a strong positive effect on earnings per share, consistent with profitability theory and the direct derivation of EPS from net income, (iii) cash flow per share influences earnings per share because liquidity supports operational stability, investment decisions, and earnings sustainability (e.g., heavy capital expenditure contexts), and (iv) the combined effects of revenue, net profit, and cash flow per share provide a stronger and more holistic prediction of earnings per share than any single variable, consistent with multidimensional organisational performance theory (a more holistic valuation model than looking at single factors). In addition, the results indicate that market capitalisation (control variable) has both strong prediction of earnings per share and strong association with earnings per share. The results of this study can offer practitioners and investors in Australia and other countries for a better understanding of the relationships between revenue, net profit, cash flow per share, and earnings per share from energy companies. The data will help investors to make good investment data-driven decisions in the energy industry or other industries. It also motivates researchers to conduct similar studies in different contexts. We further provide recommendations, including a closed-loop Artificial Intelligence (AI) data-driven approach integrated into energy accounting and operational processes to enhance profitability. This approach operationalises the revenue and earnings-per-share (EPS) strategies identified in our empirical analysis, offering practical value for industry practitioners and guiding future research in this direction. Full article

The construction sector is particularly prone to financial instability, with insolvencies occurring more frequently among micro- and small-scale firms. The current study explores the application of artificial intelligence (AI) and machine learning (ML) models for predicting insolvency within this sector. The research combined a structured literature review with empirical analysis of construction sector-level insolvency data spanning the recent decade. A critical review of studies highlighted a clear shift from traditional statistical methods to AI/ML-driven approaches, with ensemble learning, neural networks, and hybrid learning models demonstrating superior predictive accuracy and robustness. While current predictive models mostly rely on financial ratio-based inputs, this research complements this foundation by introducing additional sector-specific variables. Empirical analysis reveals persistent patterns of distress, with micro- and small-sized construction businesses accounting for approximately 92% to 96% of insolvency cases each year in the Australian construction sector. Key risk signals such as firm size, cash flow risks, governance breaches and capital adequacy issues were translated into practical features that may enhance the predictive sensitivity of the existing models. The study also emphasises the need for digital self-assessment tools to support micro- and small-scale contractors in evaluating their financial health. By transforming predictive insights into accessible, real-time evaluations, such tools can facilitate early interventions and reduce the risk of insolvency among vulnerable construction firms. The current study combines insights from the review of AI/ML insolvency prediction models with sector-specific feature derivation, potentially providing a foundation for future research and practical adaptation in the construction context. Full article

Forecast risk management is central to the financial management process. This study aims to apply Monte Carlo simulation to solve three classic probabilistic paradoxes and discuss their implementation in corporate financial management. The article presents Monte Carlo simulation as an advanced tool for risk management in financial management processes. This method allows for a comprehensive risk analysis of financial forecasts, making it possible to assess potential errors in cash flow forecasts and predict the value of corporate treasury growth under various future scenarios. In the investment decision-making process, Monte Carlo simulation supports the evaluation of the effectiveness of financial projects by calculating the expected net value and identifying the risks associated with investments, allowing more informed decisions to be made in project implementation. The method is used in reducing cash flow volatility, which contributes to lowering the cost of capital and increasing the value of a company. Simulation also enables more accurate liquidity planning, including forecasting cash availability and determining appropriate financial reserves based on probability distributions. Monte Carlo also supports the management of credit and interest rate risk, enabling the simulation of the impact of various economic scenarios on a company’s financial obligations. In the context of strategic planning, the method is an extension of decision tree analysis, where subsequent decisions are made based on the results of earlier ones. Creating probabilistic models based on Monte Carlo simulations makes it possible to take into account random variables and their impact on key financial management indicators, such as free cash flow (FCF). Compared to traditional methods, Monte Carlo simulation offers a more detailed and precise approach to risk analysis and decision-making, providing companies with vital information for financial management under uncertainty. This article emphasizes that the use of Monte Carlo simulation in financial management not only enhances the effectiveness of risk management, but also supports the long-term growth of corporate value. The entire process of financial management is able to move into the future based on predicting future free cash flows discounted at the cost of capital. We used both numerical and analytical methods to solve veridical paradoxes. Veridical paradoxes are a type of paradox in which the result of the analysis is counterintuitive, but turns out to be true after careful examination. This means that although the initial reasoning may lead to a wrong conclusion, a correct mathematical or logical analysis confirms the correctness of the results. An example is Monty Hall’s problem, where the intuitive answer suggests an equal probability of success, while probabilistic analysis shows that changing the decision increases the chances of winning. We used Monte Carlo simulation as the numerical method. The following analytical methods were used: conditional probability, Bayes’ rule and Bayes’ rule with multiple conditions. We solved truth-type paradoxes and discovered why the Monty Hall problem was so widely discussed in the 1990s. We differentiated Monty Hall problems using different numbers of doors and prizes. Full article

This systematic review analyzes and compares the predictive power between traditional financial ratios and cash flow-based ratios in estimating performance. Although traditional ratios of return on assets and debt to equity have received extensive application, cash flow ratios are increasingly valued by their dynamic insights into both liquidity and financial health. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, this review systematically analyzes 21 studies spread across various industries and regions. The results reveal that cash flow ratios usually dominate the traditional metrics during forecasting financial performance, especially in the presence of the use of machine learning models. Among the identified variables of the logistic regression model and gradient boosting model predictors, key indicators are those showing the return on investment, the current ratio, and the debt-to-asset ratio. The bottom line of the findings is that a combination of cash flow and traditional ratios gives a better understanding of a company’s financial stability. These results may serve as a starting point for investors, regulators, and entrepreneurs and may further facilitate informed decisions with a reduced chance of miscalculations that enhance proactive financial planning. In addition, future prediction models should integrate non-financial factors such as governance quality and market conditions to enhance financial health assessments. Additionally, longitudinal studies examining the evolution of financial ratios over time, along with hybrid statistical and machine learning approaches, can improve forecasting accuracy. Integrating cutting-edge analytical tools with the strength of financial metrics gives this study actionable insights that allow stakeholders to understand financial performance in a more nuanced sense. Full article

This paper systematically reviews the behavior of numerical indicators in predicting future bankruptcy of companies through statistical analysis models. Following the PRISMA standard, ten primary studies were included in the review. The obtained results underline (1) the ability of numerical indicators, through simple statistical analysis models, to forecast the bankruptcy of businesses and companies and (2) the reliability of cash flows in predicting financial distress through statistical analysis, and (3) models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country’s economic conditions can potentially impact their predictive accuracy. Full article

As the global shift toward sustainable transportation gains momentum, the integration of electric vehicles (EVs) becomes imperative, necessitating a robust and environmentally friendly charging infrastructure. Leveraging the abundant solar potential in the region, this study examines the technical, economic, and environmental feasibility of deploying photovoltaic electric vehicle charging stations (PV-EVCSs) in Hail City, Saudi Arabia, as a case study. This study examines factors such as the energy demand, grid integration, and user accessibility, aiming to address the challenges and opportunities presented by the urban fabric. The proposed solar charging station network seeks to catalyze a paradigm shift toward a cleaner and more sustainable transportation ecosystem, embodying a forward-thinking approach to meeting the evolving needs of urban mobility in the 21st century. The analysis encompasses many scenarios, encompassing a range of car battery sizes, charger powers, and car slots per station. Zone 4 is identified as the most crucial area, where seven charging stations are needed to fulfill the expected demand in the absence of any private charging alternatives. The economic evaluation of the 1047.35 kWp PV system reveals an estimated conventional payback time of 11.69 years, accompanied by a return on assets of 10.17%. The system generates accumulated cash flows amounting to SR 7,169,294.62 over 30 years, while the estimated operational and maintenance expenses are predicted to be SR 50,000 per year. The overall investment cost for the solar PV and EV charging stations is SR 4,487,982. This cost is offset by the yearly electricity savings from solar and grid sources, which can reach up to SR 396,465.26 by year 30. This work presents a detailed plan for the future of sustainable transport. It combines technical, environmental, and economic aspects to promote a cleaner and more sustainable urban mobility system. Full article

The aim of this study is to examine whether good asset allocation by a CEO leads to superior stock returns and, if so, how one might be able to identify CEOs that are good asset allocators. Employing US data from May 2001 to April 2019, we find that CEOs that invest the company’s cash flows according to a value-investing style seem to outperform companies that do not. We find that high goodwill to assets and high operating margin (good asset allocator) companies outperform companies with high or low goodwill to assets and low operating margin (poor asset allocator) companies. The findings are corroborated with out-of-sample (May 2019–April 2023) robustness tests. When buying other businesses, value investor CEOs ensure that their consolidated operating margins remain high, as opposed to other firms managed by poor asset allocator CEOs who buy businesses that bring down operating margins, either because they overpay or due to an inability to materialize expected synergies. Using both summary statistics and regression analysis, the findings of this study help us identify companies that allocate assets like value investors and enable us to anticipate future stock performance. For example, if a company, on average, has a goodwill/assets ratio of 41.03%, and an operating margin of 21.38%, it is likely this firm would be at the top quartile in terms of stock return performance over at least the next three years. At the same time, if a firm has a low average goodwill/assets ratio (i.e., 1.95%), its operating margins, on average, should be 24.46%, if it wants to achieve a similar performance as that of firms with high goodwill/assets. Moreover, the future stock return predictability of high (low) goodwill/assets and high (low) operating margin firms, found in this study, can help an investor develop trading strategies that can lead to superior stock price performance by effectively taking long positions in (shorting) firms that are (not) managed by value investor CEOs. Finally, the paper’s findings can also help investors in another way. For example, investors tend to be skeptical about companies with high goodwill/assets. The rule of thumb is to beware of companies carrying goodwill on their balance sheets that is more than 25% of assets. Based on our findings, this should not be a problem as long as the company’s operating margin has remained high and is rising. Full article

In this paper, a method was proposed for pricing NPL portfolios, which is currently a crucial point in the portfolio transactions between the banks and NPL servicers. The method was based on a simple mathematical model which simulated the collection process of the NPL portfolios considering the debtors’ behavioral response to various legal measures (phone calls, extrajudicial notices, court orders, and foreclosures). The model considered the recovery distribution over time and was applied successfully to the case of Greece. The model was also used to predict recovery, cost, and profit future cash flows, and to optimize the collection strategies related to the activation periods of different measures. A sensitivity analysis was also conducted to reveal the most significant factors affecting the collection process. Full article

This paper investigates the relative and incremental information content of KPMG’s recently developed metric for shareholder value creation: earnings less risk-free interest charge (ERIC). We assess if ERIC has a better ability to predict stock returns than earnings, cash flow from operations (CFO), earnings before extraordinary items (EBEI), residual income (RI), or economic value added (EVA). We evaluate data from 214 companies listed on the U.S. Standard & Poor’s 500 Index from 2003 to 2012 (2354 firm-year observations). Similar to previous studies, we confirm that CFO and EBEI have the strongest association with stock returns in the short term, while EVA trails behind all other metrics. In terms of new findings, ERIC is the best predictor of stock returns over a 5-year period, as well as during times of crises (from 2009 to 2010). In this period, ERIC also adds incremental information content beyond that of EBEI. However, the low-short-/mid-term predictive ability of shareholder value metrics (EVA, ERIC) raises concerns regarding their reliable use in future research on shareholder value creation. We consequently propose a research agenda that focuses less on the measurement and more on the management of shareholder value. Full article

A fundamental role of financial reporting is to provide information useful in forecasting future cash flows. Applying up-to-date time series modelling techniques, this study provides direct evidence on the usefulness of quarterly data in predicting future operating cash flows. Moreover, we show that the predictive gain from using quarterly data is larger for asset-heavy industries and industries with higher levels of earnings smoothness. This study contributes to the accounting literature by examining the usefulness of quarterly financial statements in predicting the realization of future cash flows. Our results help fill the gap in knowledge on quarterly financial statements and provide new insights on why the frequency of financial reporting matters. In addition, our findings have important policy implications for the ongoing debate over interim reporting requirements in multiple jurisdictions around the world. Full article

What incentives do managers face that might give rise to inefficient investments in leases? If managers make inefficient investments in leases, what economic consequences arise for those managers and their firms? We develop a model of expected investments in leased assets and use the residuals from the model as proxies for inefficient investments. We find that, in contrast to investments in capital expenditures, leasing appears to be a mechanism through which managers can seemingly over-invest, even among firms with high quality financial reporting and negative free cash flows. Examining economic consequences, we predict and find that unexpected investments in leased assets trigger increasing future sales growth but declining future earnings growth for as long as three years ahead. We also find a negative relation with contemporaneous stock returns, suggesting investors view unexpected investments in leases as value destructive. Finally, despite negative returns consequences, we find that unexpected investments in leases are associated with higher CEO compensation driven primarily by future sales growth. Our study suggests that compensation contracts that reward growth may give managers’ incentives to drive sales growth with larger-than-expected investments in leased assets, which lead to slower future earnings growth and negative share price consequences for investors. Our results should inform managers and board members, investors, and researchers interested in investment efficiency, corporate governance, and leases. Full article

This study investigates the future existence of firms that are engaged in environment-oriented activities. Recently, strategic activities for firms’ sustainable growth has been critical for the environment. We use regression analysis to examine the relationship using firms listed in the Korea Stock Exchange market from 2014 to 2018. We use five aspects of environment-oriented activities: organization, management, strategy, performance, and shareholders, provided by the Korea Corporate Governance Service. The empirical results indicate that the firms participating in environment-oriented activities are likely to predict future cash flow, implying firms’ sustainability. We also claim that firms engaged in environment-oriented activities are likely to advertise their pro-environmental engagements, resulting in firms’ sustainable existence in the future. These findings are robust when we use the aggregate value as an alternative measurement. Our finding provides useful information for corporate practice. Active involvement in environmental activities can be used as a strategy that leads to superior performance. These efforts will contribute to enhancing the public image and improving green competitiveness. From the perspective of regulators, the non-financial information assessment supports the government’s eco-friendly policy that emphasizes environment-oriented activities. The results indicate that transparent information for external investors seeking to invest in firms are engaged in environment-oriented activities. Full article

Generating positive long-term cash flow is vital for a firm’s sustainability. In this paper, we consider the earnings in the forecasting of future cash flow from a human resource investment of an internal control system. Using the firms listed in the Korea Stock Exchange market from 2014 to 2018, we find that the current earnings are the components of cash flow forecasting, and this relationship is genuine in a firm equipped with sufficient internal control personnel and their experiences. These findings indicate that earnings are reliable when forecasting future cash flow for a firm with a well-operated foundation. Full article

Reclaimed asphalt pavement (RAP) has received wide application in asphalt pavement construction and maintenance and it has shown cost-effectiveness over virgin hot mix asphalt (HMA). HMA with a high content of reclaimed asphalt (RA) (e.g., 40%) is sometimes used in practice, however, it may have significant adverse effects on the life cycle performance and related costs. In particular, challenges may arise as the life cycle performance of RAP is also affected by local climatic conditions. Thus, it is important to investigate whether it is still economic to use RAP under future local climate, with consideration of life cycle performance. A case study was conducted for various road structures on Interstate 95 (I-95) in New Hampshire (NH), USA for the investigation. The case study utilized dynamic modulus testing results for local virgin HMA and HMA with 40% RA (as major material alternatives) to predict life cycle performance of the selected pavement structures, considering downscaled future climates. Then, a life cycle cost analysis (LCCA) was considered to estimate and compare the life cycle cash flow of the investigated road structures. Responsive maintenance (overlay) and effectiveness were also considered in this study. It was found that using 40% RA in HMA can reduce agency costs by up to approximately 18% under the 2020–2040 predicted climate and NH should consider this practice under predicted future climate to reduce agency costs. Full article

A company’s sustainability is generally determined by whether it is able to create a positive long-term cash flow. This paper investigates whether the predictive ability of cash flows and earnings in forecasting future cash flows differs depending on the foreign investors’ ownership. Based on firms listed in the Korea Stock Exchange market from 2000 to 2017, we find that earnings and cash flow components of financial statements enhance the predictability of future cash flow in the Korean stock market. Conversely, foreign investors showed a tendency to decide on investments based on operating cash flow instead of earnings when predicting future cash flow. These findings indicate that reliability towards earnings may fall since foreign investors’ concerns are on the prospects of earnings management. These results were strengthened by the addition of several more analyses including cluster analyses, consideration of information asymmetry and the chaebol governance. Full article