Search Results (139)

The increasing adoption of machine learning models in corporate valuation has substantially improved predictive accuracy but at the cost of interpretability, a critical limitation in regulated financial environments. This study investigates whether SHAP (Shapley Additive Explanations) can systematically enhance the transparency of XGBoost based valuation models and examines whether the resulting insights extend those of classical linear regression. An empirical analysis was conducted on a cross-sectional dataset of U.S. publicly listed firms (2018), including more than 200 financial indicators. After systematic preprocessing and a hybrid feature selection procedure combining XGBoost importance, mutual information, and correlation based filtering, both an OLS regression and an XGBoost model were trained and validated. XGBoost achieved substantially higher predictive performance (R² = 0.654 vs. 0.364), while SHAP values provided transparent global and local explanations of model decisions. Both models consistently identified earnings before tax and EV to sales as primary value drivers; however, SHAP additionally showed nonlinear effects, threshold behaviors, and context dependent interactions, particularly for EBITDA margin, share buybacks, and asset based indicators, that remain undetectable in linear models. These findings confirm that SHAP significantly enhances model transparency and generates economically meaningful insights beyond classical regression, supporting its application in auditable, regulatory compliant financial modeling. Full article

Algorithmic personalization is the flagship mechanism of interactive marketing: platforms learn from user behavior and respond with individually curated value propositions, and industry practice treats the quality of that loop as a lever for customer retention. Whether personalization actually converts into consumer commitment—operationalized here as continuance intention—has been tested almost exclusively in affluent, algorithm-saturated markets. This study tests the conversion where the assumption should be most fragile: subscription video-on-demand in Albania, a price-sensitive transitional European market. Drawing on an extended value-based expectation-confirmation model within a stimulus–organism–response structure, algorithmic personalization, perceived value, and cross-device compatibility are modeled as antecedents of customer satisfaction and continuance intention—the attitudinal antecedent of subscription renewal—among 284 Albanian subscribers, using partial least squares structural equation modeling with 5000 bias-corrected bootstrap resamples. Personalization did not predict continuance intention directly (β = −0.060, ns), and its total effect was effectively zero (−0.004): the significant but weak indirect pathway through satisfaction was offset by the small negative direct path. Perceived value dominated—associated with continuance intention directly (β = 0.337) and through satisfaction (total effect = 0.609)—and emerged in an importance–performance map analysis as the highest-importance, lowest-performing construct. Robustness analyses show the null is not a measurement artifact and sharpens among personal payers; within the sample, it decomposes into a modestly positive personalization effect among lower-income subscribers and a negative effect among higher-income respondents (difference p = 0.003), contradicting a simple income-constraint account and leaving the moderating mechanism open. The findings provide evidence for the market contingency of personalization’s retention promise: in this market, the algorithm does not earn the renewal; the value proposition does. Full article

In construction project management, the execution phase relies on deterministic scheduling methods that cannot represent activity-level uncertainty or support quantitative corrective-action testing before physical commitment. This study presents a conceptual framework and proof-of-concept application that addresses this gap by integrating Building Information Modeling (BIM) with the COSMOS Simulator—a construction-specific discrete-event engine benchmarked against industry simulators in prior work. The framework formalizes a semi-automated pipeline from Autodesk Revit through Dynamo BIM to COSMOS via a governed parameter store, converting BIM-derived quantity takeoffs into stochastic simulation inputs. This study makes two contributions: The first is the development of a governed BIM-to-simulation data pipeline in which an identifier-keyed data contract and in-flow validation establish an auditable chain of custody from design element to simulation input. The second is an advancement toward addressing a limitation the engine’s own validation studies identify—execution-phase site–data integration—through a control loop that couples Earned Value Analysis in a 4D BIM environment (Synchro) with iterative COSMOS-based scenario testing, enabling managers to evaluate corrective actions quantitatively before site implementation. Feasibility is demonstrated on an illustrative reinforced concrete building schedule, in which a schedule-performance shortfall triggers the loop and stochastic forecasting exposes an upper-tail completion risk hidden by the deterministic estimate. By establishing a governed pathway from BIM to the previously BIM-isolated COSMOS engine, this work provides a basis from which field-based evaluation of BIM-integrated stochastic project control can proceed. Full article

Multivariate control charts (MCC) are widely used to detect out-of-control (OOC) situations in interrelated processes; however, they do not directly provide information about the source(s) of these signals. Although various methods for signal decomposition and interpretation have been proposed in the literature, most of them are limited to statistical interpretation and do not support automated signal classification. To overcome this limitation, an intelligent decision-support approach combining MCCs and machine learning for OOC signal classification has been developed. Interrelated cost and schedule performance indicators obtained through earned value analysis (EVA) were monitored by a Hotelling T² control chart. When an OOC signal occurred, the associated variable(s) were identified using the Mason–Young–Tracy (MYT) decomposition method, and the resulting MYT classifications were used as class labels for supervised learning of an artificial neural network (ANN). The Box–Behnken experimental design was used to determine the optimal network architecture and training hyperparameters of the ANN. The findings showed that the optimized ANN model achieved 93.33% classification accuracy, and the optimization model explained 85.14% of the variation in Mean Squared Error (MSE). The main contribution of this study is the integration of statistical signal decomposition and machine learning into a learning-based decision-support mechanism for the automated interpretation of MCC signals. The developed approach provides a systematic, practical decision-support tool for identifying which EVA-based performance parameter(s) are associated with an OOC signal in the monitoring of complex processes. Full article

Cordyceps militaris is a high-value medicinal mushroom with growing demand in functional-food and nutraceutical markets, yet practical frameworks for small-scale, family-operated cultivation remain limited. This study presents an integrated technical and economic feasibility analysis of small-scale Cordyceps production under two scenarios: a one-room setup (Scenario 1) and a two-room configuration with a shared processing area and staggered scheduling (Scenario 2). Both use consistent biological, operational, and market assumptions with no hired labor, and the analysis covers capital expenditure (CapEx), operating costs (OpEx), profitability, payback, and break-even thresholds, complemented by sensitivity analysis of parameters such as biological efficiency and contamination rates. Both scenarios were technically and financially viable. Scenario 1 achieved a net present value (NPV) of $1761, an internal rate of return (IRR) of 10%, a 4.7-year discounted payback, and a 133% five-year return on investment (ROI); Scenario 2 attained an NPV of $85,437, a 66% IRR, a 1.6-year payback, and a 366% ROI. Because gross margins were consistent across scales, the expansion’s advantage stemmed from more efficient CapEx amortization rather than improved unit profitability. Cordyceps cultivation emerges as a viable family-operated, small-scale enterprise that can diversify family income, generate supplementary or primary earnings, and support urban and rural livelihoods. Full article

The concept of virtual water is currently one of the most important issues in water resource management, especially in a context marked by structural water scarcity. Beyond the analysis of virtual water flows, which has been widely studied in the literature, this study aims to better understand the relationship between virtual water exports and economic growth. This paper analyzes the dynamic relationship between Morocco’s economic growth and agricultural virtual water exports to the European Union over the period of 1986–2023. An ARDL model was used based on annual data to test cointegration and estimate short- and long-term effects, controlling for gross fixed capital formation and agricultural value added. The bounds test confirms the existence of a stable long-term relationship between the variables. The results suggest that export specialization may be associated with foreign earnings and agricultural activity while also coinciding with greater pressure on resources and potential adaptation costs, especially for blue water resources. However, estimates indicate that in the long term, investment is positively and significantly associated with growth, while virtual water exports are associated with a negative effect on GDP, suggesting that export gains may be offset by increasing water constraints and sectoral trade-offs, and that agricultural value added mainly influences short-term dynamics. The results highlight the importance of integrating water footprint and virtual water trade concepts, as well as climate constraints, into agricultural and trade strategy planning while strengthening policies on water efficiency, innovation, and governance. Full article

Valuation methodologies vary across industries because firms differ in capital intensity, asset life, earnings stability, and exposure to risk. This study examines the valuation approaches used by South African equity analysts across the diversified mining, platinum group metals mining, gold mining, retail, and banking sectors over the 2018–2026 period, with non-financial firm coverage extending to 2024 and banking sector coverage extending to 2026. Using qualitative document analysis of 201 equity research reports covering 24 Johannesburg Stock Exchange-listed companies, including 19 non-financial firms and the five largest South African banks, the study identifies clear clustering of valuation methods by industry. The findings show that resource-based sectors are predominantly valued using intrinsic approaches such as life-of-mine discounted cash flow (DCF) and risk-adjusted net present value (NPV), while retail firms are primarily valued using earnings-based multiples. Gold mining exhibits a hybrid valuation pattern, and banking institutions are valued using balance-sheet- and profitability-based approaches anchored on book value, return on equity, and dividend flows. Overall, the results suggest that valuation practices in the sampled equity research reports are strongly industry-specific and broadly aligned with the underlying economic characteristics of the sectors analysed. The study contributes to the limited empirical literature on professional valuation practice in African capital markets and provides insights relevant to analysts, investors, and regulators. Full article

Mauritius, as a Small Island Developing State (SIDS), depends heavily on tourism and is therefore exposed to external shocks; this study examines how the sector’s performance and value capture evolved from 2010 to 2025, with particular attention to the COVID-19 disruption and subsequent recovery. The analysis uses only secondary data, combining arrivals and source-market breakdowns published by the Ministry of Tourism with the monthly series of gross tourism earnings released by the Bank of Mauritius. Trends and seasonality are described for both arrivals and earnings, and three indicators are derived to support interpretation: revenue per arrival (as a proxy for value capture), the intensity of seasonality, and the concentration of source markets. The results document the magnitude of the pandemic-related break, trace the timing of the rebound, and show how value capture and market concentration shifted between the pre- and post-COVID periods. The paper concludes by discussing the implications for resilience in island destinations, highlighting the need for diversification and higher-value positioning, and proposing a replicable monitoring approach that can be updated as new official data become available. Full article

Nailfold capillaroscopy has earned its place as a cornerstone of clinical assessment in systemic sclerosis (SSc). Its ability to detect early microvascular changes, distinguish primary from secondary Raynaud’s phenomenon, and contribute to disease classification has fundamentally reshaped the clinical approach to early diagnosis and disease stratification. The recognition of distinct capillaroscopic patterns offers a structured framework for tracking disease evolution and identifying patients who warrant closer surveillance or proactive therapeutic intervention. The inclusion of capillaroscopic abnormalities in the ACR/EULAR 2013 classification criteria validates its diagnostic importance and facilitates identification of patients with early or limited cutaneous disease. Beyond diagnosis, emerging evidence supports prognostic applications, particularly for predicting digital ulcers, though the predictive value for other organ complications requires further validation. As a non-invasive, safe, and reproducible technique, capillaroscopy is particularly well-suited to long-term disease monitoring. Quantitative scoring systems allow for rigorous, objective tracking of microangiopathic progression and hold considerable promise as outcome measures in clinical trials targeting vasculopathy. Ongoing technological advances, particularly in automated image analysis and integration with functional assessment tools, promise to enhance the clinical utility of capillaroscopy while reducing operator dependency. Standardization efforts and validation of capillaroscopic parameters as clinical trial endpoints will be crucial for realizing the full potential of this technique. Full article

This study constitutes the second part of a comprehensive investigation of the persistence of weighted average cost of capital (WACC) rates despite declining risk-free interest rates. While theory suggests that WACC should reflect lower risk-free interest rates and decline with falling government bond yields, empirical evidence reveals minimal adjustment in the reported WACC figures. Disclosed WACC of DAX40 companies remain between 7% and 8% as the yield of a ten-year German government bond fell from 4.1% to −0.2%. After the quantitative risk analysis (part I) systematically lacks market-based and fundamental explanations—demonstrating that neither systematic risk, overall market risk, earnings risk nor leverage increased sufficiently to justify this stability—this article addresses the resulting explanatory gap through qualitative inquiry. Employing a grounded theory methodology, we investigate the causes and consequences of persistent WACC through systematic analysis of 18 problem-centered semi-structured expert interviews (22 respondents comprising corporate finance executives, investment bankers, strategy consultants, auditors). The investigation reveals that behavioral economics (risk aversion, opportunism, subjectivity), organizational constraints (strategic path dependency, implementation complexity, financial criterion rigidity), and model-theoretic discretion (parameter averaging, analyst influence, supplementary risk adjustments) substantially shape practical WACC determination—factors that quantitative risk analysis cannot capture. Practitioners employ disclosed WACC strategically to reconcile investor return requirements with long-term operational stability, avoid audit friction, and hedge geopolitical–monetary risks—consequences that generate capital opportunity costs offsetting traditional value-maximization objectives. Combined quantitative and qualitative evidence yields actionable insights for value-based capital cost methodologies that are aligned with organizational and market realities. Full article

This study proposes a data-driven evaluation framework to quantify the impact of artificial intelligence (AI) on industrial process performance and enterprise value creation. The framework integrates enterprise value assessment based on the Feltham–Ohlson model with a multi-level performance evaluation framework that incorporates a hybrid Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) for indicator weighting, together with Fuzzy Comprehensive Evaluation (FCE) for multi-dimensional aggregation. This integrated approach enables systematic analysis of AI-driven effects from the perspectives of intelligent investment input, operational governance environment, and process output performance. Using panel data from 3515 Chinese A-share listed firms (20,076 firm-year observations) during 2014–2022, a Process Performance Index (PI) is constructed to measure AI-enabled operational capability across resource allocation efficiency, coordination effectiveness, and production performance dimensions. Empirical results indicate that PI is positively associated with abnormal earnings and firm profitability, demonstrating that AI-enabled process capability contributes to sustained enterprise value growth. The findings further show increased digital technology investment intensity, knowledge-based human capital accumulation, and improved data governance conditions, accompanied by enhanced production and service performance. By explicitly integrating AHP–EWM weighting and FCE aggregation within the Feltham–Ohlson valuation structure, the proposed framework provides an interpretable quantitative mechanism linking AI adoption, operational capability development, and enterprise value creation. The results offer practical insights for evaluating intelligent transformation strategies in the context of Industry 5.0 and data-driven industrial development. Full article

This study recognizes education as an investment and estimates the private rates of return to upper secondary education in Greece, overall, by type (general or vocational) and by gender. Earnings data were collected through primary research using stratified sampling from the private sector of the economy. The analysis is based on the Mincer method and is complemented by machine learning methods, including Support Vector Regression, Random Forests, and Extreme Gradient Boosting. The empirical analysis shows that investing in upper secondary education (general and vocational) is profitable. The private rates of return in upper general secondary education are higher than those in vocational education, and female graduates exhibit higher returns than male graduates. Machine learning models achieve modest improvements in predictive performance, as reflected in higher adj. R² values and lower prediction errors. However, the estimated rates of return remain broadly consistent with those obtained from the Mincer method. This convergence suggests that the Mincer specification captures the core structural relationship between education and earnings, while machine learning models primarily enhance predictive accuracy without substantially altering the estimated economic returns. This finding highlights the robustness of the traditional econometric framework and clarifies the complementary role of machine learning techniques in empirical labor economics. Full article

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

This study examines how institutional investors allocate trading across the earnings announcement cycle and whether industry trading concentration strengthens that activity. The analysis is motivated by two complementary ideas: public disclosures can increase the value of investors’ prior information, and even sophisticated investors face costly information processing. These perspectives imply that institutional trading need not be concentrated only before disclosure and may be strongest after earnings announcements, when investors combine newly released public information with prior firm- and industry-specific signals. Using daily institutional trading data from Ancerno, we find that institutional net trading is positively related to earnings surprises before, during, and after earnings announcements, with the strongest relation occurring in the post-announcement period. We also document a clear asymmetry: trading is strongly related to positive earnings surprises across all three stages, whereas trading related to negative earnings surprises is concentrated mainly after disclosure. In addition, industry trading concentration strengthens the relation between institutional trading and earnings news across the announcement cycle, especially for positive surprises. These findings provide an integrated view of institutional information processing around a major recurring disclosure event, show that the timing of institutional trading is informative about how earnings news is incorporated into prices, and support the view that industry specialization is linked to stronger earnings-related trading. Full article

The article examines the persistent stability of the weighted average cost of capital (WACC) disclosed by German DAX40 companies despite substantial declines in risk-free interest rates between 2004 and 2021. While theory suggests that WACC should reflect lower risk-free interest rates and decline as well with falling government bond yields, empirical evidence reveals minimal adjustment in reported WACC figures. Disclosed WACC of DAX40 companies remains between 7% and 8% as the yield of the ten-year German government bond fell from 4.1% to −0.2%. This study employs quantitative analyses to investigate whether systematic increases in risk exposure can explain this phenomenon. Using capital market data spanning from 2000 to 2023, we analyze five risk dimensions: systematic risk (beta factors), overall market volatility, risk aversion (lambda factors), earnings risk, and financial structure risk. Bootstrap analyses reveal a 41.5% reduction in beta factor variance, while volatility analyses demonstrate declining market risk exposure. The market price of risk analysis does not reveal definite findings. Earnings risk measures indicate improved financial stability, and debt ratios show modest declines. These findings suggest that observable risk parameters cannot explain persistent WACC levels, indicating a disconnect between theoretical WACC calculations and practitioner applications in investment project decision-making following value-based management principles. Full article