Search Results (73)

This study explores how IFRS adoption is associated with analysts’ forecast accuracy, optimism, and dispersion in Saudi Arabia. Drawing on data from publicly listed firms from 2013 to 2020, we assess changes in forecasting behavior surrounding the IFRS transition, accounting for firm-specific and macroeconomic factors. We argue that IFRS is expected to support more transparent financial statements, reduce risk and uncertainty, and offer a standardized and detailed reporting framework that influences analysts’ predictive performance. The findings reveal more accurate forecasts and a decline in both optimism and dispersion following IFRS adoption, suggesting enhanced financial reporting quality and reduced uncertainty. These associations underscore IFRS’s potential role in refining analysts’ earnings predictions and promoting stock market transparency. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

In the automotive industry, the supply of service parts—such as bumpers, batteries, and aero parts—is required even after the end of vehicle production, as customers need them for maintenance and repairs. To earn customer confidence, manufacturers must ensure timely availability of these parts while managing inventory efficiently. An excess of inventory can increase warehousing costs, while stock shortages can lead to supply delays. Accurate demand forecasting is essential to balance these factors, considering the changing demand characteristics over time, such as long-term trends, seasonal fluctuations, and irregular variations. This paper introduces a novel method for time series forecasting that employs Ensemble Empirical Mode Decomposition (EEMD) and Dynamic Mode Decomposition (DMD) to analyze service part demand. EEMD decomposes historical order data into multiple modes, and DMD is used to predict transitions within these modes. The proposed method demonstrated an approximately 30% reduction in forecasting error compared to comparative methods, showcasing its effectiveness in accurately predicting service parts demand across various patterns. Full article

In the digital age, e-commerce has become a critical part of modern economies, shaping global economic growth and the pursuit of the Sustainable Development Goals (SDGs). This study uses robust statistical methods to explore the complex relationships between traditional trade, e-commerce, and key economic and sustainability indicators. The General Linear Model (GLM), factor analysis, and linear regression reveal that conventional trade remains vital for GDP growth, even though e-commerce clearly influences SDG performance. The study emphasizes the catalytic role of e-commerce in advancing sustainability by showing how treating it as a dependent variable speeds up SDG progress through Brown, Holt, and ARIMA forecasting models. Additionally, cluster analysis uncovers a strong link between higher SDG scores and increased e-commerce activity, with countries scoring better on sustainability often having more companies in the digital economy and earning more online. This research provides a comprehensive understanding of how e-commerce can support global sustainability goals, along with integrated policy recommendations that promote digital transformation and long-term environmental and social resilience. Full article

With the expansion of global trade, China’s commodity futures market has become increasingly intertwined with regional maritime logistics. The Ningbo Containerized Freight Index (NCFI), as a key regional indicator, reflects freight rate fluctuations and logistics efficiency in real time. However, limited research has explored how commodity futures data can enhance NCFI forecasting accuracy. This study aims to bridge that gap by proposing a hybrid deep learning model that combines recurrent neural networks (RNNs) and gated recurrent units (GRUs) to predict NCFI trends. A comprehensive dataset comprising 28,830 daily observations from March 2017 to August 2022 is constructed, incorporating the futures prices of key commodities (e.g., rebar, copper, gold, and soybeans) and market indices, alongside Clarksons containership earnings. The data undergo standardized preprocessing, feature selection via Pearson correlation analysis, and temporal partitioning into training (80%) and testing (20%) sets. The model is evaluated using multiple metrics—mean absolute Error (MAE), mean squared error (MSE), root mean square error (RMSE), and R²—on both sets. The results show that the RNN–GRU model outperforms standalone RNN and GRU architectures, achieving an R² of 0.9518 on the test set with low MAE and RMSE values. These findings confirm that integrating cross-market financial indicators with deep sequential modeling enhances the interpretability and accuracy of regional freight forecasting. This study contributes to sustainable shipping strategies and provides decision-making tools for logistics firms, port operators, and policymakers seeking to improve resilience and data-driven planning in maritime transport. Full article

This study examines whether climate risk disclosure (CRD) matters to financial analysts in China. Using textual analysis to measure CRD, we find that CRD is negatively related to analyst forecast error and dispersion, supporting the information hypothesis. We also find that information disclosure quality (e.g., earnings quality) and external monitoring (e.g., long-term institutional investor) may moderate this relationship. Mechanism analysis indicates that lower information asymmetry and more climate-related on-site visits are potential channels through which CRD influences analyst forecast properties. Furthermore, the above relationship is more pronounced in regions with higher climate awareness, carbon-intensive industries, and state-owned enterprises, and the relationship is primarily driven by transition risk disclosure (TCRD) rather than physical risk disclosure (PCRD). Our findings, which remain valid after addressing various robustness and endogeneity concerns, have significant implications for regulators to standardize and enhance CRD practices. Full article

This article examines whether the presentation of integrated reports (IRs), the external assurance of non-financial information, and the use of auditing standards affect market liquidity and the accuracy of earnings per share forecasts in the Chilean market following the publication of the International IR Framework. Using ordinary least squares estimations, results show that IRs significantly reduce information asymmetry, thereby improving market liquidity. This effect is reinforced when non-financial information is externally assured, particularly under the ISAE3000 standard. However, neither IRs nor external assurance significantly impact financial analysts’ earnings forecast accuracy, suggesting that such information serves a complementary role in their evaluations. This study contributes to the literature by providing empirical evidence on the role of IRs and assurance in emerging economies, emphasizing their effectiveness in enhancing transparency and liquidity. The findings have direct implications for companies, as they suggest that adopting IRs and obtaining external assurance can strengthen market perceptions and investor confidence, particularly when using the ISAE3000 standard. For regulators, the results highlight the potential benefits of promoting standardized sustainability disclosures and assurance mechanisms to foster transparency in capital markets. Investors, in turn, can use IR quality and assurance as signals of corporate credibility and long-term value creation. Full article

We use a quasi-exogenous shock to information asymmetry among shareholders to evaluate the effect of information asymmetry on corporate disclosure. In the post-Regulation Fair Disclosure (FD) period, the merger between a shareholder and a lender of the same firm provides a shock to the information asymmetry among equity investors, because Regulation FD applies to shareholders but not lenders. After the merger, the shareholder gains access to the firm-specific private information held by the lender, which produces an asymmetry in the information held by shareholders. We first provide evidence that information asymmetry among shareholders indeed increases after the shareholder–lender mergers. We then use a difference-in-differences research design to show that after shareholder–lender merger transactions, firms issue more quarterly forecasts (including earnings, sales, capital expenditure, earnings before interest, taxes, amortization (EBITDA), and gross margin), and the quarterly earnings forecasts are more precise. This study provides direct empirical evidence that information asymmetry among shareholders affects corporate disclosure. Firms can address increased information asymmetry by providing more disclosures, fostering a more equitable information environment. Additionally, policymakers might consider these results when evaluating the implications of Regulation FD, particularly in the context of mergers and acquisitions (M&A) of financial institutions where a shareholder gains access to private information held by a debt holder. Full article

This paper challenges the prevailing view that intra-industry information transfers are primarily driven by public information. Contrary to conventional wisdom, I find that investors in late-announcing firms impound more private information after early-announcing peers report earnings. This increase is substantial, leading to an 18.2% decrease in analyst forecast consensus and a 24.9% increase in forecast precision. Moreover, the probability of informed trading rises by 2% on days with peer announcements. This finding is important because investors tend to overweight (underweight) private (public) signals, thereby exacerbating over- and underreaction anomalies. Our study confirms that these anomalies are more pronounced when early announcements stimulate private information production, offering a theoretical explanation for their puzzling coexistence. These findings have significant implications for investor behavior and market efficiency. Investors should diligently evaluate both public and private information, particularly following peer announcements. Policymakers can leverage these findings to design regulations that promote transparency and foster efficient information dissemination. Full article

As environmental, social, and governance (ESG) issues become increasingly important, ESG ratings have become a significant factor influencing audit fees for businesses. However, ESG ratings are typically assessed by multiple agencies or rating firms and, due to differences in evaluation criteria, methodologies, and data sources, the ratings provided by different institutions may vary considerably. Therefore, research on the impact of discrepancies in ESG ratings on audit fees is of great significance. This paper examines this phenomenon by analyzing a sample of Chinese listed companies from 2015 to 2022, yielding 3056 observational values through various methodologies. The study employs two-way fixed effects methods. The findings indicate that discrepancies in ESG ratings significantly elevate enterprises’ audit expenses, with operating risk and analyst earnings forecast errors serving as intermediary factors. Additionally, media attention intensifies these effects by increasing corporate disclosure, intensifying regulatory pressure, and heightening reputational risks for the company, and the positive impact of ESG rating discrepancies on audit fees is more significant when the “Big 4” accounting firms are involved in the audit. The research offers insights for enterprises, auditors, and regulatory bodies, contributing to the enhanced implementation of the ESG concept and fostering sustainable enterprise development. Full article

Forecasting using historical time series data has become increasingly important in today’s world. This paper aims to assess the potential for stable positive development within the wholesale and retail trade sector (SK NACE Section G) and the operations of HORTI, Ltd.( Košice, Slovakia), a company within this industry (SK NACE 46.31—wholesale of fruit and vegetables) by predicting three financial indicators: costs, revenues, and earnings before taxes (EBT) (or earnings after taxes (EAT)). We analyze quarterly data from Q1 2009 to Q4 2023 taken from the sector and monthly data from January 2013 to December 2022 for HORTI, Ltd. Through time series analysis, we aim to identify the most suitable model for forecasting the trends in these financial indicators. The study demonstrates that simple legacy forecasting methods, such as exponential smoothing and Box–Jenkins methodology, are sufficient for accurately predicting financial indicators. These models were selected for their simplicity, interpretability, and efficiency in capturing stable trends, and seasonality, especially in sectors with relatively stable financial behavior. The results confirm that traditional Holt–Winters’ and Autoregressive Integrated Moving Average (ARIMA) models can provide reliable forecasts without the need for more complex approaches. While advanced methods, such as GARCH or machine learning, could improve predictions in volatile conditions, the traditional models offer robust, interpretable results that support managerial decision-making. The findings can help managers estimate the financial health of the company and assess risks such as bankruptcy or insolvency, while also acknowledging the limitations of these models in predicting large shifts due to external factors or market disruptions. Full article

Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries. Full article

Forecasting the final cost of construction projects during the construction phases is challenging, particularly for long-duration projects, due to the rise in uncertainties associated with future cost performance index values after report dates and the impact of many factors on cost performance. Current practices, along with existing methods and models, often assume that the cost performance index (CPI) achieved at the report date will continue as is for the remaining work and they fail to assess the risk of cost overruns. This assumption may not be true, as in many cases, the cost performance may change due to the impact of many factors. Thus, this paper aims to introduce a new multi-method fuzzy-based forecasting model to forecast the project’s final cost and to circumvent the limitations and setbacks of the used models and methods. It integrates earned value concepts with expert judgment using fuzzy logic to address the limitations of current practice in forecasting a project’s final cost during construction. Factors influencing future cost performance were identified through conducting in-depth literature reviews and expert interviews. The identified factors were then weighed using the relative importance index and pairwise comparisons. The top five most important factors are labor productivity, rework percentage, manpower availability, subcontractor management, and project plan controllability. These factors were then utilized to define the uncertainty associated with the future cost performance according to a project management team’s experience, using fuzzy numbers for forecasting the project’s final cost. Validation with real construction projects showed that the model provides more accurate predictions of completion costs through straightforward calculations. The model has been implemented in a computer application that is integrated with commercial scheduling software (P6 Professional). Full article

This study seeks to answer the question of whether we could use a bank’s past financial data to predict the bank failure in 2009 and proposes three new empirical proxies for loan quality (LQ), interest margins (IntMag), and earnings efficiency (OIOE) to forecast bank failure. Using the bank failure list from the Federal Deposit Insurance Corporation (FDIC) database, I match the banks that failed in 2009 with a control sample based on geography, size, the ratio of total loans to total assets, and the age of banks. The model suggested by this paper could predict correctly up to 94.44% (97.15%) for the failure (and non-failure) of banks, with an overall 96.43% prediction accuracy, (p = 0.5). Specifically, the stepwise logistic regression suggests some proxies for capital adequacy, assets/loan risk, profit efficiency, earnings, and liquidity risk to be the predictors of bank failure. These results partially agree with previous studies regarding the importance of certain variables, while offering new findings that the three proposed proxies for LQ, IntMag, and OIOE statistically and economically significantly impact the probability of bank failure. Full article

The study delves into the primary large-scale atmospheric features contributing to extreme weather events across Europe during early September 2023. The period was examined using a dataset composed by the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and satellite imagery. In early September 2023, an omega blocking pattern led to the development of a low-pressure system over the Iberian Peninsula producing heavy precipitation and flooding over Spain and acting as a mechanism for a mineral dust outbreak. A second low-pressure system developed over Greece. Extreme precipitation was recorded across Greece, Turkey, and Bulgaria as the system gradually shifted southward over the Mediterranean. The system earned the name “Storm Daniel” as it acquired subtropical characteristics. It caused floods over Libya and its associated circulation favoured the transport of mineral dust over Northern Egypt as it moved eastward. Meanwhile, the high-pressure blocking system associated with the omega pattern induced heatwave temperatures in countries further north. This period was compared with the large-scale circulation observed in mid-September 2020, when severe weather also affected the Mediterranean region. However, the weather systems were not directly connected by the large-scale circulation, as shown in September 2023. Although mesoscale conditions are relevant to formation and intensification of some atmospheric phenomena, the establishment of an omega blocking pattern in early September 2023 showed how large-scale atmospheric dynamics can produce abnormal weather conditions on a continental scale over several days. Full article