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This study aims to examine the impact of intellectual capital (IC) on default risk in Indian companies listed on the National Stock Exchange.

This study applies panel data regression analysis to derive a relationship between IC and default risk for the sample period 2013–2022. The value-added intellectual coefficient (VAIC) of Pulic (2000) has been applied to measure IC performance, and default risk is estimated using the revised Z-score model of Altman (2000).

The results revealed a positive association between Z-score and VAIC. It implies that a higher value of VAIC improves financial stability and leads to a lower likelihood of default. The findings further suggest that new default forecasting models can be experimented with IC indicators for better default prediction.

The findings can have implications for investors and banks. This paper provides evidence of IC performance in improving the financial solvency of firms. Investors and financial institutions should invest their resources in a healthy firm that effectively manages and invests in their IC. It will eventually award investors and creditors high returns through efficient value-creation processes.

This study provides evidence of IC performance in improving the financial solvency of Indian high-defaulting firms, which lacks sufficient evidence in this domain of research. Numerous studies exist examining the relationship between firm performance and IC value, but this area is inadequately focused and underresearched. This study, therefore, fills the research gap from an Indian perspective.

This study aims to present a model for detecting and predicting creative accounting in companies listed on the Tehran Stock Exchange (TSE).

The authors conduct this research in three stages. First, the authors review the literature to determine the dimensions, components, indicators and techniques of creative accounting. Second, the authors conduct semi-structured interviews with experts using the fuzzy Delphi technique to obtain screening and reach a consensus. Finally, the authors develop a model to predict creative accounting by classifying the financial statements of the sample companies into two groups based on the use or non-use of creative accounting techniques, measuring the indicators determined in the previous stage, running various machine learning algorithms and choosing the superior algorithm.

The results indicate the usefulness of accounting information for detecting and predicting creative accounting and the relevance of several financial attributes as important predictors. The results also indicate the superiority of extremely randomized trees over other algorithms in predicting creative accounting and suggest that the primary purpose of creative accounting in Iran is earnings management. Contrary to the political cost hypothesis, large Iranian companies use creative accounting to inflate profits.

The present research also has several limitations that must be considered, and caution must be exercised in interpreting and generalizing the findings as specified in the revised manuscript.

This study’s implications are significant for policymakers, standard-setters and practitioners. By recognizing the detrimental effects of creative accounting on financial transparency within companies, policymakers can address existing gaps in accounting standards to minimize the potential for earnings manipulation. Consequently, strengthening internal and external mechanisms related to a firm’s financial performance becomes achievable. The study provides evidence of the need for audit firms to recognize the importance of creative accounting and consider creative accounting in their audit plans to prevent insufficient or even misleading disclosure by companies that extensively use creative accounting practices in their financial reporting. Moreover, knowledge of creative accounting techniques can help auditors assess audit and detection risks and serve as a valuable guide for reducing audit costs and improving audit quality.

Given that creative accounting practices distort the true or real accounting results, curbing creative accounting practices reduces corporate failures and could lead to the reduction of job losses and other social consequences.

This study uses a unique database in Iran to determine a model for predicting creative accounting using a mixed-method methodology, qualitative and quantitative, to identify creative accounting techniques and run various machine learning algorithms.

The purpose of this research is to examine the impact of governance mechanisms on corporate failure.

This study used a hypothesis-testing research design to collect data from the annual reports of 35 companies listed on Palestine Exchange from 2010 to 2019. Descriptive and inferential statistics were employed, along with correlation analysis to evaluate linear relationships between variables. The variance inflation factor was used to test multicollinearity, and binary logistic regression was utilized to develop the research model.

There is a significant positive relationship between board of directors' independency, institutional ownership and the quality of external audit, and corporate failure reduction. No significant relationship has been found among corporate governance variables such as board size, board meetings' frequency, board members' remuneration and audit committee existence, and corporate failure reduction.

Several empirical research studies have developed models to predict corporate failure using accounting and financial data. However, limited research has empirically investigated the impact of the different mechanisms of governance on corporate failure prediction.

The research highlighted the significance of companies' commitment to governance principles and their impact on predicting failure. The study suggests that decision-makers and managers can adopt different governance mechanisms to support corporate success and avoid those that may lead to negative consequences and failure.

This research is the first in Palestine to use a comprehensive list of corporate governance mechanisms to predict the failure of companies listed on the Palestine Stock Exchange between 2010 and 2019.

This study investigates the connection between corporate governance and zombie firm’s exit time.

With a sample of 2,794 French zombie firms, the analysis focuses on four aspects of corporate governance: board size (BS), managerial ownership (MO), director turnover (DT) and ownership concentration, using tobit regression.

Dimensions of corporate governance have an important role in determining zombie firms’ exit time. MO and ownership concentration increase zombie firm exit time, whereas larger BSs and DT reduce it.

To the best of the authors’ knowledge, this study is the first to include corporate governance as a characteristic relevant to zombie firms’ exit time. It provides new insights on why some zombie firms remain in the market longer than expected.

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.

The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.

The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.

To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

We develop a credit-risk model to study the informational role of investment in an economy susceptible to large liquidity shocks. Firms' investment decisions carry information about their asset quality, thereby mitigating informational frictions when firms enter bankruptcy. An increase in aggregate investment can reduce the informational value of investment, depressing firms' recovery values. Therefore, policies boosting investment can decrease debt and firm values by reducing the informational value of investment. The presence of debt overhang may enhance firm value by making firms' investment decisions more informative. We present suggestive empirical evidence consistent with model predictions on the relation between firms' investments and recovery rates.

By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.

The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.

The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.

From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.

This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction.

In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting.

Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters.

This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.