This study aims to analyze the influence of leverage,firm size,firm value,and managerial ownership on tax aggressiveness in technology sector companies listed on the Indonesia Stock Exchange.Tax aggressiveness has bec...This study aims to analyze the influence of leverage,firm size,firm value,and managerial ownership on tax aggressiveness in technology sector companies listed on the Indonesia Stock Exchange.Tax aggressiveness has become a critical issue in corporate financial management practices,particularly in the context of optimizing tax burdens through strategies that remain within legal boundaries.The study adopts a quantitative approach using panel data regression methods.Data processing and analysis were conducted using EViews version 12.The research sample consists of 13 technology sector companies selected through purposive sampling,with an observation period spanning five years(2019-2023),resulting in a total of 65 observations.The analysis results indicate that leverage and firm value have a negative effect on tax aggressiveness.Conversely,managerial ownership is found to have a positive effect on tax aggressiveness.Meanwhile,firm size does not show a significant influence on tax aggressiveness.展开更多
By applying the theory of the relationship between microeconomic leverages and corporate risks,this paper discusses methods that the IPOs should choose to dispose the trademark with their parent companies so as to go ...By applying the theory of the relationship between microeconomic leverages and corporate risks,this paper discusses methods that the IPOs should choose to dispose the trademark with their parent companies so as to go public with a high issue price.By analyzing their microeconomic leverage effects,this paper points out that each method has its specific effect on risks and the IPOs should choose the method according to its risk factors,existing assets and financial structure to gain risk income and decrease risk losses.展开更多
Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation ...Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation to continue in the presence of fault measurement.Affected by satellite geometry, the leverage of each measurement in position solution may differ greatly.However, the conventional RAIM FI methods are generally based on maximum likelihood of ranging error for different measurements, thereby causing a major decrease in the probability of correct identification for the fault measurement with high leverage.In this paper, the impact of leverage on the fault identification is analyzed.The leveraged RAIM fault identification（L-RAIM FI） method is proposed with consideration of the difference in leverage for each satellite in view.Furthermore,the theoretical probability of correct identification is derived to evaluate the performance of L-RAIM FI method.The experiments in various typical scenarios demonstrate the effectiveness of L-RAIM FI method over conventional FI methods in the probability of correct identification for the fault with high leverage.展开更多
Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time....Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.展开更多
This article discusses the problem of the detection of influential cases in nonlinear reproductive dispersion models (NRDM). A diagnostic based on case\|deletion approach in estimating equations is proposed. The relat...This article discusses the problem of the detection of influential cases in nonlinear reproductive dispersion models (NRDM). A diagnostic based on case\|deletion approach in estimating equations is proposed. The relationships between the generalized leverage defined by Wei et al. in 1998, statistical curvature, and the local influence of the response vector perturbations are investigated in NRDM. Two numerical examples are given to illustrate the results.展开更多
Based on the latest macro financial data, this paper estimates China＇ s overall leverage ratio and sector-specific leverage ratios for households, non-financial enterprises, government and financial institutions. It ...Based on the latest macro financial data, this paper estimates China＇ s overall leverage ratio and sector-specific leverage ratios for households, non-financial enterprises, government and financial institutions. It is noted with particular emphasis that the tendency of non-financial enterprises to increase leverage has further intensified instead of abated, which warrants our great attention. Considering that increasing leverage of government sector represents a basic international trend since the eruption of global financial crisis, we simulate the paths of dynamic evolution of China＇s debt-to-GDP ratio on the basis of different scenarios of the difference between real economic growth rate and real interest rate, together with the NPL ratio of banks. Result indicates that in the coming two decades, the leverage ratio of China＇s government sector will continue to rise and will not converge. Hiking leverage ratio, growing debt burden and rising non-performing assets present major financial risks facing China for a certain period of time in the future. Under the premise of supply-side structural reforms and in tandem with the efforts of the real economy to reduce overcapacity, inventory and eliminate zombie firms, we suggest that China focus on disposing of non-performing assets and steadily deleverage through the implementation of integrated strategies to prevent debt problems from triggering systemic financial crisis.展开更多
This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) ...This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.展开更多
This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency ...This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.展开更多
The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this d...The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.展开更多
The study aims to investigate how relying on short-term debt may help Chinese listed firms to make efficient investment decisions and reduce overinvestment problem for low-growth firms.The study uses a large set of pa...The study aims to investigate how relying on short-term debt may help Chinese listed firms to make efficient investment decisions and reduce overinvestment problem for low-growth firms.The study uses a large set of panel data of nonfinancial Chinese listed firms over the period 2007–2017 and,using the robust twostage generalized method of moments,which is robust to unobserved heterogeneity of individual firms and addresses endogeneity issues.Findings show a positive relationship between growth and investment;this association is enhanced by leverage,especially for high-growth firms.This supports the view that short-term debt helps Chinese firms to make optimal use of leverage and therefore make better investment decisions.Furthermore,the results reveal that leverage plays a disciplining and monitoring role to reduce overinvestment incentive for low-growth firms.Overall,the study suggests that shareholders should consider short-term debt to mitigate the debt overhang problem and restrict the opportunistic behavior of managers,which can lead to efficient investment decisions.It also provides foreign investors insights about capital structure in China,and how it can help them make better investment decisions.展开更多
This study addresses the role of R&D leverage in SMEs’performance creation.The authors do so by considering SMEs’high resource dependence due to isomorphism.We propose that R&D leverage,with a presence of dy...This study addresses the role of R&D leverage in SMEs’performance creation.The authors do so by considering SMEs’high resource dependence due to isomorphism.We propose that R&D leverage,with a presence of dynamic capabilities,plays a moderating role in the relation between resource investments and performance.This study,which focused on Taiwan’s SMEs,conducts a questionnaire survey using the hierarchical sampling technique,across various industries and geographic areas in Taiwan.The empirical findings reveal that R&D leverage as an essential leveler in resource management enhances resource advantages.展开更多
The paper investigates the determinants of capital structure in Nigerian listed insurance firms using data obtained from annual report of the sampled firms for the period of 2001-2010. It used five explanatory variabl...The paper investigates the determinants of capital structure in Nigerian listed insurance firms using data obtained from annual report of the sampled firms for the period of 2001-2010. It used five explanatory variables to measure their effects on debt ratio. Multiple regression is employed as a tool of analysis. The result reveals that all the explanatory variables have statistically and significantly influenced the explained variable. The results approve the prediction of pecking order theory in the case of profitability and trade-off theory in case of tangibility variables. The growth variable supports the agency theory hypothesis whereas size variable confirms to the asymmetry of information theory. It is therefore recommended that the management of listed insurance firms in Nigeria should always consider their positions using these capital structure determinants as important inputs before embarking on debt financing decision.展开更多
There are little-noticed points in the plane, which are artifacts of linear regression. The points, which are called pivot points, are the intersections of sets of regression lines. We derive the coordinates of the pi...There are little-noticed points in the plane, which are artifacts of linear regression. The points, which are called pivot points, are the intersections of sets of regression lines. We derive the coordinates of the pivot point and explain its sources. We show how a pivot point arises in a certain notable data set, which has been analyzed often for points of high leverage. We obtain the application of pivot points that shortens calculations when updating a set of bivariate observations by adding a new point.展开更多
文摘This study aims to analyze the influence of leverage,firm size,firm value,and managerial ownership on tax aggressiveness in technology sector companies listed on the Indonesia Stock Exchange.Tax aggressiveness has become a critical issue in corporate financial management practices,particularly in the context of optimizing tax burdens through strategies that remain within legal boundaries.The study adopts a quantitative approach using panel data regression methods.Data processing and analysis were conducted using EViews version 12.The research sample consists of 13 technology sector companies selected through purposive sampling,with an observation period spanning five years(2019-2023),resulting in a total of 65 observations.The analysis results indicate that leverage and firm value have a negative effect on tax aggressiveness.Conversely,managerial ownership is found to have a positive effect on tax aggressiveness.Meanwhile,firm size does not show a significant influence on tax aggressiveness.
基金sponsored by National Natural Science Foundation of China(No.7007206).
文摘By applying the theory of the relationship between microeconomic leverages and corporate risks,this paper discusses methods that the IPOs should choose to dispose the trademark with their parent companies so as to go public with a high issue price.By analyzing their microeconomic leverage effects,this paper points out that each method has its specific effect on risks and the IPOs should choose the method according to its risk factors,existing assets and financial structure to gain risk income and decrease risk losses.
基金supported by the National Basic Research Program of China (No.2011CB707004)the National Natural Science Foundation of China (No.61179054)
文摘Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation to continue in the presence of fault measurement.Affected by satellite geometry, the leverage of each measurement in position solution may differ greatly.However, the conventional RAIM FI methods are generally based on maximum likelihood of ranging error for different measurements, thereby causing a major decrease in the probability of correct identification for the fault measurement with high leverage.In this paper, the impact of leverage on the fault identification is analyzed.The leveraged RAIM fault identification（L-RAIM FI） method is proposed with consideration of the difference in leverage for each satellite in view.Furthermore,the theoretical probability of correct identification is derived to evaluate the performance of L-RAIM FI method.The experiments in various typical scenarios demonstrate the effectiveness of L-RAIM FI method over conventional FI methods in the probability of correct identification for the fault with high leverage.
文摘Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.
文摘This article discusses the problem of the detection of influential cases in nonlinear reproductive dispersion models (NRDM). A diagnostic based on case\|deletion approach in estimating equations is proposed. The relationships between the generalized leverage defined by Wei et al. in 1998, statistical curvature, and the local influence of the response vector perturbations are investigated in NRDM. Two numerical examples are given to illustrate the results.
文摘Based on the latest macro financial data, this paper estimates China＇ s overall leverage ratio and sector-specific leverage ratios for households, non-financial enterprises, government and financial institutions. It is noted with particular emphasis that the tendency of non-financial enterprises to increase leverage has further intensified instead of abated, which warrants our great attention. Considering that increasing leverage of government sector represents a basic international trend since the eruption of global financial crisis, we simulate the paths of dynamic evolution of China＇s debt-to-GDP ratio on the basis of different scenarios of the difference between real economic growth rate and real interest rate, together with the NPL ratio of banks. Result indicates that in the coming two decades, the leverage ratio of China＇s government sector will continue to rise and will not converge. Hiking leverage ratio, growing debt burden and rising non-performing assets present major financial risks facing China for a certain period of time in the future. Under the premise of supply-side structural reforms and in tandem with the efforts of the real economy to reduce overcapacity, inventory and eliminate zombie firms, we suggest that China focus on disposing of non-performing assets and steadily deleverage through the implementation of integrated strategies to prevent debt problems from triggering systemic financial crisis.
基金Project (No. 28-05-03-03) supported by the Yildiz Technical University Research Fund, Turkey
文摘This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.
基金Project(13&ZD169)supported by the Major Program of the National Social Science Foundation of ChinaProject(2016zzts009)supported by Doctoral Students Independent Explore Innovation Project of Central South University,China+3 种基金Project(13YJAZH149)supported by the Social Science Foundation of Ministry of Education of ChinaProject(2015JJ2182)supported by the Social Science Foundation of Hunan Province,ChinaProject(71573282)supported by the National Natural Science Foundation of ChinaProject(15K133)supported by the Educational Commission of Hunan Province of China
文摘This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.
基金This work was supported partially by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)Support Program(IITP-2024-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.
基金This article is supported by Liaoning provincial key project"Research on the development of digitalized rural inclusive financial service systems in Liaoning province"(Project number,LN2020Z03).
文摘The study aims to investigate how relying on short-term debt may help Chinese listed firms to make efficient investment decisions and reduce overinvestment problem for low-growth firms.The study uses a large set of panel data of nonfinancial Chinese listed firms over the period 2007–2017 and,using the robust twostage generalized method of moments,which is robust to unobserved heterogeneity of individual firms and addresses endogeneity issues.Findings show a positive relationship between growth and investment;this association is enhanced by leverage,especially for high-growth firms.This supports the view that short-term debt helps Chinese firms to make optimal use of leverage and therefore make better investment decisions.Furthermore,the results reveal that leverage plays a disciplining and monitoring role to reduce overinvestment incentive for low-growth firms.Overall,the study suggests that shareholders should consider short-term debt to mitigate the debt overhang problem and restrict the opportunistic behavior of managers,which can lead to efficient investment decisions.It also provides foreign investors insights about capital structure in China,and how it can help them make better investment decisions.
文摘This study addresses the role of R&D leverage in SMEs’performance creation.The authors do so by considering SMEs’high resource dependence due to isomorphism.We propose that R&D leverage,with a presence of dynamic capabilities,plays a moderating role in the relation between resource investments and performance.This study,which focused on Taiwan’s SMEs,conducts a questionnaire survey using the hierarchical sampling technique,across various industries and geographic areas in Taiwan.The empirical findings reveal that R&D leverage as an essential leveler in resource management enhances resource advantages.
文摘The paper investigates the determinants of capital structure in Nigerian listed insurance firms using data obtained from annual report of the sampled firms for the period of 2001-2010. It used five explanatory variables to measure their effects on debt ratio. Multiple regression is employed as a tool of analysis. The result reveals that all the explanatory variables have statistically and significantly influenced the explained variable. The results approve the prediction of pecking order theory in the case of profitability and trade-off theory in case of tangibility variables. The growth variable supports the agency theory hypothesis whereas size variable confirms to the asymmetry of information theory. It is therefore recommended that the management of listed insurance firms in Nigeria should always consider their positions using these capital structure determinants as important inputs before embarking on debt financing decision.
文摘There are little-noticed points in the plane, which are artifacts of linear regression. The points, which are called pivot points, are the intersections of sets of regression lines. We derive the coordinates of the pivot point and explain its sources. We show how a pivot point arises in a certain notable data set, which has been analyzed often for points of high leverage. We obtain the application of pivot points that shortens calculations when updating a set of bivariate observations by adding a new point.
