Does ownership structure matter in explaining derivatives' use policy in French listed firms

Boubaker, Sabri; Mefteh, Salma; Shaikh, Junaid M.

doi:10.1504/ijmfa.2010.033291

Cited by 10 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the extent that ownership structure also particularly shapes firms' decisions regarding hedging activities, Boubaker et al (2010) determined the effect of ownership structure on the uses of derivatives in a sample data of 262 French non-financial firms listed on the Paris Stock Exchange, spanning the period, 1999-2000. They found that firms' ownership structure had a significant impact on firms' decisions concerning derivatives' usage.…”

Section: Derivatives Corporate Governance and Risk Mitigationmentioning

confidence: 99%

Corporate Derivatives and Ownership Concentration: Empirical Evidence of Non-Financial Firms Listed on Pakistan Stock Exchange

Butt¹,

Nazir

Arshad

et al. 2018

JRFM

View full text Add to dashboard Cite

Risk management has been gaining tremendous fame for the last couple of years. Firms in developed and developing countries are facing a variety of risks, i.e., foreign exchange risk, interest rate risk, commodity price risk, and equity risk. It calls for such hedging techniques that mitigate this risk level, thus, allowing corporations to enjoy a solid return. This paper draws attention to a new determinant of hedging, i.e., the role of ownership concentration in risk management using derivative instruments. For this purpose, a sample data of 101 non-financial firms listed on the Pakistan Stock Exchange (PSX) for six years, ranging from 2010-2016, is used. The Mann-Whitney test for difference in users and no-users is applied along with logistic regression to check the effect of ownership concentration on derivative usage. The finding of this study reveals that concentrated owners are less likely to use derivatives for hedging purposes due to concentrated owners' interests (top five shareholders & largest shareholder, family owners). Whereas, executives are more likely to engage in the use of derivatives to increase the value of their stocks. However, associated companies are significantly less involved in hedging activities. These results are extremely advantageous for policymakers in corporations to create a more stable corporate environment.

show abstract

Section: Derivatives Corporate Governance and Risk Mitigationmentioning

confidence: 99%

Corporate Derivatives and Ownership Concentration: Empirical Evidence of Non-Financial Firms Listed on Pakistan Stock Exchange

Butt¹,

Nazir

Arshad

et al. 2018

JRFM

View full text Add to dashboard Cite

show abstract

“…The result of such a simulation of collective decision-making is obtained as the value averaged over the ensemble, but naturally, some questions are arising in that regarding the averaging technique. In the more common case, each agent may be represented as the set of some unified knowledge units or "hypothesis" [37], each of which is simple and interpretable, what one gives the opportunity to unite different kinds of knowledge and to use generalized algorithms for knowledge processing.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Organizational Knowledge Transformation and Selforganizing Knowledge Representation Model

Vishwanath¹

2023

Preprint

View full text Add to dashboard Cite

The purpose of the paper is development of a conceptual model for the representation of knowledge as an activeintellectual substance and, on this basis, study of metaphysics of knowledge transformation process being produced both individuallyand collectively in the practice of organizations. The first principle of knowledge engineering, as Edward Albert Feigenbaum noted,says that the power in solving problems that an intellectual subject (person or machine) manifests in the process of activity dependsprimarily on its knowledge base, and only secondly on the methods of inference used. Strength is hidden in knowledge. The processof producing knowledge is permanent and does not depend on whether an individual is going to use this knowledge or not.Knowledge constantly produces new knowledge regardless of the owner's desire. Besides that, knowledge can’t arise from nothing,but always – from some knowledge obtained earlier. As well as the intelligence, knowledge is an emergent instance arising from thecollective interaction of a lot of intellectual atomic elements of knowledge (knowledge quanta). Idiosyncrasy of this interaction isexpressed precisely in the creation of new knowledge.

show abstract

“…Reference [44] proposed a diagnostic system for predicting heart disease using Multi-Layer Perceptron Neural network (MLP) with back propagation as the training algorithm. The performance of the developed system was evaluated based on sensitivity, specificity, precision and accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Review of Data Mining and Machine Learning Techniques for Predicting Heart Disease

Vishwanath¹

2023

Preprint

View full text Add to dashboard Cite

: Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart diseasediagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostictools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequatepreventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors.Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures areinadequate. In today’s digital world, several clinical decision support systems on heart disease prediction have been developed bydifferent scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decisionsupport systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques.Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widelyemployed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in thecreation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporatemultiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.

show abstract

Does ownership structure matter in explaining derivatives' use policy in French listed firms

Cited by 10 publications

References 27 publications

Corporate Derivatives and Ownership Concentration: Empirical Evidence of Non-Financial Firms Listed on Pakistan Stock Exchange

Corporate Derivatives and Ownership Concentration: Empirical Evidence of Non-Financial Firms Listed on Pakistan Stock Exchange

Organizational Knowledge Transformation and Selforganizing Knowledge Representation Model

A Comprehensive Review of Data Mining and Machine Learning Techniques for Predicting Heart Disease

Contact Info

Product

Resources

About