The Experts below are selected from a list of 1770 Experts worldwide ranked by ideXlab platform
Luc Laeven - One of the best experts on this subject based on the ideXlab platform.
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pension reform ownership structure and corporate governance evidence from a natural experiment
Review of Financial Studies, 2009Co-Authors: Mariassunta Giannetti, Luc LaevenAbstract:Sweden offers a unique natural experiment to analyze the effects of institutionalized saving on the ownership structure, corporate governance, and firm performance. The Swedish pension reform increased the stock market participation of pension funds, causing a significant reshuffling in the ownership of pension funds. We show that the effects of institutional investment on firm performance depend on the industry structure of pension funds. Firm valuation improves if public pension funds and large independent private pension funds increase their shareholdings. Additionally, Controlling shareholders appear reluctant to Relinquish Control and the Control premium increases if public pension funds acquire shares. {JEL G3, G23) A large literature in corporate finance analyzes the effects of ownership on firm performance. Thus far, this literature has not been successful in establishing whether institutional ownership enhances firm value. Partly, this is because ownership and performance are jointly determined and an increase in institutional ownership may be positively correlated with performance merely because investors select firms that they expect to perform better. It is thus impossible to draw conclusions about causal relations simply by saturating firm performance regressions with a large number of firm characteristics in addition
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pension reform ownership structure and corporate governance evidence from a natural experiment
2007Co-Authors: Mariassunta Giannetti, Luc LaevenAbstract:Sweden offers a unique natural experiment to analyze the microeconomic effects of institutionalized saving on ownership structure, corporate governance and performance of listed companies. First, the Swedish pension reform increased the participation of pension funds in the domestic stock market and caused a significant reshuffling in the ownership of the existing pension funds. Second, the availability of detailed data on firm ownership allows us to document the effects of the pension reform. We show that the effects of institutional investment on firm performance depend on the industry structure of pension funds. In particular, we find that firm valuation improves if large independent private pension funds and public pension funds increase their equity stakes in the firm, but not if smaller pension funds and pension funds related to financial institutions and industrial groups increase their shareholdings. Additionally, Controlling shareholders appear reluctant to Relinquish Control and the Control premium increases if public pension funds acquire shares.
Mohammed Omran - One of the best experts on this subject based on the ideXlab platform.
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POST-PRIVATIZATION CORPORATE GOVERNANCE AND FIRM PERFORMANCE: THE ROLE OF PRIVATE OWNERSHIP CONCENTRATION, IDENTITY AND BOARD COMPOSITION
2009Co-Authors: Mohammed OmranAbstract:We examine and analyze the post-privatization corporate governance of a sample of 52 newly privatized Egyptian firms over a period of 10 years, from 1995 to 2005. We look at the ownership structure that results from privatization and its evolution; the determinants of private ownership concentration; and the impact of private ownership concentration, identity and board composition on firm performance. We find that the state gives up Control over time to the private sector, but still Controls, on average, more than 35 percent of these firms. We also document a trend in private ownership concentration over time, mostly to the benefit of foreign investors. Firm size, sales growth, industry affiliation, and timing and method of privatization seem to play a key role in determining private ownership concentration. Ownership concentration and ownership identity, in particular foreign investors, prove to have a positive impact on firm performance, while employee ownership concentration has a negative one. The higher proportion of outside directors and the change in the board composition following privatization have a positive effect on firm performance. These results could have some important policy implications where private ownership by foreign investors seems to add more value to firms, while selling state-owned enterprises (SOEs) to employees is not recommended. Also, the state is highly advised to Relinquish Control and allow for changes in the board of directors following privatization as changing ownership, per se, might not have a positive impact on firm performance unless it is coupled with a new management style.
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Post-privatization corporate governance and firm performance: The role of private ownership concentration, identity and board composition
Journal of Comparative Economics, 2009Co-Authors: Mohammed OmranAbstract:Abstract We examine and analyze the post-privatization corporate governance of a sample of 52 newly privatized Egyptian firms over a period of 10 years, from 1995 to 2005. We look at the ownership structure that results from privatization and its evolution; the determinants of private ownership concentration; and the impact of private ownership concentration, identity and board composition on firm performance. We find that the state gives up Control over time to the private sector, but still Controls, on average, more than 35% of these firms. We also document a trend in private ownership concentration over time, mostly to the benefit of foreign investors. Firm size, sales growth, industry affiliation, and timing and method of privatization seem to play a key role in determining private ownership concentration. Ownership concentration and ownership identity, in particular foreign investors, prove to have a positive impact on firm performance, while employee ownership concentration has a negative one. The higher proportion of outside directors and the change in the board composition following privatization have a positive effect on firm performance. These results could have some important policy implications where private ownership by foreign investors seems to add more value to firms, while selling state-owned enterprises (SOEs) to employees is not recommended. Also, the state is highly advised to Relinquish Control and allow for changes in the board of directors following privatization as changing ownership, per se , might not have a positive impact on firm performance unless it is coupled with a new management style.
Maske Harshal - One of the best experts on this subject based on the ideXlab platform.
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Learning, inference, and Control for construction robots and spatiotemporal processes
2020Co-Authors: Maske HarshalAbstract:Expert operators of real world robots, especially constructions robots, develop expertise from years of training and experience. In the absence of such experts, these robots are operated by novice operators, and this adversely affects the productivity. On the other hand, safety concerns and the nature of the operating environment limits the possibility of automating these robots. This thesis proposes a solution by considering a problem setting in which the robot learns a policy from experts to train novice human operators. Formally, this is posed as the problem of learning instructional policy from demonstration, that maps the state of the robot to an instruction for a human operator. Existing methods learn policy from demonstration, however such policies do not relate to the human operator's action space and hence cannot be used to generate instructions for novice operators. We introduce action primitives that address this challenge of mapping continuous state action trajectories to human parse-able and executable instructions. Construction tasks are complex as they consist of several subtasks with stochastic transitions. For such tasks, existing approaches learn policy for component subtask and then rely either on predefined decomposition or heuristics to generate policy for the entire task. To overcome this limitation, and to generate instructions for an entire construction task, this thesis proposes learning of a structured probabilistic model for instructional policy. This model utilizes hierarchy of Markov chains that incrementally captures the number of subtasks as well as their transitions. Switching between the subtasks is inferred using a likelihood rate based inference approach proposed in this thesis. Instructional policy model is tested based on a Controlled group study involving 113 participants, who learn to perform the truck loading task on a hydraulic actuated scaled excavator robot. Further this thesis investigates shared Control design for construction robots. Existing work has established that shared Control can improve cycle times in nominal conditions. However, these methods can be too slow to Relinquish Control in off-nominal cases, when the operator needs to deviate from the nominally optimal trajectory due to unforeseen obstacles or other uncertainties. With an objective to incorporate such capability, this thesis proposes a new shared Control technique that utilizes the operator's intent to quickly Relinquish Control in off-nominal conditions. Theoretical results with performance guarantees and improved obstacle reaction time are presented. Proposed design has been experimentally validated on Zermelo's navigation problem. The last part of the thesis introduces kernel observer for learning and inference of large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution. This work considers the problem of estimating the latent state of a spatiotemporally evolving continuous function using very few sensor measurements. The model consists of a dynamical systems prior over temporal evolution of weights of a kernel model. Theoretical results provide sufficient conditions on the number and spatial location of sensors required to guarantee state recovery. A lower bound on the minimum number of sensors required to robustly infer the hidden states is also derived. Finally, theoretical results for randomly selecting sensing or sampling locations based on the predictive kernel observer model are presented. Our approach outperforms state-of-the-art kernel based machine learning methods in numerical experiments on real world datasets.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste
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Learning, inference, and Control for construction robots and spatiotemporal processes
2018Co-Authors: Maske HarshalAbstract:Expert operators of real world robots, especially constructions robots, develop expertise from years of training and experience. In the absence of such experts, these robots are operated by novice operators, and this adversely affects the productivity. On the other hand, safety concerns and the nature of the operating environment limits the possibility of automating these robots. This thesis proposes a solution by considering a problem setting in which the robot learns a policy from experts to train novice human operators. Formally, this is posed as the problem of learning instructional policy from demonstration, that maps the state of the robot to an instruction for a human operator. Existing methods learn policy from demonstration, however such policies do not relate to the human operator's action space and hence cannot be used to generate instructions for novice operators. We introduce action primitives that address this challenge of mapping continuous state action trajectories to human parse-able and executable instructions. Construction tasks are complex as they consist of several subtasks with stochastic transitions. For such tasks, existing approaches learn policy for component subtask and then rely either on predefined decomposition or heuristics to generate policy for the entire task. To overcome this limitation, and to generate instructions for an entire construction task, this thesis proposes learning of a structured probabilistic model for instructional policy. This model utilizes hierarchy of Markov chains that incrementally captures the number of subtasks as well as their transitions. Switching between the subtasks is inferred using a likelihood rate based inference approach proposed in this thesis. Instructional policy model is tested based on a Controlled group study involving 113 participants, who learn to perform the truck loading task on a hydraulic actuated scaled excavator robot. Further this thesis investigates shared Control design for construction robots. Existing work has established that shared Control can improve cycle times in nominal conditions. However, these methods can be too slow to Relinquish Control in off-nominal cases, when the operator needs to deviate from the nominally optimal trajectory due to unforeseen obstacles or other uncertainties. With an objective to incorporate such capability, this thesis proposes a new shared Control technique that utilizes the operator's intent to quickly Relinquish Control in off-nominal conditions. Theoretical results with performance guarantees and improved obstacle reaction time are presented. Proposed design has been experimentally validated on Zermelo's navigation problem. The last part of the thesis introduces kernel observer for learning and inference of large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution. This work considers the problem of estimating the latent state of a spatiotemporally evolving continuous function using very few sensor measurements. The model consists of a dynamical systems prior over temporal evolution of weights of a kernel model. Theoretical results provide sufficient conditions on the number and spatial location of sensors required to guarantee state recovery. A lower bound on the minimum number of sensors required to robustly infer the hidden states is also derived. Finally, theoretical results for randomly selecting sensing or sampling locations based on the predictive kernel observer model are presented. Our approach outperforms state-of-the-art kernel based machine learning methods in numerical experiments on real world datasets
Mariassunta Giannetti - One of the best experts on this subject based on the ideXlab platform.
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pension reform ownership structure and corporate governance evidence from a natural experiment
Review of Financial Studies, 2009Co-Authors: Mariassunta Giannetti, Luc LaevenAbstract:Sweden offers a unique natural experiment to analyze the effects of institutionalized saving on the ownership structure, corporate governance, and firm performance. The Swedish pension reform increased the stock market participation of pension funds, causing a significant reshuffling in the ownership of pension funds. We show that the effects of institutional investment on firm performance depend on the industry structure of pension funds. Firm valuation improves if public pension funds and large independent private pension funds increase their shareholdings. Additionally, Controlling shareholders appear reluctant to Relinquish Control and the Control premium increases if public pension funds acquire shares. {JEL G3, G23) A large literature in corporate finance analyzes the effects of ownership on firm performance. Thus far, this literature has not been successful in establishing whether institutional ownership enhances firm value. Partly, this is because ownership and performance are jointly determined and an increase in institutional ownership may be positively correlated with performance merely because investors select firms that they expect to perform better. It is thus impossible to draw conclusions about causal relations simply by saturating firm performance regressions with a large number of firm characteristics in addition
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pension reform ownership structure and corporate governance evidence from a natural experiment
2007Co-Authors: Mariassunta Giannetti, Luc LaevenAbstract:Sweden offers a unique natural experiment to analyze the microeconomic effects of institutionalized saving on ownership structure, corporate governance and performance of listed companies. First, the Swedish pension reform increased the participation of pension funds in the domestic stock market and caused a significant reshuffling in the ownership of the existing pension funds. Second, the availability of detailed data on firm ownership allows us to document the effects of the pension reform. We show that the effects of institutional investment on firm performance depend on the industry structure of pension funds. In particular, we find that firm valuation improves if large independent private pension funds and public pension funds increase their equity stakes in the firm, but not if smaller pension funds and pension funds related to financial institutions and industrial groups increase their shareholdings. Additionally, Controlling shareholders appear reluctant to Relinquish Control and the Control premium increases if public pension funds acquire shares.
Alain Deccache - One of the best experts on this subject based on the ideXlab platform.
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reconsidering patient empowerment in chronic illness a critique of models of self efficacy and bodily Control
Social Science & Medicine, 2008Co-Authors: Isabelle Aujoulat, R Marcolongo, Leopoldo Bonadiman, Alain DeccacheAbstract:Studies that focus on patient empowerment tend to address more specifically two issues of patients' experience of illness: managing regimens and relating to health-care providers. Other aspects of illness experience, such as coming to terms with disrupted identities, tend to be overlooked. The outcome of empowerment is therefore usually referred to as achieving self-efficacy, mastery and Control. We conducted an inductive exploratory study, based on individual in-depth interviews with 40 chronically ill patients in Belgium and Italy, in order to understand the process of empowerment as it may occur in patients whose experience of illness has at some point induced a feeling of powerlessness, which we conceptualised as a threat to their senses of security and identity. Our findings show that empowerment and Control are not one and the same thing. We describe patient empowerment as a process of personal transformation which occurs through a double process of (i) "holding on" to previous self-representations and roles and learning to Control the disease and treatment, so as to differentiate one's self from illness on the one hand, and on the other hand (ii) "letting go", by accepting to Relinquish Control, so as to integrate illness and illness-driven boundaries as being part of a reconciled self. Whereas the process of separating identities ("holding on") was indeed found to be linked to efforts aimed at taking Control and maintaining or regaining a sense of mastery, the process of reconciling identities ("letting go") was found to be linked to a need for coherence, which included a search for meaning and the acceptance that not everything is Controllable. We argue that the process of Relinquishing Control is as central to empowerment as is the process of gaining Control. As a "successful" process of empowerment occurs when patients come to terms with their threatened security and identity, not only with their treatment, it may be facilitated by health-care providers through the use of narratives.