The Experts below are selected from a list of 23367 Experts worldwide ranked by ideXlab platform
Yong Tan - One of the best experts on this subject based on the ideXlab platform.
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Investor Platform Choice: Herding, Platform Attributes, and Regulations
Journal of Management Information Systems, 2018Co-Authors: Yang Jiang, Xiang-bin Yan, Yi-chun Ho, Yong TanAbstract:AbstractOnline peer-to-peer (P2P) lending, one of the most successful technology-enabled initiatives in the fintech revolution, has drastically changed the way individual investors and borrowers meet and transact. While prior research has found Herding among investors at the listing level, such social behavior has been underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors’ actions when choosing which platform to invest, and if so, how various platform attributes and regulations moderate Herding behavior. We collected a novel data set from leading platforms in a large P2P lending market. Our baseline analysis reveals that Herding exists at the platform level. Using a multilevel model, we further identify several interesting moderators: the investor’s Herding behavior is accentuated by platforms’ market share and the cumulative amount funded, but attenuated by their time in operation. Finally, we find that gov...
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Investor Platform Choice: Herding, Platform Attributes, and Regulations
SSRN Electronic Journal, 2016Co-Authors: Yang Jiang, Xiang-bin Yan, Yong TanAbstract:Online peer-to-peer (P2P) lending, one of the most successful technology-enabled initiatives in the "fintech" revolution, has drastically changed the way how individual investors and borrows meet and transact. While prior research has found Herding among investors at the loan listing level, such a social behavior was underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors' action when choosing which platform to invest, and if they do, how various platform attributes moderate Herding behavior. We collect a novel data set from 127 leading platforms in a large P2P lending market. Our baseline analysis reveals that Herding exists at the platform level. Using a multilevel mixed-effect model, we further identify several interesting moderators: investors' Herding behavior is accentuated by platforms' market share and cumulative amount funded but attenuated by their time in operation. Finally, we find that government regulatory events dampen the magnitude of the Herding effect, suggesting that more information disclosure and stricter operation standards for platforms reduces the value of observational learning to P2P investors. Our findings have managerial implications for platform owners and policy makers.
Rakesh Kumar - One of the best experts on this subject based on the ideXlab platform.
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Branch and data Herding: Reducing control and memory divergence for error-tolerant GPU applications
2014Co-Authors: John Sartori, Rakesh KumarAbstract:Abstract—Control and memory divergence between threads within the same execution bundle, or warp, have been shown to cause significant performance bottlenecks for GPU applications. In this paper, we exploit the observation that many GPU applications exhibit error tolerance to propose branch and data Herding. Branch Herding eliminates control divergence by forcing all threads in a warp to take the same control path. Data Herding eliminates memory divergence by forcing each thread in a warp to load from the same memory block. To safely and efficiently support branch and data Herding, we propose a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data Herding. Our software implementation of branch Herding on NVIDIA GeForce GTX 480 improves performance by up to 34 % (13%, on average) for a suite of NVIDIA CUDA SDK and Parboil [16] benchmarks. Our hardware implementation of branch Herding improves performance by up to 55 % (30%, on average). Data Herding improves performance by up to 32 % (25%, on average). Observed output quality degradation is minimal for several applications that exhibit error tolerance, especially for visual computing applications. EDICS: Parallel Architectures and Design Techniques I
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Branch and Data Herding: Reducing Control and Memory Divergence for Error-Tolerant GPU Applications
IEEE Transactions on Multimedia, 2013Co-Authors: John Sartori, Rakesh KumarAbstract:Control and memory divergence between threads within the same execution bundle, or warp, have been shown to cause significant performance bottlenecks for GPU applications. In this paper, we exploit the observation that many GPU applications exhibit error tolerance to propose branch and data Herding. Branch Herding eliminates control divergence by forcing all threads in a warp to take the same control path. Data Herding eliminates memory divergence by forcing each thread in a warp to load from the same memory block. To safely and efficiently support branch and data Herding, we propose a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data Herding. Our software implementation of branch Herding on NVIDIA GeForce GTX 480 improves performance by up to 34% (13%, on average) for a suite of NVIDIA CUDA SDK and Parboil benchmarks. Our hardware implementation of branch Herding improves performance by up to 55% (30%, on average). Data Herding improves performance by up to 32% (25%, on average). Observed output quality degradation is minimal for several applications that exhibit error tolerance, especially for visual computing applications.
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branch and data Herding reducing control and memory divergence for error tolerant gpu applications
International Conference on Parallel Architectures and Compilation Techniques, 2012Co-Authors: John Sartori, Rakesh KumarAbstract:Control and memory divergence between threads in the same execution bundle, or warp, can significantly throttle the performance of GPU applications. We exploit the observation that many GPU applications exhibit error tolerance to propose branch and data Herding. Branch Herding eliminates control divergence by forcing all threads in a warp to take the same control path. Data Herding eliminates memory divergence by forcing each thread in a warp to load from the same memory block. To safely and efficiently support branch and data Herding, we propose a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data Herding. Our software implementation of branch Herding on NVIDIA GeForce GTX 480 improves performance by up to 34% (13%, on average) for a suite of NVIDIA CUDA SDK and Parboil [7] benchmarks. Our hardware implementation of branch Herding improves performance by up to 55% (30%, on average). Data Herding improves performance by up to 32% (25%, on average). Observed output quality degradation is minimal for several applications that exhibit error tolerance, especially for visual computing applications. For a more detailed exposition of this work, see [6].
Ivo Welch - One of the best experts on this subject based on the ideXlab platform.
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Herding among security analysts
Social Science Research Network, 2001Co-Authors: Ivo WelchAbstract:The paper shows that the buy or sell recommendations of security analysts have a significant positive influence on the recommendations of the next two analysts. This influence can be traced to short-lived information in the most recent revisions. In contrast, the influence of the prevailing consensus is not stronger if the consensus accurately forecasts subsequent stock price movements. This indicates consensus Herding consistent with models in which analysts herd based on little information. The consensus also has a stronger influence when market conditions are favorable. The resulting poorer information aggregation could cause bull markets to be intrinsically more "fragile" (e.g., Bikhchandani et al., J. Political Economy 100(5) (1992) 992-1026).
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Herding among security analysts
Journal of Financial Economics, 2000Co-Authors: Ivo WelchAbstract:The paper shows that the buy or sell recommendations of security analysts have a signi"cant positive in#uence on the recommendations of the next two analysts. This in#uence can be traced to short-lived information in the most recent revisions. In contrast, the in#uence of the prevailing consensus is not stronger if the consensus accurately forecasts subsequent stock price movements. This indicates consensus Herding consistent with models in which analysts herd based on little information. The consensus also has a stronger in#uence when market conditions are favorable. The resulting poorer information aggregation could cause bull markets to be intrinsically more ‘fragilea (e.g., Bikhchandani et al., J. Political Economy 100(5) (1992) 992}1026). ( 2000 Elsevier Science S.A. All rights reserved. JEL classixcation: G11; G14; G24
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rational Herding in financial economics
European Economic Review, 1996Co-Authors: Andrea Devenow, Ivo WelchAbstract:This paper briefly describes recent papers on economics of rational Herding in financial markets. Some models can predict perfect Herding, in which rational agents all act alike without any counterveiling force. Such Herding typically arises either from direct payoff externalities (negative externalities in bank runs; positive externalities in the generation of trading liquidity or in information acquisition), principal-agent problems (based on managerial desire to protect or signal reputation), or informational learning (cascades). The paper also provides a few pointers to related literature and suggests issues to be addressed in future research.
Yang Jiang - One of the best experts on this subject based on the ideXlab platform.
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Investor Platform Choice: Herding, Platform Attributes, and Regulations
Journal of Management Information Systems, 2018Co-Authors: Yang Jiang, Xiang-bin Yan, Yi-chun Ho, Yong TanAbstract:AbstractOnline peer-to-peer (P2P) lending, one of the most successful technology-enabled initiatives in the fintech revolution, has drastically changed the way individual investors and borrowers meet and transact. While prior research has found Herding among investors at the listing level, such social behavior has been underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors’ actions when choosing which platform to invest, and if so, how various platform attributes and regulations moderate Herding behavior. We collected a novel data set from leading platforms in a large P2P lending market. Our baseline analysis reveals that Herding exists at the platform level. Using a multilevel model, we further identify several interesting moderators: the investor’s Herding behavior is accentuated by platforms’ market share and the cumulative amount funded, but attenuated by their time in operation. Finally, we find that gov...
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Investor Platform Choice: Herding, Platform Attributes, and Regulations
SSRN Electronic Journal, 2016Co-Authors: Yang Jiang, Xiang-bin Yan, Yong TanAbstract:Online peer-to-peer (P2P) lending, one of the most successful technology-enabled initiatives in the "fintech" revolution, has drastically changed the way how individual investors and borrows meet and transact. While prior research has found Herding among investors at the loan listing level, such a social behavior was underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors' action when choosing which platform to invest, and if they do, how various platform attributes moderate Herding behavior. We collect a novel data set from 127 leading platforms in a large P2P lending market. Our baseline analysis reveals that Herding exists at the platform level. Using a multilevel mixed-effect model, we further identify several interesting moderators: investors' Herding behavior is accentuated by platforms' market share and cumulative amount funded but attenuated by their time in operation. Finally, we find that government regulatory events dampen the magnitude of the Herding effect, suggesting that more information disclosure and stricter operation standards for platforms reduces the value of observational learning to P2P investors. Our findings have managerial implications for platform owners and policy makers.
John Sartori - One of the best experts on this subject based on the ideXlab platform.
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Branch and data Herding: Reducing control and memory divergence for error-tolerant GPU applications
2014Co-Authors: John Sartori, Rakesh KumarAbstract:Abstract—Control and memory divergence between threads within the same execution bundle, or warp, have been shown to cause significant performance bottlenecks for GPU applications. In this paper, we exploit the observation that many GPU applications exhibit error tolerance to propose branch and data Herding. Branch Herding eliminates control divergence by forcing all threads in a warp to take the same control path. Data Herding eliminates memory divergence by forcing each thread in a warp to load from the same memory block. To safely and efficiently support branch and data Herding, we propose a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data Herding. Our software implementation of branch Herding on NVIDIA GeForce GTX 480 improves performance by up to 34 % (13%, on average) for a suite of NVIDIA CUDA SDK and Parboil [16] benchmarks. Our hardware implementation of branch Herding improves performance by up to 55 % (30%, on average). Data Herding improves performance by up to 32 % (25%, on average). Observed output quality degradation is minimal for several applications that exhibit error tolerance, especially for visual computing applications. EDICS: Parallel Architectures and Design Techniques I
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Branch and Data Herding: Reducing Control and Memory Divergence for Error-Tolerant GPU Applications
IEEE Transactions on Multimedia, 2013Co-Authors: John Sartori, Rakesh KumarAbstract:Control and memory divergence between threads within the same execution bundle, or warp, have been shown to cause significant performance bottlenecks for GPU applications. In this paper, we exploit the observation that many GPU applications exhibit error tolerance to propose branch and data Herding. Branch Herding eliminates control divergence by forcing all threads in a warp to take the same control path. Data Herding eliminates memory divergence by forcing each thread in a warp to load from the same memory block. To safely and efficiently support branch and data Herding, we propose a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data Herding. Our software implementation of branch Herding on NVIDIA GeForce GTX 480 improves performance by up to 34% (13%, on average) for a suite of NVIDIA CUDA SDK and Parboil benchmarks. Our hardware implementation of branch Herding improves performance by up to 55% (30%, on average). Data Herding improves performance by up to 32% (25%, on average). Observed output quality degradation is minimal for several applications that exhibit error tolerance, especially for visual computing applications.
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branch and data Herding reducing control and memory divergence for error tolerant gpu applications
International Conference on Parallel Architectures and Compilation Techniques, 2012Co-Authors: John Sartori, Rakesh KumarAbstract:Control and memory divergence between threads in the same execution bundle, or warp, can significantly throttle the performance of GPU applications. We exploit the observation that many GPU applications exhibit error tolerance to propose branch and data Herding. Branch Herding eliminates control divergence by forcing all threads in a warp to take the same control path. Data Herding eliminates memory divergence by forcing each thread in a warp to load from the same memory block. To safely and efficiently support branch and data Herding, we propose a static analysis and compiler framework to prevent exceptions when control and data errors are introduced, a profiling framework that aims to maximize performance while maintaining acceptable output quality, and hardware optimizations to improve the performance benefits of exploiting error tolerance through branch and data Herding. Our software implementation of branch Herding on NVIDIA GeForce GTX 480 improves performance by up to 34% (13%, on average) for a suite of NVIDIA CUDA SDK and Parboil [7] benchmarks. Our hardware implementation of branch Herding improves performance by up to 55% (30%, on average). Data Herding improves performance by up to 32% (25%, on average). Observed output quality degradation is minimal for several applications that exhibit error tolerance, especially for visual computing applications. For a more detailed exposition of this work, see [6].
