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Three Essays on Hedge Funds
In Essay 1, we find that, on average, hedge funds decrease leverage prior to the beginning of the financial crisis, with leverage remaining below the pre-crisis levels. We also find that younger funds with lower current leverage and stricter fund governance are more likely to increase leverage following favorable performance; funds exposed to higher risk, higher management fee and higher current leverage tend to delever. Managers increase leverage in order to enhance future performance following superior returns only to be disappointed. We find mixed evidence on the performance difference between levered and unlevered funds, but levered funds do survive longer.
In essays 2, we find that the presence of the management companies in their investment region is the most important source of the risk-adjusted performance. The funds with a presence in their investment region outperform other funds by 4.2 % per year. On average, 18% of the emerging market hedge funds have delivered positive and statistically significant alpha. Funds producing significant alphas experience greater capital inflows than the remainder. Have-alpha funds that experience high investor inflows do not have higher probabilities of being classified as beta-only funds nor have worse risk-adjusted returns in the future.
In essay 3, we find that historical returns are routinely revised. About two-thirds of the hedge funds in our sample have revised their previously reported performance. On average, more than one-fifth of monthly returns were revised after being first reported. We find that positive revisions significantly outnumber negative revisions to returns of December. We also find an obvious decreasing time trend in both the number and proportion of return revisions, even after adjusting for performance report recency. We find a strong connection between return revisions and desirable fund characteristics such as strong fund governance at the overall fund level, the individual fund level, and the individual revision level. The revised funds outperform unrevised funds after revisions. Our findings suggest that correction may be a plausible explanation for the return revisions in hedge fund performance report. We have not found direct evidence that hedge fund managers manipulate returns
Improving Joint Operation System of Reservoir Groups in the Yangtze River Basin: A Legal Discussion
As China’s largest river basin, the Yangtze River Basin has the most mega reservoir groups worldwide. To protect the entire basin, the Central Government developed a system of joint operations of key reservoir groups in the Yangtze River Basin. This paper examines this joint operation system from a legal perspective and discusses its implementation as well as the challenges in practice. The following issues impede the effective implementation of the joint operation system: a lacking legal basis for the system, limitations related to the organizations that participate in the joint operations, limitations on the scope and objects of the joint operation system, and a lacking systematic structure for operation. This paper offers suggestions to improve the system
Semantic-Enhanced Image Clustering
Image clustering is an important and open-challenging task in computer
vision. Although many methods have been proposed to solve the image clustering
task, they only explore images and uncover clusters according to the image
features, thus being unable to distinguish visually similar but semantically
different images. In this paper, we propose to investigate the task of image
clustering with the help of a visual-language pre-training model. Different
from the zero-shot setting, in which the class names are known, we only know
the number of clusters in this setting. Therefore, how to map images to a
proper semantic space and how to cluster images from both image and semantic
spaces are two key problems. To solve the above problems, we propose a novel
image clustering method guided by the visual-language pre-training model CLIP,
named \textbf{Semantic-Enhanced Image Clustering (SIC)}. In this new method, we
propose a method to map the given images to a proper semantic space first and
efficient methods to generate pseudo-labels according to the relationships
between images and semantics. Finally, we propose performing clustering with
consistency learning in both image space and semantic space, in a
self-supervised learning fashion. The theoretical result of convergence
analysis shows that our proposed method can converge at a sublinear speed.
Theoretical analysis of expectation risk also shows that we can reduce the
expected risk by improving neighborhood consistency, increasing prediction
confidence, or reducing neighborhood imbalance. Experimental results on five
benchmark datasets clearly show the superiority of our new method
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