公募基金管理结构业绩影响研究——以“一拖多”为例

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Description
随着基金业近年来迅速发展,一位基金经理同时管理多只基金的“一拖多”模式成为了越发普遍的现象并引起广泛关注。但在学术界,这一重要行业现象尚未被充分讨论。为深入探讨基金经理“一拖多”模式的成因及业绩影响。本文搜集并整理了中国2008年到2018年的基金业数据,对该问题进行了系统探讨。首先,就基金被“一拖多”的原因看,研究发现:(1)基金公司有过度使用优秀基金经理的现象,基金经理从业时间越长、学历越高,则管理多只基金的概率越高。(2)现金流压力较小的基金更易被“一拖多”,债券型、基金的最小赎回份额较高以及个人投资者比例较低的基金现金流压力较小,被一拖多的概率更高。(3)基金公司的注册资本越高,成立时间越长,管理规模越大,其管理的基金被“一拖多”的概率就越高。 其次,本文探讨了基金经理“一拖多”的业绩影响,研究发现:(1)基金经理“一拖多”总体上降低了基金回报。(2)异质性分析显示,当基金所在企业成立年份较长、管理资产较高时,基金经理“一拖多”更易导致基金业绩回报显著下降。此外,当基金经理从业时间较短、管理的基金组合的风格集中度较低时,这一效应更加明显。(3)基金“一拖多”模式不仅通过分散经理精力降低基金回报,久经锻炼的基金经理也会利用自身经验和知识弥补甚至追回精力分散效应的损失。 最后,本文还试图研究基金经理的最优基金管理数量。研究发现:基金业绩首先会随着基金经理同时管理基金的个数增加而下降,但随着管理基金个数的进一步增加,基金业绩会有所回升。总体上,基金经理管理的基金数量在10支左右时达到收益率最劣势,当管理基金的数量在17支以上时,经验复制效应带来的收益将超过精力分散效应带来的损耗,达到效应平衡点。 本文补充了当前学术界在基金经理“一拖多”现象上的研究,并根据研究结果提出了对应的业界实务建议。
Date Created
2021
Agent

Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion

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Description

In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend the orthogonal matching pursuit method from the vector case to the matrix case. We further propose an economic version of

In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend the orthogonal matching pursuit method from the vector case to the matrix case. We further propose an economic version of our algorithm by introducing a novel weight updating rule to reduce the time and storage complexity. Both versions are computationally inexpensive for each matrix pursuit iteration and find satisfactory results in a few iterations. Another advantage of our proposed algorithm is that it has only one tunable parameter, which is the rank. It is easy to understand and to use by the user. This becomes especially important in large-scale learning problems. In addition, we rigorously show that both versions achieve a linear convergence rate, which is significantly better than the previous known results. We also empirically compare the proposed algorithms with several state-of-the-art matrix completion algorithms on many real-world datasets, including the large-scale recommendation dataset Netflix as well as the MovieLens datasets. Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.

Date Created
2014-11-30
Agent