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摘要翻译：
我们研究了一个完整的治理、多元化和价值创造变量样本数据库的可能性，提供了一个很好的方法来重构缺失的部分，以获得一个完整的样本，用于检验所有权-结构/多元化关系。它由一个基于小波的动态过程组成。通过与常用的神经网络方法的比较，证明了该方法的有效性。实证检验是在一组法国公司上进行的。
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英文标题：
《Wavelet-Based Prediction for Governance, Diversification and Value
Creation Variables》
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作者：
Ines Kahloul, Anouar Ben Mabrouk and Slah-Eddine Hallara
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最新提交年份：
2010
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
We study the possibility of completing data bases of a sample of governance, diversification and value creation variables by providing a well adapted method to reconstruct the missing parts in order to obtain a complete sample to be applied for testing the ownership-structure/diversification relationship. It consists of a dynamic procedure based on wavelets. A comparison with Neural Networks, the most used method, is provided to prove the efficiency of the here-developed one. The empirical tests are conducted on a set of French firms.
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PDF链接：
https://arxiv.org/pdf/1011.5020
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