Matching Items (3)
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- All Subjects: Measurement
- All Subjects: Market share
- Creators: Chang, Chun
- Resource Type: Text
Description
Dimensional Metrology is the branch of science that determines length, angular, and geometric relationships within manufactured parts and compares them with required tolerances. The measurements can be made using either manual methods or sampled coordinate metrology (Coordinate measuring machines). Manual measurement methods have been in practice for a long time and are well accepted in the industry, but are slow for the present day manufacturing. On the other hand CMMs are relatively fast, but these methods are not well established yet. The major problem that needs to be addressed is the type of feature fitting algorithm used for evaluating tolerances. In a CMM the use of different feature fitting algorithms on a feature gives different values, and there is no standard that describes the type of feature fitting algorithm to be used for a specific tolerance. Our research is focused on identifying the feature fitting algorithm that is best used for each type of tolerance. Each algorithm is identified as the one to best represent the interpretation of geometric control as defined by the ASME Y14.5 standard and on the manual methods used for the measurement of a specific tolerance type. Using these algorithms normative procedures for CMMs are proposed for verifying tolerances. The proposed normative procedures are implemented as software. Then the procedures are verified by comparing the results from software with that of manual measurements.
To aid this research a library of feature fitting algorithms is developed in parallel. The library consists of least squares, Chebyshev and one sided fits applied on the features of line, plane, circle and cylinder. The proposed normative procedures are useful for evaluating tolerances in CMMs. The results evaluated will be in accordance to the standard. The ambiguity in choosing the algorithms is prevented. The software developed can be used in quality control for inspection purposes.
To aid this research a library of feature fitting algorithms is developed in parallel. The library consists of least squares, Chebyshev and one sided fits applied on the features of line, plane, circle and cylinder. The proposed normative procedures are useful for evaluating tolerances in CMMs. The results evaluated will be in accordance to the standard. The ambiguity in choosing the algorithms is prevented. The software developed can be used in quality control for inspection purposes.
ContributorsVemulapalli, Prabath (Author) / Shah, Jami J. (Thesis advisor) / Davidson, Joseph K. (Committee member) / Takahashi, Timothy (Committee member) / Arizona State University (Publisher)
Created2014
Description
Since the 2008 financial crisis, the total assets managed by U.S. mutual funds have frequently hit new highs and the industry has become increasingly concentrated. In the meantime, two strategies have emerged in the American mutual fund industry: active and passive management. What factors affect the market shares of firms that adopted these two different strategies?
Building on strategic management theories, I suggest that mutual fund families that adopted active and passive management strategies tend to compete in different dimensions. Active management fund families tend to implement the product differentiation strategy, competing on “product quality” through excess-returns, innovative and differentiated fund products; passively managed fund families focus more on "price competition" by conducting an overall cost leadership strategy.
This research examines the driven factors of fund families’ market share. The results show that: the market share of actively managed fund families is more sensitive to positive impact of fund performance, while passive management firms are more sensitive to negative effect of management fees and total loads; 12b-1 expense improves the competitiveness of active fund families and thus enhance their market shares but it has negative impact on passive fund families. In addition, high turnover decreases the market share of all fund families, especially for passively managed families. The outcome reveals the latest US mutual industry orientation: products differentiation, turnover, management fee have greater impact on market share while the competition of fund performance is diminishing. The Matthew effect in US mutual fund industry is outstanding. Industrial competition dimension expands from performance and products to cost cutting.
Empirical analysis on Chinese mutual fund families is also conducted. Different from the US, there is only small number of mutual fund families targeting passive management products. The results show that the distribution channel has the largest impact on Chinese mutual fund family market share and investors are more willing to chase performance than to consider cost-efficient fund families. This study then analyses reasons behind the difference of Chinese and American mutual fund industries.
Building on strategic management theories, I suggest that mutual fund families that adopted active and passive management strategies tend to compete in different dimensions. Active management fund families tend to implement the product differentiation strategy, competing on “product quality” through excess-returns, innovative and differentiated fund products; passively managed fund families focus more on "price competition" by conducting an overall cost leadership strategy.
This research examines the driven factors of fund families’ market share. The results show that: the market share of actively managed fund families is more sensitive to positive impact of fund performance, while passive management firms are more sensitive to negative effect of management fees and total loads; 12b-1 expense improves the competitiveness of active fund families and thus enhance their market shares but it has negative impact on passive fund families. In addition, high turnover decreases the market share of all fund families, especially for passively managed families. The outcome reveals the latest US mutual industry orientation: products differentiation, turnover, management fee have greater impact on market share while the competition of fund performance is diminishing. The Matthew effect in US mutual fund industry is outstanding. Industrial competition dimension expands from performance and products to cost cutting.
Empirical analysis on Chinese mutual fund families is also conducted. Different from the US, there is only small number of mutual fund families targeting passive management products. The results show that the distribution channel has the largest impact on Chinese mutual fund family market share and investors are more willing to chase performance than to consider cost-efficient fund families. This study then analyses reasons behind the difference of Chinese and American mutual fund industries.
ContributorsLiu, Jianping (Author) / Zhu, Hongquan (Thesis advisor) / Chang, Chun (Thesis advisor) / Yan, Hong (Committee member) / Arizona State University (Publisher)
Created2018
Description随着中国居民消费占GDP比例的提升，人均GDP的增长，银行等贷款机构对个人和零售业务的长期发展，中国金融机构的个人贷款不良资产规模发生了很大的变化。居民个人对外负债主要是以债权方式体现。基于债权的一致性，对于借贷人的个人外部负债缺少特定的强制性的偿贷顺序安排，偿贷行为也不会受到法律框架的强制约束，导致借贷人在偿贷能力不足时，个体的主观意愿对于偿贷行为结果的影响显著。
既往的个人贷款不良资产管理和服务模式，均参照企业贷款不良资产管理和服务模式，体现出在标的资产的产品特点、信贷主体差异、法律完备性、对公共基础服务支撑要求等方面存在显著的不同。原有针对企业贷款不良资产的管理和服务模式在适应个人贷款不良资产管理和服务时，也就需要采用不同的方式和策略，所以，优化、提升对于借贷人的管理和服务模式就存在必要的调整和优化空间。
由于借贷人的自然人属性，区别于企业的法人属性，其生命周期自然存续期间，偿贷能力存在修复的可能，外部征信环境的改善，也会对个人贷款不良资产的产生影响。现有的个人贷款不良资产的管理和服务模式也需要做出必要的调整过和安排。
21世纪前20年，互联网/通讯/IT技术发展迅速，AI、BigData、Blockchain等技术逐渐成熟，对厘清个人贷款不良资产偿贷机制提供了必要的基础数据。在此基础上，运用日趋完备的信息不对称和行为决策等理论工具，对既有对个人贷款不良资产管理和服务模式做出优化和调整就存在可能性。
本文基于P2P个人贷款不良资产管理和服务过程中形成的数据，选取金额回款率和失联事件发生率来计量借款人的行为决策结果，通过对这两个指标在个人贷款不良资产管理和服务中呈现的规律进行分析，初步厘清了个人贷款不良资产的偿贷过程中的行为决策机制，在既有框架基础上，对个人贷款不良资产管理和服务中的资产定价优化、资产交易模式、管理和服务机构评价、不良资产策略管理策略、催收服务策略等提供提供了有益的补充。
ContributorsGuo, Dagang (Author) / Huang, Xiaochuan (Thesis advisor) / Chang, Chun (Thesis advisor) / Zhu, Ning (Committee member) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2021