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There has been much debate in the world of academia over the valuation of conglomerates. This thesis proposes the use of the EVA theory in explaining fluctuations in conglomerates’ valuation, and we believe that ROIC, WACC, and ROIC-WACC are three indicators that to a certain extent explain these valuation fluctuations.

There has been much debate in the world of academia over the valuation of conglomerates. This thesis proposes the use of the EVA theory in explaining fluctuations in conglomerates’ valuation, and we believe that ROIC, WACC, and ROIC-WACC are three indicators that to a certain extent explain these valuation fluctuations. Through analysis of a sample containing 23 conglomerates, this thesis finds that ROIC, WACC, and ROIC-WACC exhibit positive correlation with valuation fluctuations. In the case study on Fosun, this thesis finds that ROIC-WACC is highly correlated with Fosun’s valuation fluctuations and next with ROIC. Thus this thesis conjectures that for investment companies for which investment capital is derived largely from insurance float, such as Fosun, ROIC-WACC is a better valuation tool.
ContributorsLiang, Xinjun (Author) / Chen, Hong (Thesis advisor) / Pei, Ker-Wei (Thesis advisor) / Zhu, Ning (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis – the first focuses on fraud detection at the store level, and the second focuses on fraud detection

Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis – the first focuses on fraud detection at the store level, and the second focuses on fraud detection at the merchant level by aggregating store level data to the merchant level for merchants with multiple stores. My purpose is to put the model into business operations, helping to identify fraudulent merchants at the time of transactions and thus mitigate the risk exposure of the payment acquiring businesses. The model developed in this study is distinct from existing fraud detection models in three important aspects. First, it predicts the probability of fraud at the merchant level, as opposed to at the transaction level or by the cardholders. Second, it is developed by applying machine learning algorithms and logistical regressions to all the transaction level and merchant level variables collected from real business operations, rather than relying on the experiences and analytical abilities of business experts as in the development of traditional expert systems. Third, instead of using a small sample, I develop and test the model using a huge sample that consists of over 600,000 merchants and 10 million transactions per month. I conclude this study with a discussion of the model’s possible applications in practice as well as its implications for future research.
ContributorsZhou, Ye (Author) / Chen, Hong (Thesis advisor) / Gu, Bin (Thesis advisor) / Chao, Xiuli (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Readout Integrated Circuits(ROICs) are important components of infrared(IR) imag

ing systems. Performance of ROICs affect the quality of images obtained from IR

imaging systems. Contemporary infrared imaging applications demand ROICs that

can support large dynamic range, high frame rate, high output data rate, at low

cost, size and power. Some of these applications are

Readout Integrated Circuits(ROICs) are important components of infrared(IR) imag

ing systems. Performance of ROICs affect the quality of images obtained from IR

imaging systems. Contemporary infrared imaging applications demand ROICs that

can support large dynamic range, high frame rate, high output data rate, at low

cost, size and power. Some of these applications are military surveillance, remote

sensing in space and earth science missions and medical diagnosis. This work focuses

on developing a ROIC unit cell prototype for National Aeronautics and Space Ad

ministration(NASA), Jet Propulsion Laboratory’s(JPL’s) space applications. These

space applications also demand high sensitivity, longer integration times(large well

capacity), wide operating temperature range, wide input current range and immunity

to radiation events such as Single Event Latchup(SEL).

This work proposes a digital ROIC(DROIC) unit cell prototype of 30ux30u size,

to be used mainly with NASA JPL’s High Operating Temperature Barrier Infrared

Detectors(HOT BIRDs). Current state of the art DROICs achieve a dynamic range

of 16 bits using advanced 65-90nm CMOS processes which adds a lot of cost overhead.

The DROIC pixel proposed in this work uses a low cost 180nm CMOS process and

supports a dynamic range of 20 bits operating at a low frame rate of 100 frames per

second(fps), and a dynamic range of 12 bits operating at a high frame rate of 5kfps.

The total electron well capacity of this DROIC pixel is 1.27 billion electrons, enabling

integration times as long as 10ms, to achieve better dynamic range. The DROIC unit

cell uses an in-pixel 12-bit coarse ADC and an external 8-bit DAC based fine ADC.

The proposed DROIC uses layout techniques that make it immune to radiation up to

300krad(Si) of total ionizing dose(TID) and single event latch-up(SEL). It also has a

wide input current range from 10pA to 1uA and supports detectors operating from

Short-wave infrared (SWIR) to longwave infrared (LWIR) regions.
ContributorsPraveen, Subramanya Chilukuri (Author) / Bakkaloglu, Bertan (Thesis advisor) / Kitchen, Jennifer (Committee member) / Long, Yu (Committee member) / Arizona State University (Publisher)
Created2019