Corporate Governance and Market Valuation in China

This paper studies the relationship between the governance mechanisms and the market valuation of publicly listed firms in China empirically. We construct measures for corporate governance mechanisms and measures of market valuation for all publicly listed firms on the two stock markets in China by using data from the firm’s annual reports. We then investigate how the market-valuation variables are affected by the corporate governance variables while controlling for a number of factors commonly considered in market valuation analysis. A corporate governance index is also constructed to summarize the information contained in the corporate governance variables. The index is found to have statistically and economically significant effect on market valuation. The analysis indicates that investors pay a significant premium for well-governed firms in China, benefiting firms that improve their governance mechanisms.http://deepblue.lib.umich.edu/bitstream/2027.42/39949/3/wp564.pd

An Empirical Investigation On The Post-Earnings Announcement Drift And AlgorithmicTrading

Motivated by the widespread adoption of AT in financial markets, this dissertation investigates whether algorithmic trading (AT) reduces the Post-Earnings Announcement Drift (PEAD), the financial anomaly where investors under-react to earnings information. Studies suggest AT is associated with sophisticated trading and lower transaction costs and these two factors contribute to lowering PEAD. I conjecture algorithmic traders have an incentive to profit from (and therefore reduce the presence of) PEAD; however the evidence presented in this thesis fails to show that AT attenuates this anomaly. This thesis is composed of three essays. The first essay (Chapter 2) identifies the factors that explain PEAD and asks two questions: 1) does PEAD still exist; and 2) if so, has it been fully explained. I find PEAD remains a statistically and economically significant anomaly and that low investor sophistication, arbitrage risk and transaction costs are robust but nevertheless incomplete explanations. In other words, one, albeit incomplete, explanation for PEAD is that investors with low sophistication systematically under-react to earnings information and sophisticated traders cannot fully arbitrage the mispricing due to unhedgeable idiosyncratic risks and transaction costs. The second essay (Chapter 3) considers whether AT’s association with lower transaction costs and sophisticated trading implies AT attenuates PEAD. I further conjecture that if sophisticated algorithmic traders are better at extracting trading signals from earnings information AT should also improve price discovery around earnings announcements. After controlling for other explanatory factors, however, my findings show that AT does not contribute to the attenuation of PEAD, but that it is associated with improved price discovery. The third and final essay (Chapter 4) provides an explanation for why the relation between AT and PEAD may be insignificant. I suggest order-splitting can result in the under-estimation of transaction costs (measured by effective spreads) and I argue one predominant function of AT is to execute large orders via sequences of small transactions. I therefore adjust for a potential bias in the measure of effective spreads by treating sequences of consecutive buy or sell orders as a single transaction. I then revisit a popular study which documents the market impact of AT but show that a structural increase in AT is associated with insignificant improvements in effective spreads

Moment Estimation for Nonparametric Mixture Models Through Implicit Tensor Decomposition

We present an alternating least squares type numerical optimization scheme to estimate conditionally-independent mixture models in $\mathbb{R}^n$, without parameterizing the distributions. Following the method of moments, we tackle an incomplete tensor decomposition problem to learn the mixing weights and componentwise means. Then we compute the cumulative distribution functions, higher moments and other statistics of the component distributions through linear solves. Crucially for computations in high dimensions, the steep costs associated with high-order tensors are evaded, via the development of efficient tensor-free operations. Numerical experiments demonstrate the competitive performance of the algorithm, and its applicability to many models and applications. Furthermore we provide theoretical analyses, establishing identifiability from low-order moments of the mixture and guaranteeing local linear convergence of the ALS algorithm

A study of residual stresses and their effect on thermo mechanical fatigue in complex geometries

It is known that residual stresses within engine components, such as turbine housings, can combine with service generated stresses and cause unexpected failures during operation, therefore it is important that all the stresses (residual and induced, compressive and tensile) are fully characterised and understood. The use of neutrons as a tool to measure the strains within a material is well established; however, applying this technique to complex engineering components can prove challenging. This research investigates the measurement of residual stresses in complex geometries found within the turbine housing component of a turbocharger using neutron diffraction. The effect of various production methods on residual stress distributions is also explored. Successful strain measurements were taken using the Engin-X instrument at the ISIS spallation source from three turbine housings selected from various stages in the manufacturing process, allowing a study of the effect of heat treatment and machining on stress magnitudes and direction. The turbine housing consists of various sections greater than the maximum 60mm path length of the neutrons, therefore path lengths must be carefully chosen to achieve acceptable neutron count rates. Engin-X benefits from an automated experimental setup to make the selection of this limited path length easier on complex shapes. The turbine housings were mounted on to a positioning table allowing translation in X, Y, Z directions and also rotation in , . Each of the housings were scanned using laser scanners and this in conjunction with the virtual path length measurement software SScanSS allowed automated measurements of acceptable path lengths to be made. On this occasion measurements in one principal direction were measured and the correct measurement methodology established. Continuation of this work was then carried out on SALSA at the ILL reactor source. Measurements were made on turbine housings, one as cast and one heat treated. The internal divider wall of each turbine housing was examined as this is an area where crack initiation can occur. The results showed that, heat treatment can reduce compressive residual stresses. However, compressive stresses are thought to slow the onset of crack initiation and could be beneficial in the material, this will be investigated further in future work. It is hoped that this information will be used to improve production methods and result in improved simulation methodologies to allow accurate predictions of thermal fatigue and fracture locations to be established. The authors wish to thank Dr Jon James (Open University) for his help in setting up the experiment and for the use of the SScanSS software

Spectral redemption: clustering sparse networks

Spectral algorithms are classic approaches to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when other algorithms such as belief propagation can do so. Here we introduce a new class of spectral algorithms based on a non-backtracking walk on the directed edges of the graph. The spectrum of this operator is much better-behaved than that of the adjacency matrix or other commonly used matrices, maintaining a strong separation between the bulk eigenvalues and the eigenvalues relevant to community structure even in the sparse case. We show that our algorithm is optimal for graphs generated by the stochastic block model, detecting communities all the way down to the theoretical limit. We also show the spectrum of the non-backtracking operator for some real-world networks, illustrating its advantages over traditional spectral clustering.Comment: 11 pages, 6 figures. Clarified to what extent our claims are rigorous, and to what extent they are conjectures; also added an interpretation of the eigenvectors of the 2n-dimensional version of the non-backtracking matri

Corporate Governance and Market Valuation in China

This paper studies the relationship between the governance mechanisms and the market valuation of publicly listed firms in China empirically. We construct measures for corporate governance mechanisms and measures of market valuation for all publicly listed firms on the two stock markets in China by using data from the firm’s annual reports. We then investigate how the market-valuation variables are affected by the corporate governance variables while controlling for a number of factors commonly considered in market valuation analysis. A corporate governance index is also constructed to summarize the information contained in the corporate governance variables. The index is found to have statistically and economically significant effect on market valuation. The analysis indicates that investors pay a significant premium for well-governed firms in China, benefiting firms that improve their governance mechanisms.Corporate governance mechanisms, market valuation, corporate governance index, corporate governance premium

Margin-closed vector autoregressive time series models

Conditions are obtained for a Gaussian vector autoregressive time series of order $k$, VAR($k$), to have univariate margins that are autoregressive of order $k$ or lower-dimensional margins that are also VAR($k$). This can lead to $d$-dimensional VAR($k$) models that are closed with respect to a given partition $\{S_1,\ldots,S_n\}$ of $\{1,\ldots,d\}$ by specifying marginal serial dependence and some cross-sectional dependence parameters. The special closure property allows one to fit the sub-processes of multivariate time series before assembling them by fitting the dependence structure between the sub-processes. We revisit the use of the Gaussian copula of the stationary joint distribution of observations in the VAR($k$) process with non-Gaussian univariate margins but under the constraint of closure under margins. This construction allows more flexibility in handling higher-dimensional time series and a multi-stage estimation procedure can be used. The proposed class of models is applied to a macro-economic data set and compared with the relevant benchmark models.Comment: 31 page