Ownership, corporate governance and liquidity in Caribbean firms

This thesis provides the first evidence on how ownership structure and corporate governance relate to stock liquidity in the Caribbean. Based on panel data of 71 firms from three selected Caribbean markets − Barbados, Jamaica, and Trinidad & Tobago − results show that firms with concentrated ownership are associated with lower liquidity. The identity of the largest shareholder also matters: family firms and firms with foreign holding companies are more liquid than government firms. Although the second largest shareholding does not appear to matter to liquidity, there is some evidence showing that firms with foreign holding companies as the second largest shareholder are less liquid. Caribbean firms suffer from poor corporate governance but this study is unable to establish a significant relationship between corporate governance and liquidity.

CFO inside debt and corporate innovation

This thesis examines the importance of CFO incentives on the value maximization of firm. It examines the association between CFO inside debt compensation i.e., CFO pensions and deferred compensation, and investment in corporate innovation. It finds that instead of encouraging innovation, CFO inside debt appears to have a dampening effect on investment in innovation.

A computer image analysis system for microvessel density measurement in solid tumours

Microvessel density (MVD) is a widely used surrogate measure of angiogenesis in pathological specimens and tumour models. Measurement of MVD can be achieved by several methods. Automation of counting methods aims to increase the speed, reliability and reproducibility of these techniques. The image analysis system described here enables MVD measurement to be carried out with minimal expense in any reasonably equipped pathology department or laboratory. It is demonstrated that the system translates easily between tumour types which are suitably stained with minimal calibration. The aim of this paper is to offer this technique to a wider field of researchers in angiogenesis.

From words to integers and beyond in corporate life

Poets have a licence to couch great truths in succinct, emotionally powerful, and perhaps slightly mysterious and ambiguous ways. On the other hand, it is the task of academics to explore such truths intellectually, in depth and detail, identifying the key constructs and their underlying relations and structures, hopefully without impairing the essential truth. So it could be said that in January 2013, around 60 academics gathered at the University of Texas, Austin under the benign and encouraging eye of their own muse, Professor Rod Hart, to play their role in exploring and explaining the underlying truth of Yan Zhen’s words. The goals of this chapter are quite broad. Rod was explicit and yet also somewhat Delphic in his expectations and aspirations for the chapter. Even though DICTION was a key analytic tool in most chapters, this chapter was not to be about DICTION per se, or simply a critique of the individual chapters forming this section of the book. Rather DICTION and these studies, as well as some others that got our attention, were to be more a launching pad for observations on what they revealed about the current state of understanding and research into the language of institutions, as well as some ‘adventurous’, but not too outlandish reflections on future challenges and opportunities.

Corporate ownership, corporate governance reform and timeliness of earnings : Malaysian evidence

This paper provides the first evidence showing that ownership concentration and the identity of the largest shareholder matter to the timeliness of corporate earnings, measured by a stock price-based timeliness metric and the reporting lag. Using panel data of 1276 Malaysian firms from 1996 to 2009, we find a non-linear relationship between concentrated ownership, measured by the largest shareholding in a firm, and the reporting lag but not the timeliness of price discovery. Although firms with government as the largest shareholder and political connections have a significantly shorter reporting lag, only the former are timelier in price discovery. Firms with family and foreigners as the largest shareholder however are less timely in price discovery. While the reporting lag is shorter in the period after the integration of the Malaysian Code of Corporate Governance (MCCG) into Bursa listing rules, its impact on the timeliness of price discovery is mostly immaterial.

A comparison of the histochemical and image-derived proteoglycan content of articular cartilage

There are several methods for determining the proteoglycan content of cartilage in biomechanics experiments. Many of these include assay-based methods and the histochemistry or spectrophotometry protocol where quantification is biochemically determined. More recently a method based on extracting data to quantify proteoglycan content has emerged using the image processing algorithms, e.g., in ImageJ, to process histological micrographs, with advantages including time saving and low cost. However, it is unknown whether or not this image analysis method produces results that are comparable to those obtained from the biochemical methodology. This paper compares the results of a well-established chemical method to those obtained using image analysis to determine the proteoglycan content of visually normal (n=33) and their progressively degraded counterparts with the protocols. The results reveal a strong linear relationship with a regression coefficient (R2) = 0.9928, leading to the conclusion that the image analysis methodology is a viable alternative to the spectrophotometry.

Image segmentation and pose estimation of humans in video

This thesis introduces improved techniques towards automatically estimating the pose of humans from video. It examines a complete workflow to estimating pose, from the segmentation of the raw video stream to extract silhouettes, to using the silhouettes in order to determine the relative orientation of parts of the human body. The proposed segmentation algorithms have improved performance and reduced complexity, while the pose estimation shows superior accuracy during difficult cases of self occlusion.

Dealing with shadows : capturing intrinsic scene appearance for image-based outdoor localisation

In outdoor environments shadows are common. These typically strong visual features cause considerable change in the appearance of a place, and therefore confound vision-based localisation approaches. In this paper we describe how to convert a colour image of the scene to a greyscale invariant image where pixel values are a function of underlying material property not lighting. We summarise the theory of shadow invariant images and discuss the modelling and calibration issues which are important for non-ideal off-the-shelf colour cameras. We evaluate the technique with a commonly used robotic camera and an autonomous car operating in an outdoor environment, and show that it can outperform the use of ordinary greyscale images for the task of visual localisation.

Optimal camera planning under versatile user constraints in multi-camera image processing systems

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Ownership, corporate governance and IPO post-listing liquidity

This thesis investigates how ownership structure and corporate governance relate to the post-listing liquidity of IPO firms. Using a sample of 1,049 Chinese IPOs from 2001 to 2010, the results show firms with a broader shareholder base and higher ownership concentration have greater post-listing liquidity. So do firms with higher state ownership and lower institution ownership. Corporate governance is also important; post-listing liquidity is higher for firms with CEO duality, a larger and more independent board, and more frequent board meetings. The 2005 Split Share Structure Reform, which increased the proportion of tradable shares, has a positive impact on liquidity.

Using image quality metrics to detect smoke-affected laser data

Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.

Corporate reputation and financial performance : underlying dimensions of corporate reputation and their relation to sustained financial performance

This thesis examined the relationship between firms' corporate reputation and their future financial performance. Corporate reputation was represented by measuring the level of senior executives' attention to a number of intangible firm' resources (e.g. financial reputation, service culture) within firms' annual reports over a 17 year period. Initial findings suggested there was only a small relationship between reputation and future performance which lead to a reformulation of the problem. Reputation was posited to be a source of corporate resilience that helped firms with stronger reputations to sustain superior financial performance in times of difficulty, as well as allowing them to rebound more quickly from performance decline. Results suggest this interpretation of corporate reputation as well as indicating that industry sectors operate in different reputational 'domains' in which the relative importance of financial versus stakeholder aspects of corporate reputation varies.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

‘Our’ community : corporate social responsibility, neoliberalisation, and mining industry community engagement in rural Australia

This paper addresses contemporary neoliberal mobilisations of community undertaken by private corporations. It does so by examining the ways in which the mining industry, empowered through the legitimising framework of corporate social responsibility, is increasingly and profoundly involved in shaping the meaning, practice, and experience of ‘local community’. We draw on a substantial Australian case study, consisting of interviews and document analysis, as a means to examine ‘community-engagement’ practices undertaken by BHP Billiton’s Ravensthorpe Nickel Operation in the Shire of Ravensthorpe in rural Australia. This engagement, we argue, as a process of deepening neoliberalisation simultaneously defines and transforms local community according to the logic of global capital. As such, this study has implications for critical understandings of the intersections among corporate social responsibility, neoliberalisation, community, and capital.