Essays on Corporate Governance and the Quality of Disclosed Earnings: Across Transitional Europe

A growing body of empirical research examines the structure and effectiveness of corporate governance systems around the world. An important insight from this literature is that corporate governance mechanisms address the excessive use of managerial discretionary powers to get private benefits by expropriating the value of shareholders. One possible way of expropriation is to reduce the quality of disclosed earnings by manipulating the financial statements. This lower quality of earnings should then be reflected by the stock price of firm according to value relevance theorem. Hence, instead of testing the direct effect of corporate governance on the firm’s market value, it is important to understand the causes of the lower quality of accounting earnings. This thesis contributes to the literature by increasing knowledge about the extent of the earnings management – measured as the extent of discretionary accruals in total disclosed earnings - and its determinants across the Transitional European countries. The thesis comprises of three essays of empirical analysis of which first two utilize the data of Russian listed firms whereas the third essay uses data from 10 European economies. More specifically, the first essay adds to existing research connecting earnings management to corporate governance. It testifies the impact of the Russian corporate governance reforms of 2002 on the quality of disclosed earnings in all publicly listed firms. This essay provides empirical evidence of the fact that the desired impact of reforms is not fully substantiated in Russia without proper enforcement. Instead, firm-level factors such as long-term capital investments and compliance with International financial reporting standards (IFRS) determine the quality of the earnings. The result presented in the essay support the notion proposed by Leuz et al. (2003) that the reforms aimed to bring transparency do not correspond to desired results in economies where investor protection is lower and legal enforcement is weak. The second essay focuses on the relationship between the internal-control mechanism such as the types and levels of ownership and the quality of disclosed earnings in Russia. The empirical analysis shows that the controlling shareholders in Russia use their powers to manipulate the reported performance in order to get private benefits of control. Comparatively, firms owned by the State have significantly better quality of disclosed earnings than other controllers such as oligarchs and foreign corporations. Interestingly, market performance of firms controlled by either State or oligarchs is better than widely held firms. The third essay provides useful evidence on the fact that both ownership structures and economic characteristics are important factors in determining the quality of disclosed earnings in three groups of countries in Europe. Evidence suggests that ownership structure is a more important determinant in developed and transparent countries, while economic determinants are important determinants in developing and transitional countries.

Corporate Funding on the European Debt Capital Market

The integrated European debt capital market has undoubtedly broadened the possibilities for companies to access funding from the public and challenged investors to cope with an ever increasing complexity of its market participants. Well into the Euro-era, it is clear that the unified market has created potential for all involved parties, where investment opportunities are able to meet a supply of funds from a broad geographical area now summoned under a single currency. Europe’s traditionally heavy dependency on bank lending as a source of debt capital has thus been easing as corporate residents are able to tap into a deep and liquid capital market to satisfy their funding needs. As national barriers eroded with the inauguration of the Euro and interest rates for the EMU-members converged towards over-all lower yields, a new source of debt capital emerged to the vast majority of corporate residents under the new currency and gave an alternative to the traditionally more maturity-restricted bank debt. With increased sophistication came also an improved knowledge and understanding of the market and its participants. Further, investors became more willing to bear credit risk, which opened the market for firms of ever lower creditworthiness. In the process, the market as a whole saw a change in the profile of issuers, as non-financial firms increasingly sought their funding directly from the bond market. This thesis consists of three separate empirical studies on how corporates fund themselves on the European debt capital markets. The analysis focuses on a firm’s access to and behaviour on the capital market, subsequent the decision to raise capital through the issuance of arm’s length debt on the bond market. The specific areas considered are contributing to our knowledge in the fields of corporate finance and financial markets by considering explicitly firms’ primary market activities within the new market area. The first essay explores how reputation of an issuer affects its debt issuance. Essay two examines the choice of interest rate exposure on newly issued debt and the third and final essay explores pricing anomalies on corporate debt issues.

From cages to wedges and clefts : Design of some novel hosts based on Image , Image , Image -triquinane framework

Annulation of aromatic rings on the folded Image ,Image ,Image -triquinane backbone has led to the design of potential host systems Image and Image whose crystal structures have been determined.

Adaptation in triadic business relationship settings: A study in corporate travel management

ANNE HOLMA ADAPTATION IN TRIADIC BUSINESS RELATIONSHIP SETTINGS – A STUDY IN CORPORATE TRAVEL MANAGEMENT Business-to-business relationships form complicated networks that function in an increasingly dynamic business environment. This study addresses the complexity of business relationships, both when it comes to the core phenomenon under investigation, adaptation, and the structural context of the research, a triadic relationship setting. In business research, adaptation is generally regarded as a dyadic phenomenon, even though it is well recognised that dyads do not exist isolated from the wider network. The triadic approach to business relationships is especially relevant in cases where an intermediary is involved, and where all three actors are directly connected with each other. However, only a few business studies apply the triadic approach. In this study, the three dyadic relationships in triadic relationship settings are investigated in the context of the other two dyads to which each is connected. The focus is on the triads as such, and on the connections between its actors. Theoretically, the study takes its stand in relationship marketing. The study integrates theories and concepts from two approaches, the industrial network approach by the Industrial marketing and purchasing group, and the Service marketing and management approach by the Nordic School. Sociological theories are used to understand the triadic relationship setting. The empirical context of the study is corporate travel management. The study is a retrospective case study, where the data is collected by in-depth interviews with key informants from an industrial enterprise and its travel agency and service supplier partners. The main theoretical contribution of the study concerns opening a new research area in relationship marketing by investigating adaptation in business relationships with a new perspective, and in a new context. This study provides a comprehensive framework to analyse adaptation in triadic business relationship settings. The analysis framework was created with the help of a systematic combining approach, which is based on abductive logic and continuous iteration between the theory and the case study results. The framework describes how adaptations initiate, and how they progress. The framework also takes into account how adaptations spread in triadic relationship settings, i.e. how adaptations attain all three actors of the triad. Furthermore, the framework helps to investigate the outcomes of the adaptations for individual firms, for dyadic relationships, and for the triads. The study also provides concepts and classification that can be used when evaluating adaptation and relationship development in both dyadic and triadic relationships.

Corporate Environmental Responsibility in Demand Networks (summary section only)

Research on corporate responsibility has traditionally focused on the responsibilities of companies within their corporate boundaries only. Yet this view is challenged today as more and more companies face the situation in which the environmental and social performance of their suppliers, distributors, industry or other associated partners impacts on their sales performance and brand equity. Simultaneously, policy-makers have taken up the discussion on corporate responsibility from the perspective of globalisation, in particular of global supply chains. The category of selecting and evaluating suppliers has also entered the field of environmental reporting. Companies thus need to tackle their responsibility in collaboration with different partners. The aim of the thesis is to further the understanding of collaboration and corporate environmental responsibility beyond corporate boundaries. Drawing on the fields of supply chain management and industrial ecology, the thesis sets out to investigate inter-firm collaboration on three different levels, between the company and its stakeholders, in the supply chain, and in the demand network of a company. The thesis is comprised of four papers: Paper A discusses the use of different research approaches in logistics and supply chain management. Paper B introduces the study on collaboration and corporate environmental responsibility from a focal company perspective, looking at the collaboration of companies with their stakeholders, and the salience of these stakeholders. Paper C widens this perspective to an analysis on the supply chain level. The focus here is not only beyond corporate boundaries, but also beyond direct supplier and customer interfaces in the supply chain. Paper D then extends the analysis to the demand network level, taking into account the input-output, competitive and regulatory environments, in which a company operates. The results of the study broaden the view of corporate responsibility. By applying this broader view, different types of inter-firm collaboration can be highlighted. Results also show how environmental demand is extended in the supply chain regardless of the industry background of the company.

Wavelet transform based error concealment approach for image denoising

Denoising of images in compressed wavelet domain has potential application in transmission technology such as mobile communication. In this paper, we present a new image denoising scheme based on restoration of bit-planes of wavelet coefficients in compressed domain. It exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each band. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with conventional unrestored scheme, in context of error reduction and has capability to adapt to situations where noise level in the image varies. The applicability of the proposed approach has implications in restoration of images due to noisy channels. This scheme, in addition, to being very flexible, tries to retain all the features, including edges of the image. The proposed scheme is computationally efficient.

Image reconstruction by inhomogeneous diffusion for positron emission tomography

In positron emission tomography (PET), image reconstruction is a demanding problem. Since, PET image reconstruction is an ill-posed inverse problem, new methodologies need to be developed. Although previous studies show that incorporation of spatial and median priors improves the image quality, the image artifacts such as over-smoothing and streaking are evident in the reconstructed image. In this work, we use a simple, yet powerful technique to tackle the PET image reconstruction problem. Proposed technique is based on the integration of Bayesian approach with that of finite impulse response (FIR) filter. A FIR filter is designed whose coefficients are determined based on the surface diffusion model. The resulting reconstructed image is iteratively filtered and fed back to obtain the new estimate. Experiments are performed on a simulated PET system. The results show that the proposed approach is better than recently proposed MRP algorithm in terms of image quality and normalized mean square error.

On resampling detection and its application to detect image tampering

Usually digital image forgeries are created by copy-pasting a portion of an image onto some other image. While doing so, it is often necessary to resize the pasted portion of the image to suit the sampling grid of the host image. The resampling operation changes certain characteristics of the pasted portion, which when detected serves as a clue of tampering. In this paper, we present deterministic techniques to detect resampling, and localize the portion of the image that has been tampered with. Two of the techniques are in pixel domain and two others in frequency domain. We study the efficacy of our techniques against JPEG compression and subsequent resampling of the entire tampered image.

GAP-RBF based NR image quality measurement for JPEG coded images

In this paper, we present a growing and pruning radial basis function based no-reference (NR) image quality model for JPEG-coded images. The quality of the images are estimated without referring to their original images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the problem of quality estimation is transformed to a function approximation problem and solved using GAP-RBF network. GAP-RBF network uses sequential learning algorithm to approximate the functional relationship. The computational complexity and memory requirement are less in GAP-RBF algorithm compared to other batch learning algorithms. Also, the GAP-RBF algorithm finds a compact image quality model and does not require retraining when the new image samples are presented. Experimental results prove that the GAP-RBF image quality model does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.

Design of a FIR filter for image restoration using principal component neural networks

The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.

Real-time image denoising algorithm in teleradiology systems - art.no.606304

Denoising of medical images in wavelet domain has potential application in transmission technologies such as teleradiology. This technique becomes all the more attractive when we consider the progressive transmission in a teleradiology system. The transmitted images are corrupted mainly due to noisy channels. In this paper, we present a new real time image denoising scheme based on limited restoration of bit-planes of wavelet coefficients. The proposed scheme exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each sub-band. The desired bit-rate control is achieved by applying the restoration on a limited number of bit-planes subject to the optimal smoothing. The proposed method adapts itself to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with unrestored case, in context of error reduction. It also has capability to adapt to situations where noise level in the image varies and with the changing requirements of medical-experts. The applicability of the proposed approach has implications in restoration of medical images in teleradiology systems. The proposed scheme is computationally efficient.

A new FIR filter for image restoration

Image filtering techniques have potential applications in biomedical image processing such as image restoration and image enhancement. The potential of traditional filters largely depends on the apriori knowledge about the type of noise corrupting the image. This makes the standard filters to be application specific. For example, the well-known median filter and its variants can remove the salt-and-pepper (or impulse) noise at low noise levels. Each of these methods has its own advantages and disadvantages. In this paper, we have introduced a new finite impulse response (FIR) filter for image restoration where, the filter undergoes a learning procedure. The filter coefficients are adaptively updated based on correlated Hebbian learning. This algorithm exploits the inter pixel correlation in the form of Hebbian learning and hence performs optimal smoothening of the noisy images. The application of the proposed filter on images corrupted with Gaussian noise, results in restorations which are better in quality compared to those restored by average and Wiener filters. The restored image is found to be visually appealing and artifact-free

Wavelet transform based error concealment approach for image denoising

Denoising of images in compressed wavelet domain has potential application in transmission technology such as mobile communication. In this paper, we present a new image denoising scheme based on restoration of bit-planes of wavelet coefficients in compressed domain. It exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each band. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with conventional unrestored scheme, in context of error reduction and has capability to adapt to situations where noise level in the image varies. The applicability of the proposed approach has implications in restoration of images due to noisy channels. This scheme, in addition, to being very flexible, tries to retain all the features, including edges of the image. The proposed scheme is computationally efficient.