The impact of IFRS on the analysts' information environment: the role of accounting policies and corporate disclosure

The thesis presents the results of a study on the impact of International Financial Reporting Standards on the analysts information environment. The analysis is concentrated on the role of specific IFRSs and corporate disclosure. The effect of IFRS adoption on the information asymmetry between firms and outsiders is examined through properties of analysts earnings forecasts. A contribution to the existing academic literature is made by examining the role of goodwill, intangible assets and acquisitions before and after IFRS adoption in Europe. The results show that the IFRSs for goodwill, acquisitions and intangible assets are related to improvements in the analysts information environment. Another contribution to knowledge is made by investigating the effect of corporate disclosure quantity on the analysts information environment before and after IFRS adoption. For this purpose, a new approach and text analysis technique to assess the impact of corporate disclosure quantity is developed. This involves the creation of a new custom dictionary and the collection of an extensive set of qualitative data. The results show that corporate disclosure quantity under IFRS, is related to improvements in the analysts information environment but that there are differences in this effect across European countries. The results also demonstrate that the improvements in the accuracy of analysts earnings forecasts are related particularly to disclosure concerning financial instruments and operating segments. Overall, the findings of the thesis suggest that the adoption of IFRS resulted in an increase in the quality of reported earnings, which is likely to derive from higher comparability of financial statements, enhanced transparency and an improved analysts information environment. It is also established that fundamental differences across countries remain after IFRS adoption and that the development and harmonisation of financial reporting standards alone are not sufficient to increase the quality of financial information and decrease information asymmetry between market participants

An IoT-based solution for monitoring a fleet of educational buildings focusing on energy efficiency

Raising awareness among young people and changing their behaviour and habits concerning energy usage iskey to achieving sustained energy saving. Additionally, young people are very sensitive to environmental protection so raising awareness among children is much easier than with any other group of citizens. This work examinesways to create an innovative Information & Communication Technologies (ICT) ecosystem (including web-based, mobile, social and sensing elements) tailored specifically for school environments, taking into account both theusers (faculty, staff, students, parents) and school buildings, thus motivating and supporting young citizenś behavioural change to achieve greater energy efficiency. A mixture of open-source IoT hardware and proprietary platforms on the infrastructure level, are currently being utilized for monitoring a fleet of 18 educational buildings across 3 countries, comprising over 700 IoT monitoring points. Hereon presented is the system's high-level architecture, as well as several aspects of its implementation, related to the application domain of educational building monitoring and energy efficiency. The system is developed based on open-source technologies andservices in order to make it capable of providing open IT-infrastructure and support from different commercial hardware/sensor vendors as well as open-source solutions. The system presented can be used to develop and offer newapp-based solutions that can be used either for educational purposes or for managing the energy efficiency ofthebuilding. The system is replicable and adaptable to settings that may be different than the scenarios envisionedhere (e.g., targeting different climate zones), different IT infrastructures and can be easily extended to accommodate integration with other systems. The overall performance of the system is evaluated in real-world environment in terms of scalability, responsiveness and simplicity

Scenarios for Educational and Game Activities using Internet of Things Data

Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. The goal to address the global climate problem requires informing the population on their roles in mitigation actions and adaptation of sustainable behaviors. Addressing climate change and achieve ambitious energy and climate targets requires a change in citizen behavior and consumption practices. IoT sensing and related scenario and practices, which address school children via discovery, gamification, and educational activities, are examined in this paper. Use of seawater sensors in STEM education, that has not previously been addressed, is included in these educational scenaria

Accounting narratives and disclosure quality: Empirical evidence from the UK, France and Germany

In this paper, we analyse narrative disclosures from over 28,000 corporate announcements for 137 UK, French and German companies over the years 2003 to 2011. A custom-dictionary, based on software used by Kothari et al (2009) is employed to classify the disclosures into six categories based on theme and content, and the volume of disclosures in each category, in each month is recorded for each firm. The resulting variables are then used in models of disclosure quality based on the accuracy and dispersion of analysts’ earnings forecasts, to draw conclusions about the extent to which the quantity of disclosed information is related to disclosure quality. We also investigate the effects of the adoption of International Financial Reporting Standards (IFRS) by the firms during this period, differences between countries and the effect of various company specific factors including the level of intangible assets and goodwill in the firm’s balance sheet

A RESTful Rule Management Framework for Internet of Things Applications

Web technologies are currently regarded as key enabling factors for the Internet of Things (IoT), and substantial effort is being dedicated to bringing sensors and data from the real world to the Web. In addition, rule-based automation mechanisms are expected to play a significant role in the effective integration of the physical world with the virtual world by leveraging a trigger-action paradigm. Although several rule engines are already available, limited effort has been devoted to rule-based solutions that are tailored to the IoT and consider rule configurability and extensibility according to application requirements. In this work, we propose a RESTful rule management framework for IoT applications that satisfies these requirements. The framework is centered around a resource-based graph, which enables the uniform representation of things (e.g., sensors and domain entities) and rules as URI-addressable resources. We describe the design and implementation choices of the main rule management features (rule scheduling, activation and RESTful operations for managing rules at various levels of configurability and extensibility). Finally, we present a case study and performance evaluation results regarding the use of this rule management framework in a set of school buildings that were part of a real-world IoT deployment that was realized within the Horizon 2020 GAIA research project, with the objective of promoting energy -saving behaviors in school communities

Approximate High Dimensional Graph Mining With Matrix Polar Factorization: A Twitter Application

At the dawn of the Internet era graph analytics play an important role in high- and low-level network policymaking across a wide array of fields so diverse as transportation network design, supply chain engineering and logistics, social media analysis, and computer communication networks, to name just a few. This can be attributed not only to the size of the original graph but also to the nature of the problem parameters. For instance, algorithmic solutions depend heavily on the approximation criterion selection. Moreover, iterative or heuristic solutions are often sought as it is a high dimensional problem given the high number of vertices and edges involved as well as their complex interaction. Replacing under constraints a directed graph with an undirected one having the same vertex set is often sought in applications such as data visualization, community structure discovery, and connection-based vertex centrality metrics. Polar decomposition is a key matrix factorization which represents a matrix as a product of a symmetric positive (semi)definite factor and an orthogonal one. The former can be an undirected approximation of the original adjacency matrix. The proposed graph approximation has been tested with three Twitter graphs with encouraging results with respect to density, Fiedler number, and certain vertex centrality metrics based on matrix power series. The dataset was hosted in an online MongoDB instance

Transform-based graph topology similarity metrics

Graph signal processing has recently emerged as a field with applications across a broad spectrum of fields including brain connectivity networks, logistics and supply chains, social media, computational aesthetics, and transportation networks. In this paradigm, signal processing methodologies are applied to the adjacency matrix, seen as a two-dimensional signal. Fundamental operations of this type include graph sampling, the graph Laplace transform, and graph spectrum estimation. In this context, topology similarity metrics allow meaningful and efficient comparisons between pairs of graphs or along evolving graph sequences. In turn, such metrics can be the algorithmic cornerstone of graph clustering schemes. Major advantages of relying on existing signal processing kernels include parallelism, scalability, and numerical stability. This work presents a scheme for training a tensor stack network to estimate the topological correlation coefficient between two graph adjacency matrices compressed with the two-dimensional discrete cosine transform, augmenting thus the indirect decompression with knowledge stored in the network. The results from three benchmark graph sequences are encouraging in terms of mean square error and complexity especially for graph sequences. An additional key point is the independence of the proposed method from the underlying domain semantics. This is primarily achieved by focusing on higher-order structural graph patterns