Employee layoff announcements and shareholder wealth: empirical evidence from Finland 2008-2013

This thesis investigates the short-term stock price reaction to layoff announcements in Finland. It also studies whether the characteristics of the firm or the layoff announcement have an impact on the stock market reaction. Standard event study methodology was utilized to examine the stock price reactions to layoffs and to test the created hypotheses. The event pool consisted of 102 publicly disclosed layoff announcements that were announced during the time period from June 2008 to December 2013. The empirical results show that the stock market reaction is strongly positive in the pre-event period of -10 to -1 with CAAR of 2,69%. The reaction is however slightly negative on the event date with AAR of -0,57%. Based on the results the conclusion is that either the managers are timing the markets or the layoffs are seen as efficiency improving acts and the market becomes aware of such actions pre-event. Additionally different characteristic hypotheses are tested to find out whether they would explain the reaction. The characteristics are: the reason stated by the management, business cycle, industry group, prior performance, leverage-ratio, the size of the company, the size of the layoff and the duration of the layoff.

Sensor communication in Smart cities and regions: An efficient IoT-based remote health monitoring system

Recent advances in Information and Communication Technology (ICT), especially those related to the Internet of Things (IoT), are facilitating smart regions. Among many services that a smart region can offer, remote health monitoring is a typical application of IoT paradigm. It offers the ability to continuously monitor and collect health-related data from a person, and transmit the data to a remote entity (for example, a healthcare service provider) for further processing and knowledge extraction. An IoT-based remote health monitoring system can be beneficial in rural areas belonging to the smart region where people have limited access to regular healthcare services. The same system can be beneficial in urban areas where hospitals can be overcrowded and where it may take substantial time to avail healthcare. However, this system may generate a large amount of data. In order to realize an efficient IoT-based remote health monitoring system, it is imperative to study the network communication needs of such a system; in particular the bandwidth requirements and the volume of generated data. The thesis studies a commercial product for remote health monitoring in Skellefteå, Sweden. Based on the results obtained via the commercial product, the thesis identified the key network-related requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, the thesis has proposed an architecture called IReHMo - an IoT-based remote health monitoring architecture. This architecture allows users to incorporate several types of IoT devices to extend the sensing capabilities of the system. Using IReHMo, several IoT communication protocols such as HTTP, MQTT and CoAP has been evaluated and compared against each other. Results showed that CoAP is the most efficient protocol to transmit small size healthcare data to the remote servers. The combination of IReHMo and CoAP significantly reduced the required bandwidth as well as the volume of generated data (up to 56 percent) compared to the commercial product. Finally, the thesis conducted a scalability analysis, to determine the feasibility of deploying the combination of IReHMo and CoAP in large numbers in regions in north Sweden.

The Impact of Event Marketing on Brand Awareness in eSport Industry

The purpose of this qualitative research is to study what is the impact of event marketing on brand awareness in the context of electronic sport industry. Based on the research questions, the theoretical framework will be developed. This research will analyze earlier theories, and also searching more fresh literature to explain the current phenomenon in the eSport industry. In the empirical part, there were total of five case companies interviewed. The context of this research is eSport, which has its own chapter. The theoretical part of the thesis focuses on event marketing and brand awareness. In this research, event marketing is analyzed from the event organizers perspective. In some occasions, event exhibitors’ perspective is also analyzed. In brand awareness, the focus is how to create a brand recognizable, recalled and from there top of mind in consumers’ minds. The results of this research revealed that many companies’ struggles on getting their brand recognizable. Some of the case companies lacks a strategy and don’t exactly know the core values of their customers. However some of the case companies were opposite. One reason behind this is that some of them has experience on the field and the companies have resources that covers them. Also the current strong brand has clearly a positive affect on their business.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).