The key objective of this research is to examine various issues relating to trades of UK directors (insiders’) in their company shares. Specifically, we examine the general patterns and characteristics of directors’ trades, the seasonality patterns of aggregate directors’ trades (measured by insider aggregate number and value of insider trading activities), the impact that director’s age has on trade informativeness, and the effect of industry classification on the information content of directors’ trades. To the best of our knowledge, no empirical examination of these issues has yet to be examined. When examining the general patterns and characteristics of directors’ trades, we find that directors buy more frequently than they sell but the average value of sell trades are approximately seven times larger, which suggests that directors sell less frequently but in larger monetary amounts. Furthermore, the majority of trades occur for directors aged between 45 and 65. Small transactions tend to be purchases while large transactions tend to be sells. The majority of the trades were by former directors (for both transaction types) followed by executive and non- executive directors. The majority of trades occurred in the financial industry. When examining the seasonal patterns of aggregate directors’ trades (as measured by the number and the value of insider transactions), the results show that there is a day of the week anomaly in aggregate insider activities. Insiders tend to trade more on Fridays and less on Tuesdays. Also, there is a month of the year anomaly in aggregate insider activities (as measured by the number of insider transactions). Specifically, insiders tend to trade more in March and trade less in August. The impact of director’s age is also examined, and the results suggest that younger directors’ buy transactions produce significantly higher abnormal returns than older directors. There is some evidence of statistically significantly negative CAARs for younger directors’ sell trades. When controlling for director type, we find that younger executives (formers) are more informed about their buy trades than executives (formers) of other age groups. Unlike the previous pattern, older non-executives (over 70) seem to be more informed about their buy trades than younger non-executives. Finally, the results of whether industry classifications have an impact on the informativeness of directors’ trades indicate that abnormal returns are highest for directors of technology industries. The level of information asymmetry has an impact on the informativeness of directors’ trades. Specifically, insider gains are highest, for directors, in high R&D, high volatility, low regulated, highly concentrated, and low CEO compensation industries/sectors.

Suppliers of wheat must ensure that their products have the required quality profile demanded by customers and consistently deliver that quality in order to be competitive. Australia’s wheat industry is highly exposed to such competitive threats because it relies heavily on exports. An integral component in maintaining Australia’s competitiveness has been its classification system. The first step involves the complex process of determining a genotypic quality profile of each variety – a variety classification. At harvest, subsequent steps are the use of a statutory declaration and testing of physical quality traits. Together these steps determine how deliveries of wheat are segregated. A single variety can have different classifications across the 7 classification regions of Australia. Most classification regions are divided along state borders and these are not reflective of potential environmental influences. / The manner in which Australia wheat breeding programs now tackle their task has changed since 1999. The commercially focused companies of the current era have national targets to remain viable, and are focused on costs. Other evolutions associated with the change, are the introduction of different sources of parental material, and moving to more economic composite quality testing regimes instead of the individual site by site testing used in the past. Together, these factors, particularly variety adaptability and stability of performance, have the capacity to increase variability. The likelihood of variation is further increased given that the current classification regions upon which classification decisions are made do not adequately reflect environmental effects on the expression of quality. To determine whether better divisions of the Australian wheat-belt could be identified for variety classification purposes, a substantial spatial and temporal database of historical quality results was assembled. The creation of this relational database was unique, because never before had expansive sets of independent, state-based, quality sub-sets been joined together. However, the data were unbalanced and required alternative statistical tools to be analysed. The relational database was the platform from which three phases of research were conducted. / The first research phase investigated the extent of cross over, or re-ranking of results, statistically referred to as genotype x environment interaction. The approach was to assess balanced data sets, in a manner reminiscent of the most common method identified from the literature. The results of those analyses showed that the size of genotype and environment interaction was small compared with the main effects of genotype and environment. The second phase of research focused on identifying alternative boundaries for classification purposes. Test divisions were compared with the current set of 7 classification regions for the capacity to minimise environmental variance while maintaining differences between the zones of a set. Test divisions were based on fourteen published divisions of the Australian wheat-belt. Analyses were conducting using residual maximum likelihood because of the unbalanced structure of the data. Estimates of variance components, quality trait means and standard errors were calculated. Consideration of such estimates resulted in the identification of 4 different divisions of the wheat-belt that had low environmental variance levels for important quality traits such as maximum resistance, dough development time, and water absorption. / In addition, these 4 divisions of the wheat-belt had fewer number of zones compared with the existing set of classification regions because they linked separate parts of the wheat-belt together. In order of decreasing merit, the 4 divisions of the wheat-belt represented average October maximum temperatures; agro-ecological zones reported by Williams et al. (2002); average annual rainfall; and Departments of Agriculture recommendation zones. A final phase of crosschecking was performed to assess the veracity of the 4 identified divisions. A cluster analysis supported the orientation of their boundaries and it was also observed that the use of fixed boundaries for classification purposes would not be negatively affected by seasonal variation. The 4 divisions of the wheat-belt identified in this research support the use of environmentally focused classification boundaries. In addition to improving the capacity to segregate consistent quality, the linking of geographically separate production areas of the wheat-belt reduced the number of zones and this offers process efficiencies.

The purpose of this thesis is to investigate differences in the R&D motivations for manufacturing and service firms. The thesis contributes to existing R&D literature as it proposes a novel approach for the categorization of manufacturing and service firms. Using data from the Statistics Sweden R&D survey, the paper classifies firms according to their income structure, which allows the study to base industry classifications to what best aligns with firms’ actual activities. The methodology consists of Welch’s two- sample t-test in combination with OLS regressions to examine differences in the motivations for manufacturing and service firms. The results suggest that there is a statistically significant difference in the motivations. Manufacturing firms are found to devote a higher proportion of their total R&D investments towards the improvement of existing products. Services were found to devote a greater share of their R&D investments to the development of new processes and to increases in general knowledge-building. Moreover, the study finds a substantial disparity in how firms are classified according to industry classification codes and how they actually earn their revenues and therefore questions the accuracy of conventional industry classification methods.

Made available in DSpace on 2016-03-15T19:25:41Z (GMT). No. of bitstreams: 1 Ricardo Presz Palmaka.pdf: 1143013 bytes, checksum: c432292cad770577bf08db44878294e5 (MD5) Previous issue date: 2011-02-15 / Fundo Mackenzie de Pesquisa / Classifying things in groups is basic to study differences between these things; it is a way to store data and retrieve information. This happens in biology, the area of study where the classification of organisms is unquestionable important. Inspired by biology, Organizational Ecology theory has been concerned with the classification of organizations too. Much of the organizational research relies primarily on a firm classification which separates them by type of industry they belong to or by the type of product they offer to certain markets (HANNAN, HSU, 2005). This is not a surprise, since these are the criteria adopted by the official statistics in the classifications of economic activities, both nationally and internationally. Grouping companies by products they offer is also a commonly used way to target potential customers in marketing strategies, to create more effective sales processes, communication or promotion actions, for example. This kind of classification, however, is not always adequate, especially from an organizational point of view. It brings the risk of putting together different companies within a group: a large multinational company could share the same economic activity of a small business and both, although so different, be classified into a common group, since they offer the same types of products or services. The main goal of this dissertation is, using the concept of organizational form proposed by the Organizational Ecology, identify different groups in the software market. Similarly to the gene in biology, the organizational form serves as a set of instructions for the creation and conduction of collective action within the organization. This paper sought to show that within a group of software companies it possible to classify them according to their organizational form, taking into consideration not only their activities, but other attributes that form a company. To identify those attributes, the definitions of the form in Organizational Ecology were used, with a survey of 100 Information Technology companies, which generated five groups of companies, using objective characteristics of the organizational forms. / Classificar coisas é a base para que seja possível estudar as diferenças entre estas coisas; é uma forma de armazenar dados e permitir as buscas pelas informações. Assim ocorre na biologia, área de estudo em que a classificação de organismos é de indiscutível importância. Inspirada na biologia, a teoria de Ecologia Organizacional tem se preocupado com a classificação de organizações. Muitas das pesquisas sobre organizações utilizam predominantemente uma classificação de empresas que as distingue por tipo de indústria as quais pertencem ou pelo tipo de produto que oferecem a determinados mercados (HANNAN; HSU, 2005). Isso não é surpreendente, visto que estes são critérios adotados pelas estatísticas oficiais nas classificações das atividades econômicas, tanto em nível nacional quanto internacional. A classificação por produto supõe que em um grupo existam empresas homogêneas e é comumente usada para segmentação de potenciais clientes nas áreas de marketing das empresas para criar ações mais efetivas de vendas, comunicação ou promoção, por exemplo. Esta classificação, entretanto, não é adequada, sobretudo do ponto de vista organizacional. Corre-se o risco de juntar empresas diferentes entre si dentro de um mesmo grupo: uma grande empresa multinacional pode compartilhar a mesma atividade econômica de uma microempresa e as duas, tão diferentes entre si, serem classificadas dentro de um grupo comum, pois oferecem os mesmos tipos de produtos ou serviços. O principal objetivo desta dissertação é, usando o conceito de forma organizacional proposto pela Ecologia Organizacional, identificar os segmentos de empresas de serviços de informática. Analogamente ao gene na biologia, a forma organizacional funciona como um conjunto de instruções para criação e condução das ações coletivas no âmbito da organização. O trabalho procurou mostrar que dentro de um grupo de empresas de software é possível classificá-las de acordo com a sua forma organizacional, levando em consideração não apenas suas atividades, mas outros atributos que dão forma a uma empresa. Para a identificação desses atributos foram utilizadas as definições de forma na Ecologia Organizacional em uma pesquisa em 100 empresas de Tecnologia da Informação, em que foram encontrados cinco grupos de empresas, usando características objetivas de forma organizacional.

Sustainable business has become highly sought after today by stakeholders, which explains the importance of CSR for companies. Companies are required to work sustainably in order to legitimize themselves both in the market and society. This can be done by fulfilling and satisfying the economic, environmental and sustainable, as well as the social frameworks that exist in business society. Despite CSR's central role in companies, researchers have differentiated meanings on whether CSR initiatives really improve corporate profitability in terms of financial performance, and whether it has a positive relationship with corporate value. Regarding this, the report examines CSR's relationship to CFP and company value, through the profitability measures ROA and ROE, as well as the valuation measures market value and P/E ratio. A quantitative method has been applied to examine the companies published on Dagens Industri's sustainability index for the year 2021. Dagens Industri's list includes the largest listed companies in the GICS categories on the Swedish stock market exchange. The study uses regression analysis as a choice of statistical analysis method to examine the variables relationships. From the regressions, a small part of the result showed a weak negative relationship between CSR and ROA. The regressions otherwise mainly showed a non-significant relationship between CSR and a company's profitability and value. The variables do not have a significant relationship with each other, and a neutral relationship between them can thus be demonstrated. The existing research gap is therefore still ambiguous, hence the room for further future studies in the field.

Factor models attribute stock returns to a linear combination of factors. A model with great explanatory power (R2) can be used to estimate the systematic risk of an investment. One of the most important factors is the industry which the company of the stock operates in. In commercial risk models this factor is often determined with a manually constructed stock classification scheme such as GICS. We present Natural Language Industry Scheme (NLIS), an automatic and multivalued classification scheme based on topic modeling. The topic modeling is performed on transcripts of company earnings calls and identifies a number of topics analogous to industries. We use non-negative matrix factorization (NMF) on a term-document matrix of the transcripts to perform the topic modeling. When set to explain returns of the MSCI USA index we find that NLIS consistently outperforms GICS, often by several hundred basis points. We attribute this to NLIS’ ability to assign a stock to multiple industries. We also suggest that the proportions of industry assignments for a given stock could correspond to expected future revenue sources rather than current revenue sources. This property could explain some of NLIS’ success since it closely relates to theoretical stock pricing. / Faktormodeller förklarar aktieprisrörelser med en linjär kombination av faktorer. En modell med hög förklaringsgrad (R2) kan användas föratt skatta en investerings systematiska risk. En av de viktigaste faktorerna är aktiebolagets industritillhörighet. I kommersiella risksystem bestäms industri oftast med ett aktieklassifikationsschema som GICS, publicerat av ett finansiellt institut. Vi presenterar Natural Language Industry Scheme (NLIS), ett automatiskt klassifikationsschema baserat på topic modeling. Vi utför topic modeling på transkript av aktiebolags investerarsamtal. Detta identifierar ämnen, eller topics, som är jämförbara med industrier. Topic modeling sker genom icke-negativmatrisfaktorisering (NMF) på en ord-dokumentmatris av transkripten. När NLIS används för att förklara prisrörelser hos MSCI USA-indexet finner vi att NLIS överträffar GICS, ofta med 2-3 procent. Detta tillskriver vi NLIS förmåga att ge flera industritillhörigheter åt samma aktie. Vi föreslår också att proportionerna hos industritillhörigheterna för en aktie kan motsvara förväntade inkomstkällor snarare än nuvarande inkomstkällor. Denna egenskap kan också vara en anledning till NLIS framgång då den nära relaterar till teoretisk aktieprissättning.

Valuing Intellectual Property assets is increasingly critical in today’s economy, where intangible assets constitute a significant portion of business value. This thesis addresses the challenges inherent in the IP valuation process, particularly the subjectivity and variability associated with different IP types and valuation methodologies. It proposes a new way to value IP assets, by building upon existing disaggregation methods, and by introducing the IP-heaviness classification system. The study aims to develop an objective valuation model for IP assets by introducing the IP-heaviness classification system. The goal of the model is to estimate the range of IP Contribution (IPC) to company value across different industry groups. Our study employed Kernel Density Estimation and Monte Carlo Simulation to analyze the dataset and generate a larger data sample. We then developed the IPH classification system, which categorizes industries based on their reliance on IP as a value contributor, grouping them by similar levels of IP dependence. This structured approach allows for a preliminary estimation of the IP contribution for each group, providing a standardized framework for IP valuation. Each IPH group was assigned its own probability density curve to represent its potential IPC value. Ultimately, our model produced confidence intervals for each IPH group, offering a reliable measure of the IP contribution within each category. Our findings reveal significant variability in the impact of IP on company value across different industries. Higher IPH groups, representing industries with substantial IP reliance, show a greater proportion of their value attributed to IP assets. Conversely, lower IPH groups, with less reliance on IP, exhibit lower IP contributions. The IPH classification system addresses the challenges of traditional IP valuation methods by providing a more objective and transparent approach. It enhances the comparability of companies within and across IPH groups and reduces subjectivity in the valuation process.