Internet financial reporting in Saudi Arabia: users' perceptions and disclosures

This thesis investigates corporate financial disclosure practices on Web sites and their impact. This is done, first by examining the views of various Saudi user groups (institutional investors, financial analysts and private investors) on disclosure of financial reporting on the Internet and assessing differences, if any, in perceptions of the groups. Over 303 individuals from three groups responded to a questionnaire. Views were elicited regarding: users attitude to the Internet infrastructure in Saudi Arabia, users information sources about companies in Saudi Arabia, respondents perception about the advantages and disadvantages in Internet financial reporting (IFR), respondents attitude to the quality of IFR provided by Saudi public companies and the impact of IFR on users information needs. Overall, it was found professional groups (Institutional investors, financial analysts) hold similar views in relation to many issues, while the opinions of private investors differ considerably. Second, the thesis examines the use of the Internet for the disclosure of financial and investor-related information by Saudi public companies (113 companies) and look to identify reasons for the differences in the online disclosure practices of companies by testing the association between eight firm-specific factors and the level of online disclosure. The financial disclosure index (167 items) is used to measure public company disclosure in Saudi Arabia. The descriptive part of the study reveals that 95 (84%) of the Saudi public companies in the sample had a website and 51 (45%) had a financial information section of some description. Furthermore, none of the sample companies provided 100% of the 167 index items applicable to the company. Results of multivariate analysis show that firm size and stock market listing are significant explanatory variables for the amount of information disclosed on corporate Web sites. The thesis finds a significant and negative relationship between the proportion of institutional ownership of a companys shares and the level of IFR.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.