Managerial ownership concentration and agency conflict using logistic regression approach : evidence from Bursa Malaysia

This paper adopted logistic regression model to examine the relationship between level of managerial ownership concentration and agency conflict which are proxied by level of risk, firms leverage and firms dividend policy. The study covers a period of 5 years from 1997 through 2001. The study is based on the 100 blue-chip stocks, majority of which are derived from CI components. The findings suggest a positive and significant association between level of level of risk at lower level and managerial ownership while a negative and significant association is also evidenced between risk at higher level and managerial ownership concentration. While debt policy which serves as positive monitoring substitute for agency conflict is found to be positive and significant explaining the level of ownership concentration. Furthermore, dividend policies, which also serve as monitoring, substitute to reduce agency conflict between manager and external shareholders do not appear to have any significant impact on managerial ownership. On the other hand, the level of institutional ownership, which serves as external monitoring force, is found to have inverse impact on level of managerial ownership concentration. This is marginally significant at 10 level (p=.12). The findings, in part explain the argument that the managerial ownership help reduce agency conflict between outside equity holders and managers.

A fast malware feature selection approach using a hybrid of multi-linear and stepwise binary logistic regression

Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation anti-virus engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current anti-virus engines in detecting malware. In this paper, we propose a stepwise binary logistic regression-based dimensionality reduction techniques for malware detection using application program interface (API) call statistics. Finding the most significant malware feature using traditional wrapper-based approaches takes an exponential complexity of the dimension (m) of the dataset with a brute-force search strategies and order of (m-1) complexity with a backward elimination filter heuristics. The novelty of the proposed approach is that it finds the worst case computational complexity which is less than order of (m-1). The proposed approach uses multi-linear regression and the p-value of each individual API feature for selection of the most uncorrelated and significant features in order to reduce the dimensionality of the large malware data and to ensure the absence of multi-collinearity. The stepwise logistic regression approach is then employed to test the significance of the individual malware feature based on their corresponding Wald statistic and to construct the binary decision the model. When the selected most significant APIs are used in a decision rule generation systems, this approach not only reduces the tree size but also improves classification performance. Exhaustive experiments on a large malware data set show that the proposed approach clearly exceeds the existing standard decision rule, support vector machine-based template approach with complete data and provides a better statistical fitness.

Dysglycaemia and other predictors for progression or regression from impaired fasting glucose to diabetes or normoglycaemia

Aims. Diabetes mellitus is a growing health problem worldwide. This study aimed to describe dysglycaemia and determine the impact of body composition and clinical and lifestyle factors on the risk of progression or regression from impaired fasting glucose (IFG) to diabetes or normoglycaemia in Australian women. Methods. This study included 1167 women, aged 20-94 years, enrolled in the Geelong Osteoporosis Study. Multivariable logistic regression was used to identify predictors for progression to diabetes or regression to normoglycaemia (from IFG), over 10 years of follow-up. Results. At baseline the proportion of women with IFG was 33.8% and 6.5% had diabetes. Those with fasting dysglycaemia had higher obesity-related factors, lower serum HDL cholesterol, and lower physical activity. Over a decade, the incidence of progression from IFG to diabetes was 18.1 per 1,000 person-years (95% CI, 10.7-28.2). Fasting plasma glucose and serum triglycerides were important factors in both progression to diabetes and regression to normoglycaemia. Conclusions. Our results show a transitional process; those with IFG had risk factors intermediate to normoglycaemics and those with diabetes. This investigation may help target interventions to those with IFG at high risk of progression to diabetes and thereby prevent cases of diabetes.

Management attitudes to corporate governance issues : evidence from Sri Lanka

This study investigates the attitudes of senior managers in Sri Lankan firms to governance issues using a countrywide cross-sectional survey. Respondents from 64 public firms provide information on manager's attitudes to internal control procedures: (1) producing misleading financial reports, (2) providing faulty investment advice, (3) permitting insider-trading, and (4) providing inaccurate advertising. We establish if these attitudes vary with 5 firm-specific factors: industry group, international exposure of firms, size, whether the firm was listed or not, and whether the firm had a written code of ethics. Employing ordinal logistic regression techniques, the results demonstrate significant variation by respondents within different types of firms. Specifically there was little variation to these issues when respondents were classified by industry, with most variation when classified by international involvement. Respondents from firms with significant international exposures were strongly opposed to most practices, while respondents from firms with written codes of ethics were strongly opposed to the production of misleading reports and insider-trading. Interestingly respondents from listed firms were most opposed to insider-trading, while smaller firms were more opposed to misleading advertising than respondents from larger firms. The results have important implications for the implementation of corporate governance practice.

Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south-eastern Australia

The pathogen Phytophthora cinnamomi causes extensive 'dieback' of Australian native vegetation. This study investigated the distribution of infection in an area of significant sclerophyll vegetation in Australia. It aimed to determine the relationship of infection to site variables and to develop a predictive model of infection. Site variables recorded at 50 study sites included aspect, slope, altitude, proximity to road and road characteristics, soil profile characteristics and vegetation attributes. Soil and plant tissues were assayed for the presence of the pathogen. A geographical information systyem (GIS) was employed to provide accurate estimations of spatial variables and develop a predictive model for the distribution of P. cinnamomi. The pathogen was isolated from 76% of the study sites. Of the 17 site variables initially investigated during the study a logistic regression model identified only two, elevation and sun-index, as significant in determining the probability of infection. The presence of P. cinnamomi infection was negatively associated with elevation and positively associated with sun-index. The model predicted that up to 74% of the study area (11 875 ha) had a high probability of being affected by P. cinnamomi. However, the present areas of infection were small, providing an opportunity for management to minimize spread into highly susceptible uninvaded areas.

Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach

1. To develop a conservation management plan for a species, knowledge of its distribution and spatial arrangement of preferred habitat is essential. This is a difficult task, especially when the species of concern is in low   abundance. In south-western Victoria, Australia, populations of the rare rufous bristlebird Dasyornis broadbenti are threatened by fragmentation of suitable habitat. In order to improve the conservation status of this species, critical habitat requirements must be identified and a system of corridors must be established to link known populations. A predictive spatial model of rufous bristlebird habitat was developed in order to identify critical areas requiring preservation, such as corridors for dispersal.
2. Habitat models generated using generalized linear modelling techniques can assist in delineating the specific habitat requirements of a species. Coupled with geographic information system (GIS) technology, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitat.
3. Models were generated using logistic regression, with bristlebird presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multispectral digital imagery, as the predictors. A multimodel inference approach based on Akaike’s information criterion was used and the resulting model was applied in a GIS to extrapolate predicted likelihood of occurrence across the entire area of concern. The predictive performance of the selected model was evaluated using the receiver operating characteristic (ROC) technique. A hierarchical partitioning protocol was used to identify the predictor variables most likely to influence variation in the dependent variable. Probability of species presence was used as an index of habitat suitability.
4. Negative associations between rufous bristlebird presence and  increasing elevation, 'distance to cree', 'distance to coast' and sun index were evident, suggesting a preference for areas relatively low in altitude, in close proximity to the coastal fringe and drainage lines, and receiving less direct sunlight. A positive association with increasing habitat complexity also suggested that this species prefers areas containing high vertical density of vegetation.
5. The predictive performance of the selected model was shown to be high (area under the curve 0·97), indicating a good fit of the model to the data. Hierarchical partitioning analysis showed that all the variables considered had significant  independent contributions towards explaining the variation in the dependent variable. The proportion of the total study area that was predicted as suitable habitat for the rufous bristlebird (using probability of occurrence at a ≥0·5 level ) was 16%.
6. Synthesis and applications. The spatial model clearly delineated areas predicted as highly suitable rufous bristlebird habitat, with evidence of potential corridors linking coastal and inland populations via gullies. Conservation of this species will depend on management actions that protect the critical habitats identified in the model. A multi-scale  approach to the modelling process is recommended whereby a spatially explicit model is first generated using landscape variables extracted from a GIS, and a second model at site level is developed using fine-scale habitat variables measured on the ground. Where there are constraints on the time and cost involved in measuring finer scale variables, the first step alone can be used for conservation planning.

Landscape characteristics associated with species richness and occurrence of small native mammals inhabiting a coastal heathland: a spatial modelling approach

In the coastal region of south-western Victoria, Australia, populations of native small mammal species are restricted to patches of suitable habitat in a highly fragmented landscape. The size and spatial arrangement of these patches is likely to influence both the occupancy and richness of species at a location. Geographic Information System (GIS)-based habitat models of the species richness of native small mammals, and individual species  occurrences, were developed to produce maps displaying the spatial  configuration of suitable habitat. Models were generated using either generalised linear Poisson regression (for species richness) or logistic regression (for species occurrences) with species richness or  presence/absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictor variables. A multi-model inference approach based on the Akaike Information Criterion was used and the resulting model was applied in a GIS framework to extrapolate predicted richness/likelihood of occurrence across the entire area of the study. A negative association between species  richness and elevation, habitat complexity and sun index indicated that richness within the study area decreases with increasing altitude, vertical vegetation structure and exposure to solar radiation. Landform  characteristics were important (to varying degrees) in determining habitat occupancy for all of the species examined, while the influence of habitat complexity was important for only one of the species. Performance of all but one of the models generated using presence/absence data was high, as indicated by the area under the curve of a receiver-operating characteristic plot. The effective conservation of the small mammal species in the area of concern is likely to depend on management actions that promote the protection of the critical habitats identified in the models.

Modelling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria, Australia

In recent years, predictive habitat distribution models, derived by combining multivariate statistical analyses with Geographic Information System (GIS) technology, have been recognised for their utility in conservation planning. The size and spatial arrangement of suitable habitat can influence the long-term persistence of some faunal species. In southwestern Victoria, Australia, populations of the rare swamp antechinus (Antechinus minimus maritimus) are threatened by further fragmentation of suitable habitat. In the current study, a spatially explicit habitat suitability model was developed for A. minimus that incorporated a measure of vegetation structure. Models were generated using logistic regression with species presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictors. The most parsimonious model, based on the Akaike Information Criterion, was spatially extrapolated in the GIS. Probability of species presence was used as an index of habitat suitability. A negative association between A. minimus presence and both elevation and habitat complexity was evidenced, suggesting a preference for relatively low altitudes and a vegetation structure of low vertical complexity. The predictive performance of the selected model was shown to be high (91%), indicating a good fit of the model to the data. The proportion of the study area predicted as suitable habitat for A. minimus (Probability of occurrence greater-or-equal, slanted0.5) was 11.7%. Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for other populations, making them an important conservation tool.