Loyalty to the profession? A study of Australian financial planners

The purpose of this paper is to examine the demographic variables of age and gender in conjunction with three independent variables: Internal versus external locus of control personality dimension, individualist versus collectivist personality dimension, and perceived environmental uncertainty and to relate same to the professional commitment (PC) of financial planners in Australia. A questionnaire was used to survey a sample of 312 financial planners nationally, with a 36% response rate and statistically significant results. At the 90% confidence level (p=0.10) respondents over the age of 35 demonstrated no difference between their levels of professional commitment than did those under the age of 35, while at the same level of confidence, females demonstrated a statistically significant higher level of PC than did their male counterparts. Respondents with an external locus of control displayed lower levels of PC (p=0.10) that those with an internal locus of control. These findings contribute to our understanding of the professional commitment of financial planners, and are important from a public policy perspective in an era of increasing attention to, and likely increased regulation of, the financial planning industry.

Organisational commitment of financial planners in Australia

The purpose of this paper is to determine whether the demographic variables of age, gender and length of service can be shown to be related to the organisational commitment (OC) of financial planners in Australia. The financial planners were surveyed using an instrument derived from established questionnaires. It was mailed nationally to 312 financial planners. A response rate of 36% was achieved, equating to 113 useable responses. The analyses revealed statistically significant results at the 90% confidence level (p=0.10), that respondents over the age of 35 demonstrated a significantly higher level of OC than did those under the age of 35, and at the same level of confidence, females demonstrated a statistically significant higher level of OC than did their male counterparts.

Such findings contribute to our understanding of the organisational commitment of Financial Planners, and have implications for employers in terms of hiring and retention of employees. The analyses are also important from a public policy perspective in an era of increasing attention given to, and likely increased regulation of, the financial planning industry.

An examination of the organisational commitment of financial planners

This paper sets out to determine whether the demographic variables of age, gender, length of service can be shown to be related to the organisational commitment (OC) of financial planners in Australia. The financial planners were surveyed using an instrument derived from established questionnaires. It was mailed nationally to 312 financial planners. A response rate of 36% was achieved, equating to 113 useable responses. The analyses revealed statistically significant results at the 95% confidence level (p=0.05), that female respondents demonstrated a statistically significant higher level of OC than did their male counterparts.Such findings contribute to our understanding of the organisational commitment of Financial Planners, and have implications for employers in terms of hiring and retention of employees. The analyses are also important from a public policy perspective in an era of increasing attention given to, and likely increased regulation of, the financial planning industry.

Professional commitment of financial planners in Australia: an examination

The purpose of this paper is to examine the demographic variables of age and gender in conjunction with three independent variables: Internal versus external locus of control personality dimension, individualist versus collectivist personality dimension, and perceived environmental uncertainty and to relate same to the professional commitment (PC) of financial planners in Australia. A questionnaire was used to survey a sample of 312 financial planners nationally, with a 36% response rate and statistically significant results. At the 95% confidence level (p=0.05) respondents over the age of 35 demonstrated no difference between their levels of professional commitment than did those under the age of 35, while at the same level of confidence, females demonstrated no statistically significant difference in their level of PC than did their male counterparts. Respondents with an external locus of control displayed lower levels of PC (p=0.05), than those with an internal locus of control but these were not statistically significant. These findings contribute to our understanding of the professional commitment of financial planners, and are important from a public policy perspective in an era of increasing attention to, and likely increased regulation of, the financial planning industry.

Financial planners in Australia : an examination of loyalty

The purpose of this paper is to determine whether the demographic variables of age, gender and length of service, and the levels of the three independent variables of internal versus external locus of control personality dimension, individualist versus collectivist personality dimension, and perceived environmental uncertainty can be shown to be related to the organisational commitment (OC) and professional commitment (PC) of financial planners in Australia. The financial planners employed by one major Australian bank, during the period November to December, 2004 were surveyed using an instrument derived from established questionnaires. It was mailed nationally to 312 financial planners. A response rate of 36% was achieved, equating to 113 useable responses. The analyses revealed no statistically significant results at the 95% confidence level (p=0.05), that the level of OC and PC for respondents over the age of 35 differed from those under the age of 35. At the same level of confidence, females demonstrated a statistically significant higher level of OC than did their male counterparts, however there was no difference between their levels of PC. Financial planners employed for a period of over 3 years showed no difference in their levels of OC or PC than those employed for a period of less than 3 years (p=0.05). Respondents with an external locus of control displayed  statistically significant lower levels of OC than those with an internal locus of control, however there was no difference between these groups in their levels of PC (p=0.05). Such findings contribute to our understanding of the organisational and professional commitment of financial planners, and have implications for employers in terms of hiring and retention of employees. The analyses are also important from a public policy perspective in an era of increasing attention to, and likely increased regulation of, the financial planning industry.

Estimating process capability index Cpm using a bootstrap sequential sampling procedure

Construction of a confidence interval for process capability index CPM is often based on a normal approximation with fixed sample size. In this article, we describe a different approach in constructing a fixed-width confidence interval for process capability index CPM with a preassigned accuracy by using a combination of bootstrap and sequential sampling schemes. The optimal sample size required to achieve a preassigned confidence level is obtained using both two-stage and modified two-stage sequential procedures. The procedure developed is also validated using an extensive simulation study.

Prediction interval construction using interval type-2 fuzzy logic systems

This study proposes a novel non-parametric method for construction of prediction intervals (PIs) using interval type-2 Takagi-Sugeno-Kang fuzzy logic systems (IT2 TSK FLSs). The key idea in the proposed method is to treat the left and right end points of the type-reduced set as the lower and upper bounds of a PI. This allows us to construct PIs without making any special assumption about the data distribution. A new training algorithm is developed to satisfy conditions imposed by the associated confidence level on PIs. Proper adjustment of premise and consequent parameters of IT2 TSK FLSs is performed through the minimization of a PI-based objective function, rather than traditional error-based cost functions. This new cost function covers both validity and informativeness aspects of PIs. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Quantitative measures are applied for assessing the quality of PIs constructed using IT2 TSK FLSs. The demonstrated results for four benchmark case studies with homogenous and heterogeneous noise clearly show the proposed method is capable of generating high quality PIs useful for decision-making.

Uncertainty quantification for wind farm power generation

Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.

Towards automated detection of depression from brain structural magnetic resonance images

Introduction : Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).Methods : Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.Results : The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.Conclusion : Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.

Prediction intervals for short-term wind farm power generation forecasts

Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.

An interval type-2 fuzzy logic system-based method for prediction interval construction

This paper introduces a new non-parametric method for uncertainty quantification through construction of prediction intervals (PIs). The method takes the left and right end points of the type-reduced set of an interval type-2 fuzzy logic system (IT2FLS) model as the lower and upper bounds of a PI. No assumption is made in regard to the data distribution, behaviour, and patterns when developing intervals. A training method is proposed to link the confidence level (CL) concept of PIs to the intervals generated by IT2FLS models. The new PI-based training algorithm not only ensures that PIs constructed using IT2FLS models satisfy the CL requirements, but also reduces widths of PIs and generates practically informative PIs. Proper adjustment of parameters of IT2FLSs is performed through the minimization of a PI-based objective function. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Performance of the proposed method is examined for seven synthetic and real world benchmark case studies with homogenous and heterogeneous noise. The demonstrated results indicate that the proposed method is capable of generating high quality PIs. Comparative studies also show that the performance of the proposed method is equal to or better than traditional neural network-based methods for construction of PIs in more than 90% of cases. The superiority is more evident for the case of data with a heterogeneous noise. © 2014 Elsevier B.V.

Constructing optimal prediction intervals by using neural networks and bootstrap method

This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Prediction interval estimation for wind farm power generation forecasts using support vector machines

Accurate forecasting of wind power generation is quite an important as well as challenging task for the system operators and market participants due to its high uncertainty. It is essential to quantify uncertainties associated with wind power generation forecasts for their efficient application in optimal management of wind farms and integration into power systems. Prediction intervals (PIs) are well known statistical tools which are used to quantify the uncertainty related to forecasts by estimating the ranges of the future target variables. This paper investigates the application of a novel support vector machine based methodology to directly estimate the lower and upper bounds of the PIs without expensive computational burden and inaccurate assumptions about the distribution of the data. The efficiency of the method for uncertainty quantification is examined using monthly data from a wind farm in Australia. PIs for short term application are generated with a confidence level of 90%. Experimental results confirm the ability of the method in constructing reliable PIs without resorting to complex computational methods.

The management of adolescents at risk of suicide by Australian general practitioners: issues in practice and confidence

This study involved an anonymous survey of 41Victorian GPs regarding their diagnostic and treatment practices with adolescent patients with depression and/or suicide ideation. The results indicated that the majority of respondents correctly diagnosed the level of depression and the risk of suicide in a case scenario. Although they commonly asked some of the questions related to an assessment of suicide risk, they rarely conducted a comprehensive risk assessment and the level of referral to telephone and internet crisis services was poor. Most GPs indicated a lack of confidence in their ability to detect and manage depression and suicide in this population and strongly emphasized a need for more training.