Return-based style analysis in Australian funds

This study applies return-based style analysis to a sample of Australian managed and superannuation funds, seeking to compare their asset allocation strategies across different style groups. Style analysis is performed using a rolling window estimation technique. As expected, riskier fund classes are more exposed to the riskier benchmarks. Further, differences in institutional and legal settings lead the managers of managed and superannuation funds to invest differently, with the latter employing a more conservative investment strategy despite having longer investment horizons.

Dynamic price discrimination with customer recognition

This paper studies a general two-period model of product line pricing with customer recognition. Specifically, we consider a monopolist who can sell vertically differentiated products over two periods to heterogeneous consumers. Each consumer demands one unit of the product in each period. In the second period, the monopolist can condition the price-quality offers on the observed purchasing behavior in the first period. In this setup, the monopolist can price discriminate consumers in two dimensions: by quality as well as by purchase history. We fully characterize the monopolist's optimal pricing strategy when there are two types of consumers. When the type space is a continuum, we show that there is no fully separating equilibrium, and some properties of the optimal contracts (price-quality pairs) are characterized within the class of partitional perfect Bayesian equilibria.

Aggregate volatility risk and the cross-section of stock returns: Australian evidence

This study examines the relation between aggregate volatility risk and the cross-section of stock returns in Australia. We use a stock's sensitivity to innovations in the ASX200 implied volatility (VIX) as a proxy for aggregate volatility risk. Consistent with theoretical predictions, aggregate volatility risk is negatively related to the cross-section of stock returns only when market volatility is rising. The asymmetric volatility effect is persistent throughout the sample period and is robust after controlling for size, book-to-market, momentum, and liquidity issues. There is some evidence that aggregate volatility risk is a priced factor, especially in months with increasing market volatility.

Parameter optimization of kernel-based one-class classifier on imbalance learning

Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to learn from unbalanced data set is regarded as the recognition based learning and has shown to have the potential of achieving better performance. Similar to twoclass learning, parameter selection is a significant issue, especially when the classifier is sensitive to the parameters. For one-class learning scheme with the kernel function, such as one-class Support Vector Machine and Support Vector Data Description, besides the parameters involved in the kernel, there is another one-class specific parameter: the rejection rate v. In this paper, we proposed a general framework to involve the majority class in solving the parameter selection problem. In this framework, we first use the minority target class for training in the one-class classification stage; then we use both minority and majority class for estimating the generalization performance of the constructed classifier. This generalization performance is set as the optimization criteria. We employed the Grid search and Experiment Design search to attain various parameter settings. Experiments on UCI and Reuters text data show that the parameter optimized one-class classifiers outperform all the standard one-class learning schemes we examined.

Kernel-based naive bayes classifier for breast cancer prediction

The classification of breast cancer patients is of great importance in cancer diagnosis. Most classical cancer classification methods are clinical-based and have limited diagnostic ability. The recent advances in machine learning technique has made a great impact in cancer diagnosis. In this research, we develop a new algorithm: Kernel-Based Naive Bayes (KBNB) to classify breast cancer tumor based on memography data. The performance of the proposed algorithm is compared with that of classical navie bayes algorithm and kernel-based decision tree algorithm C4.5. The proposed algorithm is found to outperform in the both cases. We recommend the proposed algorithm could be used as a tool to classify the breast patient for early cancer diagnosis.

Software reliability growth models based on local polynomial modeling with kernel smoothing

Software reliability growth models (SRGMs) are extensively employed in software engineering to assess the reliability of software before their release for operational use. These models are usually parametric functions obtained by statistically fitting parametric curves, using Maximum Likelihood estimation or Least–squared method, to the plots of the cumulative number of failures observed N(t) against a period of systematic testing time t. Since the 1970s, a very large number of SRGMs have been proposed in the reliability and software engineering literature and these are often very complex, reflecting the involved testing regime that often took place during the software development process. In this paper we extend some of our previous work by adopting a nonparametric approach to SRGM modeling based on local polynomial modeling with kernel smoothing. These models require very few assumptions, thereby facilitating the estimation process and also rendering them more relevant under a wide variety of situations. Finally, we provide numerical examples where these models will be evaluated and compared.

Association between perception of fault for the crash and function, return to work and health status 1 year after road traffic injury: a registry-based cohort study

Objectives To establish the association between the patient's perception of fault for the crash and 12-month outcomes after non-fatal road traffic injury.Setting Two adult major trauma centres, one regional trauma centre and one metropolitan trauma centre in Victoria, Australia.Participants 2605 adult, orthopaedic trauma patients covered by the state's no-fault third party insurer for road traffic injury, injured between September 2010 and February 2014.Outcome measures EQ-5D-3L, return to work and functional recovery (Glasgow Outcome Scale—Extended score of upper good recovery) at 12 months postinjury.Results After adjusting for key confounders, the adjusted relative risk (ARR) of a functional recovery (0.57, 95% CI 0.46 to 0.69) and return to work (0.92, 95% CI 0.86 to 0.99) were lower for the not at fault compared to the at fault group. The ARR of reporting problems on EQ-5D items was 1.20–1.35 times higher in the not at fault group. Conclusions Patients who were not at fault, or denied being at fault despite a police report of fault, experienced poorer outcomes than the at fault group. Attributing fault to others was associated with poorer outcomes. Interventions to improve coping, or to resolve negative feelings from the crash, could facilitate better outcomes in the future.

A dynamic pricing reverse auction-based resource allocation mechanism in cloud workflow systems

Market-oriented reverse auction is an efficient and cost-effective method for resource allocation in cloud workflow systems since it can dynamically allocate resources depending on the supply-demand relationship of the cloud market. However, during the auction the price of cloud resource is usually fixed, and the current resource allocation mechanisms cannot adapt to the changeable market properly which results in the low efficiency of resource utilization. To address such a problem, a dynamic pricing reverse auction-based resource allocation mechanism is proposed. During the auction, resource providers can change prices according to the trading situation so that our novel mechanism can increase the chances of making a deal and improve efficiency of resource utilization. In addition, resource providers can improve their competitiveness in the market by lowering prices, and thus users can obtain cheaper resources in shorter time which would decrease monetary cost and completion time for workflow execution. Experiments with different situations and problem sizes are conducted for dynamic pricing-based allocation mechanism (DPAM) on resource utilization and the measurement of Time∗Cost (TC). The results show that our DPAM can outperform its representative in resource utilization, monetary cost, and completion time and also obtain the optimal price reduction rates.

Scaling the volatility of credit spreads: evidence from Australian dollar eurobonds

The linear rescaling of the variance of an asset's return is used by many asset pricing models when an annualised risk coefficient is required. However, this approach may not be appropriate for time series, which are not independent and identically distributed (IID). This paper investigates the scaling relationships for daily credit spreads, from January 1995 to May 1998, between AAA-, AA-, and A-rated Australian dollar denominated Eurobonds with maturities of 2, 5, 7, and 10 years. The credit spread return all display similar scaling properties with the estimated standard deviation, based upon a scaling at the square root of time, significantly underestimating the actual level of risk predicted from a normal distribution. These results have implications for risk managers and trading of credit spread instruments.