Concurrent momentum and contrarian strategies in the Australian stock market

We investigate the coexistence of momentum and contrarian strategies in the Australian equity market from 1992 to 2011. We show that contrarian strategies prevail in the short-term investment horizon while momentum strategies dominate in the intermediate- and long-term horizons. However, only short-term contrarian strategies significantly outperform the simple buy-and-hold strategy of investing in the market index over the same period. Further examination of these strategies shows that the Australian mining sector undermines the performance of momentum while enhancing performance of contrarian strategies. Lastly, using both parametric and non-parametric approaches, we show that these strategies’ returns are persistent anomalies and not completely explained by standard return-generating models.

Geographical dimensions of imagined futures: post school participation in education and work in peri-urban and regional Australia

This paper discusses preliminary findings from a sub-set of empirical data collected for a recent NCVER study that explored the geographic dimensions of social exclusion in four locations in Victoria and South Australia with lower than average post school education participation. Set against the policy context of the Bradley Review (2008) and the drive to increase the post-school participation of young people from low socio-economic status neighbourhoods, this qualitative research study, responding to identified gaps in the literature, sought a nuanced understanding of how young people make decisions about their post-school pathways. Drawing on Appadurai’s (2004) concept ‘horizons of aspiration’ the paper explores the aspirations of two young people formed from, and within, their particular rural ‘neighborhoods’. The paper reveals how their post-school education and work choices, imagined futures and conceptions of a ‘good life’, have topographic and gendered influences that are important considerations for policy makers.

Extinction risk in cloud forest fragments under climate change and habitat loss

Aim: To quantify the consequences of major threats to biodiversity, such as climate and land-use change, it is important to use explicit measures of species persistence, such as extinction risk. The extinction risk of metapopulations can be approximated through simple models, providing a regional snapshot of the extinction probability of a species. We evaluated the extinction risk of three species under different climate change scenarios in three different regions of the Mexican cloud forest, a highly fragmented habitat that is particularly vulnerable to climate change. Location Cloud forests in Mexico.
Methods: Using Maxent, we estimated the potential distribution of cloud forest for three different time horizons (2030, 2050 and 2080) and their overlap with protected areas. Then, we calculated the extinction risk of three contrasting vertebrate species for two scenarios: (1) climate change only (all suitable areas of cloud forest through time) and (2) climate and land-use change (only suitable areas within a currently protected area), using an explicit patch-occupancy approximation model and calculating the joint probability of all populations becoming extinct when the number of remaining patches was less than five.
Results: Our results show that the extent of environmentally suitable areas for cloud forest in Mexico will sharply decline in the next 70 years. We discovered that if all habitat outside protected areas is transformed, then only species with small area requirements are likely to persist. With habitat loss through climate change only, high dispersal rates are sufficient for persistence, but this requires protection of all remaining cloud forest areas.
Main conclusions: Even if high dispersal rates mitigate the extinction risk of species due to climate change, the synergistic impacts of changing climate and land use further threaten the persistence of species with higher area requirements. Our approach for assessing the impacts of threats on biodiversity is particularly useful when there is little time or data for detailed population viability analyses.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.