Are target date funds the easy bake option?

Target date funds provide a simple, automated approach to retirement savings in defined contribution plans. The passing of the Pension Protection Act of 2006 has seen an increase in the popularity of these funds in the United States, becoming the default option for many plans. However, recent research findings have challenged the easy bake or ‘set-and-forget’ nature of target date funds. This study explores some of the critical design features of target date funds (which shifts an individual’s asset allocation from growth to defensive assets following a pre-set glidepath) against a simple balanced (or target risk) fund design. Using both time-weighted and dollar-weighted returns, our results suggest that there is more to achieving successful retirement outcomes than the investor simply selecting a proposed year of retirement. Our findings can perhaps be summarized by Einstein’s famous epithet, that in the murky world of retirement product design, everything should be made as simple as possible, but not simpler.

Principles of investments

"Emphasises asset allocation while presenting the practical applications of investment theory. The authors concentrate on the intuition and insights that will be useful to students throughout their careers as new ideas and challenges emerge from the financial marketplace. It provides a good foundation to understand the basic types of securities and financial markets as well as how trading in those markets is conducted. The Portfolio Management section is discussed towards the end of the course and supported by a web-based portfolio simulation with a hypothetical $100,000 brokerage account to buy and sell stocks and mutual funds. Students get a chance to use real data found in the Wall Street Survivor simulation in conjunction with the chapters on investments. This site is powered by StockTrak, the leading provider of investment simulation services to the academic community. Principles of Investments includes increased attention to changes in market structure and trading technology. The theory is supported by a wide range of exercises, worksheets and problems."--publisher website Contents: Investments: background and issues -- Asset classes and financial markets -- Securities markets -- Managed funds and investment management -- Risk and return: past and prologue -- Efficient diversification -- Capital asset pricing and arbitrage pricing theory -- The efficient market hypothesis -- Bond prices and yields -- Managing bond portfolios -- Equity valuation -- Macroeconomic and industry analysis -- Financial statement analysis -- Investors and the investment process -- Hedge funds -- Portfolio performance evaluation.

A new method for quantifying the contribution of different critical agents on railway bridge deterioration

Railway Bridges deteriorate over time due to different critical factors including, flood, wind, earthquake, collision, and environment factors, such as corrosion, wear, termite attack, etc. In current practice, the contributions of the critical factors, towards the deterioration of railway bridges, which show their criticalities, are not appropriately taken into account. In this paper, a new method for quantifying the criticality of these factors will be introduced. The available knowledge as well as risk analyses conducted in different Australian standards and developed for bridge-design will be adopted. The analytic hierarchy process (AHP) is utilized for prioritising the factors. The method is used for synthetic rating of railway bridges developed by the authors of this paper. Enhancing the reliability of predicting the vulnerability of railway bridges to the critical factors, will be the significant achievement of this research.

A novel method for quantifying the criticality and vulnerability of the components of railway bridges

Conditions of bridges deteriorate with age, due to different critical factors including, changes in loading, fatigue, environmental effects and natural events. In order to rate a network of bridges, based on their structural condition, the condition of the components of a bridge and their effects on behaviour of the bridge should be reliably estimated. In this paper, a new method for quantifying the criticality and vulnerability of the components of the railway bridges in a network will be introduced. The type of structural analyses for identifying the criticality of the components for carrying train loads will be determined. In addition to that, the analytical methods for identifying the vulnerability of the components to natural events whose probability of occurrence is important, such as, flood, wind, earthquake and collision will be determined. In order to maintain the practicality of this method to be applied to a network of thousands of railway bridges, the simplicity of structural analysis has been taken into account. Demand by capacity ratios of the components at both safety and serviceability condition states as well as weighting factors used in current bridge management systems (BMS) are taken into consideration. It will be explained what types of information related to the structural condition of a bridge is required to be obtained, recorded and analysed. The authors of this paper will use this method in a new rating system introduced previously. Enhancing accuracy and reliability of evaluating and predicting the vulnerability of railway bridges to environmental effects and natural events will be the significant achievement of this research.

Spreading peak demand for urban rail transit through differential fare policy : a review of empirical evidence

More evenly spread demand for public transport throughout a day can reduce transit service provider‟s total asset and labour costs. A plausible peak spreading strategy is to increase peak fare and/or to reduce off-peak fare. This paper reviews relevant empirical studies for urban rail systems, as rail transit plays a key role in Australian urban passenger transport and experiences severe peak loading variability. The literature is categorised into four groups: a) passenger opinions on willingness to change time for travel, b) valuations of displacement time using stated preference technique, c) simulations of peak spreading based on trip scheduling models, and: d) real-world cases of peak spreading using differential fare. Policy prescription is advised to take into account impacts of traveller‟s time flexibility and joint effects of mode shifting and peak spreading. Although focusing on urban rail, arguments in this paper are relevant to public transport in general with values to researchers and practitioners.

Assessing the suitability of market approach valuation methods for mine and quarry valuations

An area of property valuation that has attracted less attention than other property markets over the past 20 years has been the mining and extractive industries. These operations can range from small operators on leased or private land to multinational companies. Although there are a number of national mining standards that indicate the type of valuation methods that can be adopted for this asset class, these standards do not specify how or when these methods are best suited to particular mine operations. The RICS guidance notes and the draft IVSC guidance notes also advise the various valuations methods that can be used to value mining properties; but, again they do not specify what methods should be applied where and when. One of the methods supported by these standards and guidelines is the market approach. This paper will carry out an analysis of all mine, extractive industry and waste disposal sites sale transactions in Queensland Australia, a major world mining centre, to determine if a market valuation approach such as direct comparison is actually suitable for the valuation of a mine or extractive industry. The analysis will cover the period 1984 to 2011 and covers sale transactions for minerals, petroleum and gas, waste disposal sites, clay, sand and stone. Based on this analysis, the suitability of direct comparison for valuation purposes in this property sector will be tested.

Knowledge Management Strategies in Technopreneurial Firms

Intellectual capital is increasingly viewed as the single most important asset of organisations. While most large organisations have resources, staff and plans in place to support and develop intellectual capital, many smaller organisations do not. In particular technology-oriented young firms (technopreneurial firms), which play an important role in innovation and commercialisation of new ideas, do not have well developed strategies for managing their intellectual capital. These firms are often founded by engineers, scientists or academics who posses great scientific/technological knowledge, but limited know-how in other aspects of managing a business including knowledge management (KM). Successful managing and integrating their specialised knowledge is of particular importance when it comes to developing a new product or process. This article therefore focuses on developing strategies for knowledge management within technopreneurial organisations as they incorporate technology and strive to build and retain a productive and creative workforce.

Multi-criteria optimization of group replacement schedules for distributed water pipeline assets

This thesis presents a multi-criteria optimisation study of group replacement schedules for water pipelines, which is a capital-intensive and service critical decision. A new mathematical model was developed, which minimises total replacement costs while maintaining a satisfactory level of services. The research outcomes are expected to enrich the body of knowledge of multi-criteria decision optimisation, where group scheduling is required. The model has the potential to optimise replacement planning for other types of linear asset networks resulting in bottom-line benefits for end users and communities. The results of a real case study show that the new model can effectively reduced the total costs and service interruptions.

Response of a buried tunnel to surface blast using different numerical techniques

This paper presents a comparative study on the response of a buried tunnel to surface blast using the arbitrary Lagrangian-Eulerian (ALE) and smooth particle hydrodynamics (SPH) techniques. Since explosive tests with real physical models are extremely risky and expensive, the results of a centrifuge test were used to validate the numerical techniques. The numerical study shows that the ALE predictions were faster and closer to the experimental results than those from the SPH simulations which over predicted the strains. The findings of this research demonstrate the superiority of the ALE modelling techniques for the present study. They also provide a comprehensive understanding of the preferred ALE modelling techniques which can be used to investigate the surface blast response of underground tunnels.

Learning routines used by firms during construction of infrastructure projects using the alliance procurement method

Collaborative contracting has emerged over the past 15 years as an innovative project delivery framework that is particularly suited to infrastructure projects. Australia leads the world in the development of project and program alliance approaches to collaborative delivery. These approaches are considered to promise superior project results. However, very little is known about the learning routines that are most widely used in support of collaborative projects in general and alliance projects in particular. The literature on absorptive capacity and dynamic capabilities indicates that such learning enhances project performance. The learning routines employed at corporate level during the operation of collaborative infrastructure projects in Australia were examined through a large survey conducted in 2013. This paper presents a descriptive summary of the preliminary findings. The survey captured the experiences of 320 practitioners of collaborative construction projects, including public and private sector clients, contractors, consultants and suppliers (three per cent of projects were located in New Zealand, but for brevity’s sake the sample is referred to as Australian). The majority of projects identified used alliances (78.6%); whilst 9% used Early Contractor Involvement (ECI) contracts and 2.7% used Early Tender Involvement contracts, which are ‘slimmer’ types of collaborative contract. The remaining 9.7% of respondents used traditional contracts that employed some collaborative elements. The majority of projects were delivered for public sector clients (86.3%), and/or clients experienced with asset procurement (89.6%). All of the projects delivered infrastructure assets; one third in the road sector, one third in the water sector, one fifth in the rail sector, and the rest spread across energy, building and mining. Learning routines were explored within three interconnected phases: knowledge exploration, transformation and exploitation. The results show that explorative and exploitative learning routines were applied to a similar extent. Transformative routines were applied to a relatively low extent. It was also found that the most highly applied routine is ‘regularly applying new knowledge to collaborative projects’; and the least popular routine was ‘staff incentives to encourage information sharing about collaborative projects’. Future research planned by the authors will examine the impact of these routines on project performance.

The politics of Twitter data

Talk of Big Data seems to be everywhere. Indeed, the apparently value-free concept of ‘data’ has seen a spectacular broadening of popular interest, shifting from the dry terminology of labcoat-wearing scientists to the buzzword du jour of marketers. In the business world, data is increasingly framed as an economic asset of critical importance, a commodity on a par with scarce natural resources (Backaitis, 2012; Rotella, 2012). It is social media that has most visibly brought the Big Data moment to media and communication studies, and beyond it, to the social sciences and humanities. Social media data is one of the most important areas of the rapidly growing data market (Manovich, 2012; Steele, 2011). Massive valuations are attached to companies that directly collect and profit from social media data, such as Facebook and Twitter, as well as to resellers and analytics companies like Gnip and DataSift. The expectation attached to the business models of these companies is that their privileged access to data and the resulting valuable insights into the minds of consumers and voters will make them irreplaceable in the future. Analysts and consultants argue that advanced statistical techniques will allow the detection of ongoing communicative events (natural disasters, political uprisings) and the reliable prediction of future ones (electoral choices, consumption)...

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Physical activity promotion as a school priority : what do parents think?

The demands and responsibilities placed on schools in contemporary education systems are vast. However, with growing obesity levels and physical inactivity, the prevention of chronic disease has focused on youth populations, with schools playing the focal educative asset in this strategy. Parents play a decisive role in their child’s educational setting, and as fee and tax payers, are ultimately a consumer. Parents (82 males and 208 females) of secondary school children were recruited from three private (n=151) and two government schools (n=150) in Brisbane, Australia. The mean (standard deviation) age was 44.57 (6.21) years. Participants responded to a series of questions about physical activity at their child’s school, in addition to completing the International Physical Activity Questionnaire. Data were analysed using descriptive statistics, frequency distributions and logistic regressions. Parents were deemed sufficiently physically active if they participated in at least 150 minutes of moderate-to-vigorous physical activity per week. Overall, 83 (59.7%) parents from private and 60 (50.8%) parents from government schools were deemed sufficiently physically active. Concerning whether physical activity promotion should be a priority at their child’s school, 111 (73.5%) parents from private schools either agreed or strongly agreed, as opposed to 97 (64.7%) parents from government schools. Logistic regressions indicated that the concept of physical activity promotion being prioritised at schools was dependent on whether the child attended a private school (OR =1.34, z = 2.30, p = 0.02), and whether the participant was sufficiently active (OR =.71, z = -2.48, p = 0.01). Physical activity promotion within schools may provide substantial future benefits on a population scale. The demands on schools may need to be addressed to meet the needs of students and the desires of their parents.

The promise of justice reinvestment

Abstract: This article examines the notion and practice of Justice Reinvestment (‘JR’), an emerging approach addressing the high social and economic costs of soaring incarceration rates. JR invests in public safety by reallocating dollars from corrections budgets to finance education, housing, healthcare, and jobs in high-crime communities. Key distinguishing features of JR (including justice and asset mapping, budgetary devolution and localism, and the desirability of bipartisanship) are briefly outlined, followed by discussion of its recent emergence and application in the United States, and to a lesser extent in the United Kingdom. The prospects for the adoption of JR approaches in Australia are then considered, with particular reference to the high imprisonment rates of Indigenous people. If JR is to be promoted in the Australian context it is important that it be subject to critical scrutiny and therefore some of the key problems are briefly outlined, before a conclusion which emphasizes the potential benefits of JR.

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of -1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required.