Forecasting trends in the Brisbane central business district office market

The Queensland Department of Public Works (DPW) holds a significant interest in the Brisbane Central Business District (CBD) in controlling approximately 20 percent of the office space within its confines. This comprises a total of 333,903 square metres of space, of which 170,111 square metres is owned and 163,792 square metres is leased from the private sector. The department’s nominal ownership extends to several enduring, landmark buildings as well as several modern office towers. The portfolio includes the oldest building in the CBD, being the former Commissariat Stores building and one of the newest, a 15,000 square metre office tower under construction at 33 Charlotte Street.

Organized crime : policing illegal business entrepreneurialism

Organized crime in the twenty-first century is a knowledge war that poses an incalculable global threat to the world economy and harm to society - the economic and social costs are estimated at upwards of L20 billion a year for the UK alone (SOCA 2006/7). Organized Crime: Policing Illegal Business Entrepreneurialism offers a unique approach to the tackling of this area by exploring how it works through the conceptual framework of a business enterprise. Structured in three parts, the book progresses systematically through key areas and concepts integral to dealing effectively with the myriad contemporary forms of organised crime and provides insights on where, how and when to disrupt and dismantle a criminal business activity through current policing practices and policies. From the initial set up of a crime business through to the long term forecasting for growth and profitability, the authors dissect and analyse the different phases of the business enterprise and propose a 'Knowledge-Managed Policing' (KMP) approach to criminal entrepreneurialism. Combining conceptual and practical issues, this is a must-have reference for all police professionals, policing academics and government policy makers who are interested in a Strategy-led, Intelligence supported, Knowledge-Managed approach to policing illegal business entrepreneurialism.

The impact of regulatory reforms on the earnings forecasting behaviour of IPO firms

Purpose – The purpose of this paper is to jointly assess the impact of regulatory reform for corporate fundraising in Australia (CLERP Act 1999) and the relaxation of ASX admission rules in 1999, on the accuracy of management earnings forecasts in initial public offer (IPO) prospectuses. The relaxation of ASX listing rules permitted a new category of new economy firms (commitments test entities (CTEs))to list without a prior history of profitability, while the CLERP Act (introduced in 2000) was accompanied by tighter disclosure obligations and stronger enforcement action by the corporate regulator (ASIC). Design/methodology/approach – All IPO earnings forecasts in prospectuses lodged between 1998 and 2003 are examined to assess the pre- and post-CLERP Act impact. Based on active ASIC enforcement action in the post-reform period, IPO firms are hypothesised to provide more accurate forecasts, particularly CTE firms, which are less likely to have a reasonable basis for forecasting. Research models are developed to empirically test the impact of the reforms on CTE and non-CTE IPO firms. Findings – The new regulatory environment has had a positive impact on management forecasting behaviour. In the post-CLERP Act period, the accuracy of prospectus forecasts and their revisions significantly improved and, as expected, the results are primarily driven by CTE firms. However, the majority of prospectus forecasts continue to be materially inaccurate. Originality/value – The results highlight the need to control for both the changing nature of listed firms and the level of enforcement action when examining responses to regulatory changes to corporate fundraising activities.

Semi-parametric forecasting of realized volatility

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.

Forecasting volatility and correlation : the role of option implied measures

Forecasts of volatility and correlation are important inputs into many practical financial problems. Broadly speaking, there are two ways of generating forecasts of these variables. Firstly, time-series models apply a statistical weighting scheme to historical measurements of the variable of interest. The alternative methodology extracts forecasts from the market traded value of option contracts. An efficient options market should be able to produce superior forecasts as it utilises a larger information set of not only historical information but also the market equilibrium expectation of options market participants. While much research has been conducted into the relative merits of these approaches, this thesis extends the literature along several lines through three empirical studies. Firstly, it is demonstrated that there exist statistically significant benefits to taking the volatility risk premium into account for the implied volatility for the purposes of univariate volatility forecasting. Secondly, high-frequency option implied measures are shown to lead to superior forecasts of the intraday stochastic component of intraday volatility and that these then lead on to superior forecasts of intraday total volatility. Finally, the use of realised and option implied measures of equicorrelation are shown to dominate measures based on daily returns.

Forecasting success on large projects : developing reliable scales to predict multiple perspectives by multiple stakeholders over multiple time frames

Our aim is to develop a set of leading performance indicators to enable managers of large projects to forecast during project execution how various stakeholders will perceive success months or even years into the operation of the output. Large projects have many stakeholders who have different objectives for the project, its output, and the business objectives they will deliver. The output of a large project may have a lifetime that lasts for years, or even decades, and ultimate impacts that go beyond its immediate operation. How different stakeholders perceive success can change with time, and so the project manager needs leading performance indicators that go beyond the traditional triple constraint to forecast how key stakeholders will perceive success months or even years later. In this article, we develop a model for project success that identifies how project stakeholders might perceive success in the months and years following a project. We identify success or failure factors that will facilitate or mitigate against achievement of those success criteria, and a set of potential leading performance indicators that forecast how stakeholders will perceive success during the life of the project's output. We conducted a scale development study with 152 managers of large projects and identified two project success factor scales and seven stakeholder satisfaction scales that can be used by project managers to predict stakeholder satisfaction on projects and so may be used by the managers of large projects for the basis of project control.

Semi-parametric forecasting of spikes in electricity prices

The occurrence of extreme movements in the spot price of electricity represents a significant source of risk to retailers. A range of approaches have been considered with respect to modelling electricity prices; these models, however, have relied on time-series approaches, which typically use restrictive decay schemes placing greater weight on more recent observations. This study develops an alternative, semi-parametric method for forecasting, which uses state-dependent weights derived from a kernel function. The forecasts that are obtained using this method are accurate and therefore potentially useful to electricity retailers in terms of risk management.

Selecting volatility forecasting models for portfolio allocation purposes

Techniques for evaluating and selecting multivariate volatility forecasts are not yet understood as well as their univariate counterparts. This paper considers the ability of different loss functions to discriminate between a set of competing forecasting models which are subsequently applied in a portfolio allocation context. It is found that a likelihood-based loss function outperforms its competitors, including those based on the given portfolio application. This result indicates that considering the particular application of forecasts is not necessarily the most effective basis on which to select models.

Identifying and forecasting the reverse salient in video game consoles: A performance gap ratio comparative analysis

This study uses the reverse salient methodology to contrast subsystems in video game consoles in order to discover, characterize, and forecast the most significant technology gap. We build on the current methodologies (Performance Gap and Time Gap) for measuring the magnitude of Reverse Salience, by showing the effectiveness of Performance Gap Ratio (PGR). The three subject subsystems in this analysis are the CPU Score, GPU core frequency, and video memory bandwidth. CPU Score is a metric developed for this project, which is the product of the core frequency, number of parallel cores, and instruction size. We measure the Performance Gap of each subsystem against concurrently available PC hardware on the market. Using PGR, we normalize the evolution of these technologies for comparative analysis. The results indicate that while CPU performance has historically been the Reverse Salient, video memory bandwidth has taken over as the quickest growing technology gap in the current generation. Finally, we create a technology forecasting model that shows how much the video RAM bandwidth gap will grow through 2019 should the current trend continue. This analysis can assist console developers in assigning resources to the next generation of platforms, which will ultimately result in longer hardware life cycles.

Forecasting outcomes in tennis matches using within-match betting markets

Klaassen and Magnus (2003) provide a model of the probability of a given player winning a tennis match, with the prediction updated on a point-by-point basis. This paper provides a point-by-point comparison of that model with the probability of a given player winning the match, as implied by betting odds. The predictions implied by the betting odds match the model predictions closely, with an extremely high correlation being found between the model and the betting market. The results for both men’s and women’s matches also suggest that there is a high level of efficiency in the betting market, demonstrating that betting markets are a good predictor of the outcomes of tennis matches. The significance of service breaks and service being held is anticipated up to four points prior to the end of the game. However, the tendency of players to lose more points than would be expected after conceding a break of service is not captured instantaneously in betting odds. In contrast, there is no evidence of a biased reaction to a player winning a game on service.

Understanding urban rail travel for improved patronage forecasting

Improved forecasting of urban rail patronage is essential for effective policy development and efficient planning for new rail infrastructure. Past modelling and forecasting of urban rail patronage has been based on legacy modelling approaches and often conducted at the general level of public transport demand, rather than being specific to urban rail. This project canvassed current Australian practice and international best practice to develop and estimate time series and cross-sectional models of rail patronage for Australian mainland state capital cities. This involved the implementation of a large online survey of rail riders and non-riders for each of the state capital cities, thereby resulting in a comprehensive database of respondent socio-economic profiles, travel experience, attitudes to rail and other modes of travel, together with stated preference responses to a wide range of urban travel scenarios. Estimation of the models provided a demonstration of their ability to provide information on the major influences on the urban rail travel decision. Rail fares, congestion and rail service supply all have a strong influence on rail patronage, while a number of less significant factors such as fuel price and access to a motor vehicle are also influential. Of note, too, is the relative homogeneity of rail user profiles across the state capitals. Rail users tended to have higher incomes and education levels. They are also younger and more likely to be in full-time employment than non-rail users. The project analysis reported here represents only a small proportion of what could be accomplished utilising the survey database. More comprehensive investigation was beyond the scope of the project and has been left for future work.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.