House price bubble estimations in Australia's capital cities with market fundamentals

This paper investigates the existence of house price bubbles in Australia's eight capital cities in recent years by using quantitative analyses including Johansen cointegration test, Granger causality test, impulse response and Chow forecast test. While interactions between house prices and market fundamentals are discussed in long-run and causal estimations, shocks from the market fundamentals to house prices are investigated in generalized impulse response analyses. Findings from estimating house price bubbles for eight capital cities suggest that there was an obvious house price bubble in Perth, while a slight house price bubble occurred in Sydney. In contrast, house prices in Adelaide and Darwin can be explained very well by market fundamentals, while house prices in Melbourne, Brisbane, Hobart and Canberra were undervalued in the study period.

Ins and outs of discovery layers: EDS and Encore

Deakin University Library offers a number of search and discovery tools to its user communities: a web scale discovery product, a faceted display catalogue and a traditional catalogue. The presentation provides an overview of the challenges the Library has faced in its attempt to offer a seamless, comprehensive search and discovery service, that facilitates the finding of information resources. The information literacy and research skill levels of the University’s various cohort groups are considered, as well as the important role metadata plays in leading users to the resources they want.

Hybrid neural-evolutionary model for electricity price forecasting

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

An examination of Australian gold mining firms' exposure over the collapse of gold price in the late 1990s

Purpose – The purpose of this study is to examine the exposures of Australian gold mining firms in the highly volatile period from 1995 to 2000. This period has been characterized by significant changes in gold price due to bulk sale of gold by collective central banks. Specifically, the paper aims to investigate several firm-specific factors that are hypothesized to carry substantial influence on gold beta.

Design/methodology/approach – To estimate gold beta, we use the following multifactor model: R_g,_t = a+ß_gGPR_t + ß_xFXR_t + ß_mR_m,_t + _Et , where R_g,_t is the return on the gold stock Index at time t, GPR_t is the gold price return denominated in US dollar at time t, FXR_t is the foreign exchange return of Australian dollar in terms of US dollar at time t, R_m,t is the market return at time t, and _Et is the random error term at time t.

Findings – The paper finds that the values of gold beta are consistently greater than one, implying the sensitive nature of firms’ stock returns to gold price changes. This also suggests that investors holding gold mining stock would receive higher percentage increases in stock returns from a percentage increase in gold price returns, as opposed to investors holding gold bullion. Furthermore, these values have changed substantially over time with significant changes in gold price volatility. The most important and consistent relationship that we find is the impact of firms’ hedging behavior on their respective gold betas. This is consistent with Tufano’s study. It implies that firms, which hedge a greater proportion of their gold reserves, are less sensitive to movements in gold prices. The finding therefore supports the risk management theory that hedging increases shareholder’s wealth. However, cash operating costs, cash reserves and the level of gold production seem to influence very little on the firms’ exposure to gold price changes.

Originality/value – This study is of interest and important to the stock mining companies and investors because the extent of the effect of gold price movements on the stock returns of gold mining companies has significant impacts on returns for both firms and investors especially in their risk management and investment decisions, respectively.

Reliable graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated. In this chapter we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be. We also examined the performance difference of different discovery algorithms. This reveals the impact of discovery process. The experimental results show the superior reliability and robustness of MML method to standard significance tests in the recovery of graph links with small samples and weak links.

Subsequence frequency measurement and its impact on knowledge discovery in single sequences

Subsequence frequency measurement is a basic and essential problem in knowledge discovery in single sequences. Frequency based knowledge discovery in single sequences tends to be unreliable since different resulting sets may be obtained from a same sequence when different frequency metrics are adopted. In this chapter, we investigate subsequence frequency measurement and its impact on the reliability of knowledge discovery in single sequences. We analyse seven previous frequency metrics, identify their inherent inaccuracies, and explore their impacts on two kinds of knowledge discovered from single sequences, frequent episodes and episode rules. We further give three suggestions for frequency metrics and introduce a new frequency metric in order to improve the reliability. Empirical evaluation reveals the inaccuracies and verifies our findings.

Dean's great discovery : multiplication, division and fractions

Multiplication, division and fractions are 'hotspots' for students in the middle years with many students experiencing difficulty with these concepts (Siemon, Virgona & Cornielle, 2001). Arrays effectively model multiplication and help children develop multiplicative thinking and learn multiplication facts (Young-Loveridge, 2005). In this article we show how an open-ended array problem enabled a Grade 5/6 student to think about the relationship between multiplication, division and fractions. In the article we describe the project and 'hot spot' mathematical tasks that we used and provide some background on multiplicative thinking before presenting the case and a commentary (Western Melbourne Roundtable, 1997) of one student's exploration. This case was documented whilst we were working on a collaborative project with a team of upper primary teachers and a group of pre-service teachers at a local primary school.