Reviewing the impact of the 2008/2009 global financial crisis (GFC) on international property markets and property professions

The global financial crisis that impacted on all world economies throughout 2008 and 2009. This impact has not been confined to the finance industries but has had a direct and indirect impact on the property industry worldwide from both an ownership and investment perspective. Property markets have experienced various levels of impact from this event, but universally the greatest impact has been on the traditional commercial and industrial property sectors from the investor perspective, with investment and superannuation funds reporting significant declines in the reported value of these investments. Despite the very direct impact of these declining property markets, the GFC has also had a very significant indirect impact on the various property professions and how these professions are now operating in this declining property market. Of particular interest is the comparison of the property market forecasts in late 2007 to the actual results in 2008/2009.

Were regulatory changes in reporting "abnormal items" justified? : evidence of intra-period classificatory earnings management practices in Australia

Purpose Managers generally have discretion in determining how components of earnings are presented in financial statements in distinguishing between ‘normal’ earnings and items classified as unusual, special, significant, exceptional or abnormal. Prior research has found that such intra-period classificatory choice is used as a form of earnings management. Prior to 2001, Australian accounting standards mandated that unusually large items of revenue and expense be classified as ‘abnormal items’ for financial reporting, but this classification was removed from accounting standards from 2001. This move by the regulators was partly in response to concerns that the abnormal classification was being used opportunistically to manage reported pre-abnormal earnings. This study extends the earnings management literature by examining the reporting of abnormal items for evidence of intra-period classificatory earnings management in the unique Australian setting. Design/methodology/approach This study investigates associations between reporting of abnormal items and incentives in the form of analyst following and the earnings benchmarks of analysts’ forecasts, earnings levels, and earnings changes, for a sample of Australian top-500 firms for the seven-year period from 1994 to 2000. Findings The findings suggest there are systematic differences between firms reporting abnormal items and those with no abnormal items. Results show evidence that, on average, firms shifted expense items from pre-abnormal earnings to bottom line net income through reclassification as abnormal losses. Originality/value These findings suggest that the standard setters were justified in removing the ‘abnormal’ classification from the accounting standard. However, it cannot be assumed that all firms acted opportunistically in the classification of items as abnormal. With the removal of the standardised classification of items outside normal operations as ‘abnormal’, firms lost the opportunity to use such disclosures as a signalling device, with the consequential effect of limiting the scope of effectively communicating information about the nature of items presented in financial reports.

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.

A method for systematically pooling data in very early stage construction price forecasting

Client owners usually need an estimate or forecast of their likely building costs in advance of detailed design in order to confirm the financial feasibility of their projects. Because of their timing in the project life cycle, these early stage forecasts are characterized by the minimal amount of information available concerning the new (target) project to the point that often only its size and type are known. One approach is to use the mean contract sum of a sample, or base group, of previous projects of a similar type and size to the project for which the estimate is needed. Bernoulli’s law of large numbers implies that this base group should be as large as possible. However, increasing the size of the base group inevitably involves including projects that are less and less similar to the target project. Deciding on the optimal number of base group projects is known as the homogeneity or pooling problem. A method of solving the homogeneity problem is described involving the use of closed form equations to compare three different sampling arrangements of previous projects for their simulated forecasting ability by a cross-validation method, where a series of targets are extracted, with replacement, from the groups and compared with the mean value of the projects in the base groups. The procedure is then demonstrated with 450 Hong Kong projects (with different project types: Residential, Commercial centre, Car parking, Social community centre, School, Office, Hotel, Industrial, University and Hospital) clustered into base groups according to their type and size.

The impact of litigation risk on corporate prospective disclosure : a review of the empirical literature

We review the literature on the impact of litigation risk (a form of external governance) on corporate prospective disclosure decisions as reflected in management earnings forecasts. From this analysis we identify four key areas for future research. First, litigation risk warrants more attention from researchers; currently it tends to be treated as a secondary factor impacting MEF decisions. Second, it would be informative from a governance perspective for researchers to explore why litigation risk has a differential impact on MEF decisions across countries. Third, understanding the interaction between litigation risk and forecast/firm-specific characteristics is important from management, investor and regulatory perspectives but is currently under-explored Last, research on the litigation risk and MEF attributes link is piecemeal and incomplete, requiring more integrated and expanded analysis.

Provision of customer knowledge to supply chains

Knowledge about customers is vital for supply chains in order to ensure customer satisfaction. In an ideal supply chain environment, supply chain partners are able to perform planning tasks collaboratively, because they share information. However, customers are not always able or willing to share information with their suppliers. End consumers, on the one hand, do not usually provide a retail company with demand information. On the other hand, industrial customers might consciously hide information. Wherever a supply chain is not provided with demand forecast information, it needs to derive these demand forecasts by other means. Customer Relationship Management provides a set of tools to overcome informational uncertainty. We show how CRM and SCM information can be integrated on the conceptual as well as technical levels in order to provide supply chain managers with relevant information.

Essays on volatility and liquidity in financial markets

The price formation of financial assets is a complex process. It extends beyond the standard economic paradigm of supply and demand to the understanding of the dynamic behavior of price variability, the price impact of information, and the implications of trading behavior of market participants on prices. In this thesis, I study aggregate market and individual assets volatility, liquidity dimensions, and causes of mispricing for US equities over a recent sample period. How volatility forecasts are modeled, what determines intradaily jumps and causes changes in intradaily volatility and what drives the premium of traded equity indexes? Are they induced, for example, by the information content of lagged volatility and return parameters or by macroeconomic news, changes in liquidity and volatility? Besides satisfying our intellectual curiosity, answers to these questions are of direct importance to investors developing trading strategies, policy makers evaluating macroeconomic policies and to arbitrageurs exploiting mispricing in exchange-traded funds. Results show that the leverage effect and lagged absolute returns improve forecasts of continuous components of daily realized volatility as well as jumps. Implied volatility does not subsume the information content of lagged returns in forecasting realized volatility and its components. The reported results are linked to the heterogeneous market hypothesis and demonstrate the validity of extending the hypothesis to returns. Depth shocks, signed order flow, the number of trades, and resiliency are the most important determinants of intradaily volatility. In contrast, spread shock and resiliency are predictive of signed intradaily jumps. There are fewer macroeconomic news announcement surprises that cause extreme price movements or jumps than those that elevate intradaily volatility. Finally, the premium of exchange-traded funds is significantly associated with momentum in net asset value and a number of liquidity parameters including the spread, traded volume, and illiquidity. The mispricing of industry exchange traded funds suggest that limits to arbitrage are driven by potential illiquidity.

Volatility timing : how best to forecast portfolio exposures

This paper investigates how best to forecast optimal portfolio weights in the context of a volatility timing strategy. It measures the economic value of a number of methods for forming optimal portfolios on the basis of realized volatility. These include the traditional econometric approach of forming portfolios from forecasts of the covariance matrix, and a novel method, where a time series of optimal portfolio weights are constructed from observed realized volatility and directly forecast. The approach proposed here of directly forecasting portfolio weights shows a great deal of merit. Resulting portfolios are of equivalent economic benefit to a number of competing approaches and are more stable across time. These findings have obvious implications for the manner in which volatility timing is undertaken in a portfolio allocation context.

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.

Visualization of predictive distributions for discrete spatial-temporal log Cox processes approximated with MCMC

An important aspect of decision support systems involves applying sophisticated and flexible statistical models to real datasets and communicating these results to decision makers in interpretable ways. An important class of problem is the modelling of incidence such as fire, disease etc. Models of incidence known as point processes or Cox processes are particularly challenging as they are ‘doubly stochastic’ i.e. obtaining the probability mass function of incidents requires two integrals to be evaluated. Existing approaches to the problem either use simple models that obtain predictions using plug-in point estimates and do not distinguish between Cox processes and density estimation but do use sophisticated 3D visualization for interpretation. Alternatively other work employs sophisticated non-parametric Bayesian Cox process models, but do not use visualization to render interpretable complex spatial temporal forecasts. The contribution here is to fill this gap by inferring predictive distributions of Gaussian-log Cox processes and rendering them using state of the art 3D visualization techniques. This requires performing inference on an approximation of the model on a discretized grid of large scale and adapting an existing spatial-diurnal kernel to the log Gaussian Cox process context.

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.

Polarization of forecast densities : a new approach to time series classification

Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.

Supply, demand, and equilibrium in the market for CRNAs

This study determined the current trends in supply, demand, and equilibrium (ie, the level of employment where supply equals demand) in the market for Certified Registered Nurse Anesthetists (CRNAs). It also forecasts future needs for CRNAs given different possible scenarios. The impact of the current availability of CRNAs, projected retirements, and changes in the demand for surgeries are considered in relation to CRNAs needed for the future. The study used data from many sources to estimate models associated with the supply and demand for CRNAs and the relationship to relevant community and policy characteristics such as per capita income of the community and managed care. These models were used to forecast changes in surgeries and in the supply of CRNAs in the future. The supply of CRNAs has increased in recent years, stimulated by shortages of CRNAs and subsequent increases in the number of CRNAs trained. However, the increases have not offset the number of retiring CRNAs to maintain a constant age in the CRNA population. The average age will continue to increase for CRNAs in the near future despite increases in CRNAs trained. The supply of CRNAs in relation to surgeries will increase in the near future.

Robust and reversible numerical set watermarking

Numeric sets can be used to store and distribute important information such as currency exchange rates and stock forecasts. It is useful to watermark such data for proving ownership in case of illegal distribution by someone. This paper analyzes the numerical set watermarking model presented by Sion et. al in “On watermarking numeric sets”, identifies it’s weaknesses, and proposes a novel scheme that overcomes these problems. One of the weaknesses of Sion’s watermarking scheme is the requirement to have a normally-distributed set, which is not true for many numeric sets such as forecast figures. Experiments indicate that the scheme is also susceptible to subset addition and secondary watermarking attacks. The watermarking model we propose can be used for numeric sets with arbitrary distribution. Theoretical analysis and experimental results show that the scheme is strongly resilient against sorting, subset selection, subset addition, distortion, and secondary watermarking attacks.