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Hospitals recently search for more accurate forecasting systems, given the unpredictable demand and the increasing occurrence of disruptive incidents (mass casualty incidents, pandemics and natural disasters). Besides, the incorporation of automatic inventory and replenishment systems – that hospitals are undertaking – requires developed and accurate forecasting systems. Researchers propose different artificial intelligence (AI)-based forecasting models to predict hospital assets consumption (AC) for everyday activity case and prove that AI-based models generally outperform many forecasting models in this framework. The purpose of this paper is to identify the appropriate AI-based forecasting model(s) for predicting hospital AC under disruptive incidents to improve hospitals' response to disasters/pandemics situations.

The authors select the appropriate AI-based forecasting models according to the deduced criteria from hospitals' framework analysis under disruptive incidents. Artificial neural network (ANN), recurrent neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and learning-FIS (FIS with learning algorithms) are generally compliant with the criteria among many AI-based forecasting methods. Therefore, the authors evaluate their accuracy to predict a university hospital AC under a burn mass casualty incident.

The ANFIS model is the most compliant with the extracted criteria (autonomous learning capability, fast response, real-time control and interpretability) and provides the best accuracy (the average accuracy is 98.46%) comparing to the other models.

This work contributes to developing accurate forecasting systems for hospitals under disruptive incidents to improve their response to disasters/pandemics situations.

Claims as property modelling and forecasting techniques have developed to take account of new investment theories, property researchers have tended to follow the approach of modern portfolio theory and, sometimes, the capital asset pricing model (CAPM). Argues that one of the reasons why property is often not included in actuarial property forecasting models for the purpose of asset allocation (which is a widespread perception in the property industry) is because actuaries have not made clear to property researchers the forms of their models, which are often quite different from those used in others parts of the finance literature. Explains how traditional investment theory can be adapted for actuarial use and how actuaries use forecasting models in asset allocation. Areas of property research which would assist actuaries develop better property forecasting models are identified.

This research examines the use of a number of time series model structures of a moderate allocation mutual fund, PRWCX. PRWCX was rated as the top fund in its category during the past five years. The fund invests at least 50% of its total assets that the fund manager believes that have above average potential for capital growth. The remaining assets are generally invested in convertible securities, corporate and government debt bank loans, and foreign securities. Forecasting the total NAV of such a moderate allocation mutual fund, composed of an extremely large number of investments, requires a method that produces accurate results. These models are exponentially smoothing (single, double, and Winter’s Method), trend models (linear, quadratic, and exponential) are Box-Jenkins models.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

This research examines the use of econometric models to predict the total net asset value (NAV) of an asset allocation mutual fund. In particular, the mutual fund case used is the Vanguard Wellington Fund (VWELX). This fund maintains a balance between relatively conservative stocks and bonds. The period of the study on which the prediction of the total NAV is based is the 24-month period of 2010 and 2011 and the forecasting period is the first three months of 2012. Forecasting the total NAV of a massive conservative allocation fund, composed of an extremely large number of investments, requires a method that produces accurate results. Achieving this accuracy has no necessary relationship to the complexity of the methods typically employed in many financial forecasting studies.

This paper aims to provide an investigation into whether financial analysts' forecast accuracy differs between the pre‐ and post‐adoption of the international financial reporting standards (IFRS) in the Asia‐Pacific region, namely, for the countries of Australia, Hong Kong and New Zealand. In particular, this study seeks to examine whether the treatment of intangibles capitalized in the post‐IFRS period have positively aided analysts in forecasting future earnings of a firm.

Panel data analysis is applied over a period from 2001 to 2008.

Evidence is found to show intangibles capitalized under the new recognition and measurement rules of IFRS are negatively associated with analysts' earnings forecast errors. The results are robust to several model specifications across each of the countries, suggesting that the adoption of IFRS may indeed provide more value‐relevant information in financial statements for the users of financial reports.

This paper analyzed whether the adoption of IFRS has led to any changes in the accuracy of earnings forecasts. The results will be of help to analysts' earnings forecast activity and those with interest in the subject.

Restates the importance of asset volatility forecasts for option pricing and portfolio management and outlines previous research on forecasting models. Discusses the relative information content and predictive power of implied and historical volatility and the existence of overreaction in option markets. Analyses 1989‐1997 daily exchange rate data for six currencies to examine this. Presents the results, which suggest that implied volatility has more information than volatility based on past prices; and is better than GARCH‐based or historic volatility forecasts for horizons up to three months; but can be a biased predictor of future realized volatility. Finds limited evidence that long term volatilities in option prices overreact to short term volatilities.

The term “Black Swan” has gained recent popularity to describe an extraordinary event that causes extensive damage. The combination of low predictability and major impact makes an upswing in the magnitude of Black Swan events an important factor when making property market forecasts. The difficulty is that the preferred property forecast models traditionally depend on input data constructed on a normal (bell curve) conditions. As outliers, Black Swan events appear not to form part of the property forecast process. The aim of this research is to examine Black Swan events.

This research examines Black Swan events and tests their impact on the accuracy of short term, six month bi‐annual survey forecasts from leading economists and property analysts for key Australian property market determinants: economic activity (GDP), ten year bonds and CBD office vacancy rates for the 2005‐2011 period.

A range of statistical tests showed inconsistencies with the expert forecasts to actual performance and importantly all the experts appeared to miss the start of the Global Financial Crisis in the December 2008 period. The variations in the margin of forecast error suggest that the experts examine the selected property determinants independently. Also, for economic activity and CBD office vacancy rates, there is an interesting trend that in several consecutive time periods, a simple naïve “no change” forecast was better than those of the experts.

In an environment of increasing occurrence of Black Swan events, this inability to forecast short term property market determinants with confidence highlights serious issues with the current approach to forecasting property market performance. The first step for research in this area is to understand and frame the property risks associated with Black Swan events.

The importance of reversibility criteria is discussed and existing measures described. A procedure for calculating a Bailout‐Portfolio‐Funding Index is set out and its advantages and limitations are analysed.

The purpose of this paper is to investigate whether analysts’ optimism affects the stock crash risk.

The sample covers 49,246 firm-year observations for the period between 1995 and 2015. The authors use OLS regressions with firm and year fixed effects for analyses.

The study finds that there is a positive association between analysts’ optimism and stock crash risk. Such a positive impact is more pronounced for firms with opaque information environment and for analysts who are considered ex ante credible.

The results indicate that analysts’ optimism can be an important source of stock crash risk.

The findings can be useful for informational users of analyst reports. Given that information provided by analysts might have negative consequences, the empirical results can be useful in assessing future stock return behaviors.

This paper has the potential to shed light on the large literature of crash risk. Prior studies suggest that crash is driven by the agency tension between shareholders and managers. It remains possible that crashes could be caused by overpriced stocks in the absence of bad news hoarding. The paper investigates crash from a perspective, financial analysts, that is underexplored.