Measuring expectations: forecast vs. ideal expectations. Does it really matter?

Consumer’s participation in service delivery is so central to cognition that it affects consumer’s quality evaluations. The study presented in this paper investigates the ways that visitor expectations change as a result of first hand experience with a service in the context of a major art exhibition. The research design allowed for two operational definitions of expectations, namely forecast and ideal expectations, in order to investigate differences between respondents’ pre and post experiences with a service. A total of 550 respondent visitors were interviewed during a major art exhibition, using two questionnaires delivered to two sub samples of respondents. The primary questionnaire was designed to capture recalled expectations after visitation while the parallel questionnaire captured forecast expectations prior to visitation and perceptions in the post experience phase. The findings suggest that forecast expectations were different to ideal expectations in both qualitative and quantitative ways and that these differences had important implications for perceptions of service quality. These differences can be explained, at least in part, by the way that expectations are formed and by the way that expectations are shaped by the actual visitation experience. For market researchers, the question of when and how to measure expectations has important implications for research design.

The forecast quality of CBOE implied volatility indexes

We examine the forecast quality of Chicago Board Options Exchange (CBOE) implied volatility indexes based on the Nasdaq 100 and Standard and Poor's 100 and 500 stock indexes. We find that the forecast quality of CBOE implied volatilities for the S&P 100 (VXO) and S&P 500 (VIX) has improved since 1995. Implied volatilities for the Nasdaq 100 (VXN) appear to provide even higher quality forecasts of future volatility. We further find that attenuation biases induced by the econometric problem of errors in variables appear to have largely disappeared from CBOE volatility index data since 1995.

Evaluation and comparison of type reduction algorithms from a forecast accuracy perspective

A variety of type reduction (TR) algorithms have been proposed for interval type-2 fuzzy logic systems (IT2 FLSs). The focus of existing literature is mainly on computational requirements of TR algorithm. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms from a forecasting performance perspective. Algorithms are judged based on the generalization power of IT2 FLS models developed using them. Four synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts accuracies. It is found that Coupland-Jonh TR algorithm leads to models with a better forecasting performance. However, there is no clear relationship between the width of the type reduced set and TR algorithm.

State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth

It has been well documented that the consensus forecast from surveys of professional forecasters shows a bias that varies over time. In this paper, we examine whether this bias may be due to forecasters having an asymmetric loss function. In contrast to previous research, we account for the time variation in the bias by making the loss function depend on the state of the economy. The asymmetry parameter in the loss function is specified to depend on set state variables which may cause forecaster to intentionally bias their forecasts. We consider both the Lin–Ex and asymmetric power loss functions. For the commonly used Lin–Ex and Lin–Lin loss functions, we show the model can be easily estimated by least squares. We apply our methodology to the consensus forecast of real U.S. GDP growth from the Survey of Professional Forecasters. We find that forecast uncertainty has an asymmetric effect on the asymmetry parameter in the loss function dependent upon whether the economy is in expansion or contraction. When the economy is in expansion, forecaster uncertainty is related to an overprediction in the median forecast of real GDP growth. In contrast, when the economy is in contraction, forecaster uncertainty is related to an underprediction in the median forecast of real GDP growth. Our results are robust to the particular loss function that is employed in the analysis.

Aggregation of Pi-based forecast to enhance prediction accuracy

In contrast to point forecast, prediction interval-based neural network offers itself as an effective tool to quantify the uncertainty and disturbances that associated with process data. However, single best neural network (NN) does not always guarantee to predict better quality of forecast for different data sets or a whole range of data set. Literature reported that ensemble of NNs using forecast combination produces stable and consistence forecast than single best NN. In this work, a NNs ensemble procedure is introduced to construct better quality of Pis. Weighted averaging forecasts combination mechanism is employed to combine the Pi-based forecast. As the key contribution of this paper, a new Pi-based cost function is proposed to optimize the individual weights for NN in combination process. An optimization algorithm, named simulated annealing (SA) is used to minimize the PI-based cost function. Finally, the proposed method is examined in two different case studies and compared the results with the individual best NNs and available simple averaging Pis aggregating method. Simulation results demonstrated that the proposed method improved the quality of Pis than individual best NNs and simple averaging ensemble method.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Improving load forecast accuracy by clustering consumers using smart meter data

Utility companies provide electricity to a large number of consumers. These companies need to have an accurate forecast of the next day electricity demand. Any forecast errors will result in either reliability issues or increased costs for the company. Because of the widespread roll-out of smart meters, a large amount of high resolution consumption data is now accessible which was not available in the past. This new data can be used to improve the load forecast and as a result increase the reliability and decrease the expenses of electricity providers. In this paper, a number of methods for improving load forecast using smart meter data are discussed. In these methods, consumers are first divided into a number of clusters. Then a neural network is trained for each cluster and forecasts of these networks are added together in order to form the prediction for the aggregated load. In this paper, it is demonstrated that clustering increases the forecast accuracy significantly. Criteria used for grouping consumers play an important role in this process. In this work, three different feature selection methods for clustering consumers are explained and the effect of feature extraction methods on forecast error is investigated.

Initial public offerings in Australia 1994 to 1999, recent evidence of underpricing and underperformance

This paper analyses whether financial and non financial characteristics of Australian initial public offerings (IPOs) can explain observed underpricing and long term underperformance over the period 1994 to 1999. A number of previous Australian studies have investigated initial day underpricing and longer term underperformance of IPOs and this study updates those papers. We find that initial day underpricing can in part be explained by market sentiment, forecast dividend per share yields, underwriter options and share options. Our longer term analysis supports the finding of previous studies in that IPOs on average, underperform the market in the first year following their listing.

Do you really want to ask an underwriter how much money you should leave on the table?

This paper analyses whether the owners of companies seeking to list will leave less money on the table if underwriters are employed to price and market the issue. Our findings indicate that limited liability and Industrial company initial public offerings (IPOs) that have used underwriters have left
more money on the table than those not employing underwriters. Not only is there a direct cost in employing an underwriter but this study suggests there might also be an indirect cost. We also find that a positive forecast earnings per share yield may be useful in reducing the amount of money left on the table.

Dividend forecasts and dividend payments of initial public offerings-when zero means zero and no comment most likely also means zero

Managers often try to forecast dividends because as Brown et al. (2002)  suggest, dividends have cash flow implications for investors and are important signalling devices. This study analyses the dividend forecasts in the prospectuses of initial public offerings (IPOs) in Australia over the period 1994 to 1999. While many companies forecast dividends, many make no dividend forecast at all and some forecast no (or zero) dividends for the forthcoming year. This paper seeks to determine if no forecast at all should present a different signal to investors than a zero dividend forecast. It is found that those that do not forecast a dividend, by and large, do not pay a dividend. It is also found that those that forecast a zero dividend, true to their forecast, pay no dividend.

The use of injury surveillance databases to identify emerging injury hazards

Among the many valuable uses of injury surveillance is the potential to alert health authorities and societies in general to emerging injury trends, facilitating earlier development of prevention measures. Other than road safety, to date, few attempts to forecast injury data have been made, although forecasts have been made of other public health issues. This may in part be due to the complex pattern of variance displayed by injury data. The profile of many injury types displays seasonality and diurnal variance, as well as stochastic variance. The authors undertook development of a simple model to forecast injury into the near term. In recognition of the large numbers of possible predictions, the variable nature of injury profiles and the diversity of dependent variables, it became apparent that manual forecasting was impractical. Therefore, it was decided to evaluate a commercially available forecasting software package for prediction accuracy against actual data for a set of predictions. Injury data for a 4-year period (1996 to 1999) were extracted from the Victorian Emergency Minimum Dataset and were used to develop forecasts for the year 2000, for which data was also held. The forecasts for 2000 were compared to the actual data for 2000 by independent t-tests, and the standard errors of the predictions were modelled by stepwise hierarchical multiple regression using the independent variables of the standard deviation, seasonality, mean monthly frequency and slope of the base data (R = 0.93, R² = 0.86, F_{(3, 27)} = 55.2, p < 0.0001). Significant contributions to the model included the SD (β = 1.60, p < 0.001), mean monthly frequency (β =  - 0.72, p < 0.002), and the seasonality of the data (β = 0.16, p < 0.02). It was concluded that injury data could be reliably forecast and that commercial software was adequate for the task. Variance in the data was found to be the most important determinant of prediction accuracy. Importantly, automated forecasting may provide a vehicle for identifying emerging trends.

The pricing of property trust IPOs in Australia

Following Brounen and Eichholtz (2002) this paper adds to the international literature investigating the underpricing of REIT initial public offerings (IPOs), with a study into Australian property trusts. This study finds that initial day returns can in part be explained by forecast profit distributions (or dividends) and the market sentiment towards property trusts from the date of the prospectus to the date of listing. There is some support for the “winners curse” explanation of underpricing with evidence that large investor or institutional involvement at the outset of the IPO also has some explanatory power.

Contemporaneous intra-day volume, option, and futures volatility transmissions across parallel markets

The primary objective of this article is to investigate volatility transmission across three parallel markets operating on the Sydney Futures Exchange (SFE), both within and out of sample. Half-hourly observations are sampled from transaction data for the share price index (SPI) futures, SPI futures options, and 90-day bank accepted bill (BAB) futures markets, and the analysis is carried out using the simultaneous volatility (SVL) system of equations as well as competing volatility models. The results confirm the poor ability of GARCH models to fit intraday data. This study also applies an artificial nesting procedure to evaluate the out-of-sample volatility forecasts. Implied volatility has very limited (if any) predictive power when evaluated in isolation, whereas the SVL model with implied volatility embedded provides incremental information relative to competing model forecasts.