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.

Analysis of IPO dividend forecasts for Australian LPTs

While dividend forecasts in the prospectuses of initial public offerings (IPOs) are common, Brown et al. (2000) have found them to be optimistically biased. This study investigates the dividend/distribution forecasts in the prospectuses of Australian LPT IPOs during the period 1994 to 2004 and finds on average that they are not optimistically biased. Because dividends have important cash flow implications for investors, this study also examines factors that might influence the magnitude of the errors between the forecast and the actual distributions. It finds that LPT IPOs that offer stapled securities have overestimated their distribution paying ability.

Improving load forecasting accuracy through combination of best forecasts

Neural network (NN) models have been widely used in the literature for short-term load forecasting. Their popularity is mainly due to their excellent learning and approximation capability. However, their forecasting performance significantly depends on several factors including initializing parameters, training algorithm, and NN structure. To minimize negative effects of these factors, this paper proposes a practically simple, yet effective and an efficient method to combine forecasts generated by NN models. The proposed method includes three main phases: (i) training NNs with different structures, (ii) selecting best NN models based on their forecasting performance for a validation set, and (iii) combination of forecasts for selected best NNs. Forecast combination is performed through calculating the mean of forecasts generated by best NN models. The performance of the proposed method is examined using real world data set. Comparative studies demonstrate that the accuracy of combined forecasts is significantly superior to those obtained from individual NN models.

Exploring the moderating role of growth options on the relation between board characteristics and management earnings forecasts

Manuscript Type
Empirical
Research Question/Issue
This study examines whether director independence, reputation, and financial expertise are related to management earnings forecast (MEF) activity. In particular, we examine whether such a relationship is moderated by firms’ growth options.
Research Findings/Insights
Using Australian archival data for 1,928 firm-years between 1999 and 2006, we find several board characteristics have a significant positive relationship with: (1) the likelihood of firms issuing MEFs; (2) their specificity; (3) their accuracy; and (4) a negative relationship with their bias. For (1), (2), and (3) we show that these relationships are accentuated for firms with high growth options.
Theoretical/Academic Implications
While the theory of voluntary disclosure suggests firms will disclose information that is favorable to them or their managers, well-governed firms issue informative MEFs that potentially reduce information asymmetries in capital markets. We extend the prior literature by showing that such a relation is enhanced in the presence of information asymmetry and moral hazard associated with growth options.
Practitioner/Policy Implications
Our results have strategic implications for nomination committees by showing that independent directors and those with strong reputations and financial expertise enhance the governance of high growth firms. We also inform the regulatory debate by showing that good corporate governance enhancing disclosure quality is context-specific – it is not a case of “one size fits all”.

Prediction intervals for short-term wind farm power generation forecasts

Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.

Bayesian model averaging of load demand forecasts from neural network models

Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations.

An optimized mean variance estimation method for uncertainty quantification of wind power forecasts

A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

Gender-diverse boards and properties of analyst earnings forecasts

Using a sample of 2,200 U.S. listed firm-year observations (2001-2007), this study shows a positive (negative) relation between gender diversity on corporate boards and analysts' earnings forecast accuracy (dispersion), after controlling for earnings quality, corporate governance, audit quality, stock price informativeness, and potential endogeneity. Our findings are important as they suggest that board diversity adds to the transparency and accuracy of financial reports such that earnings expectations are likely to be more accurate for these firms.