Annual Forecasts Research Articles

The Second EU Economic Crisis in 2013-14

The objective of this paper is to provide a short-term forecast for the economic crisis in the EU. Most economic forecasts are forecasts for economic growth; forecasts for economic crisis are not common in the field. By using the Carrying Capacity Function from population studies as the basis for the predictive function, the point at which the economy may fall into economic crisis may be predicted. The Laplace Rule of Succession to generate the Bayesian prior. John F. Muth’s equation for endogenous shock was also used to calculate economic shock for the EU in order to obtain a comparative study. This paper fills that gap. The data used in this paper is derived from the World Economic Outlook Report of the IMF FY 2012. From this data base, two cases are used for the study, namely the EU and the ASEAN. T the end, there are three forecast numbers: IMF annual forecast, forecast under Muth’s endogenous shock, and the forecast number obtained through calculation method of this paper. Among these three forecast, to forecast points to economic crisis in 2013. The probability for this likely crisis was calculated. Under the Laplace Rule of Success and the Binomial Probability Distribution Density, the prospect of each forecast was calculated. The IMF’s forecast for a growth of 0.929 in 2013 has 21% prospect; forecast under Muth’s endogenous shock equation yields 22% prospect; and the forecast by the carrying Capacity Function yields 67% prospect. Under Rosenthal’s Determinants of Inferential Error grid, it was confirmed that holding the significance level at, neither Type I nor Type II error was evident (Rosenthal, 1984). The Effect Size was moderate showing (Cohen, 1988, and Friedman, 1968).

Atlantic hurricane forecast: a statistical analysis

Purpose – A reliable forecast of hurricane activity in the Atlantic Basin has the potential to help mitigate the economic losses caused by hurricanes. One of the difficult problems is to make reasonable annual forecast of catastrophe losses based on the short record of historical observations. Atmospheric conditions tend to influence tropical cyclone development. Considering the complex interactions among climatological factors, prediction of future hurricane activity is challenging. In this study, the authors are attempting to predict the number of Atlantic hurricanes for a given year based on two different approaches.Design/methodology/approach – In part I, an autoregressive integrated moving average (ARIMA) is used to model a long‐run behavior of Atlantic hurricane frequency. The authors present a comparison of CSU's forecast with ARIMA model. Part II focuses on the relationship between the climate signals and hurricane activity and introduces a new approach in including climate indices into the predic...

A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm

Accurate annual power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power industry. The annual power load forecasting is a non-linear problem because the load curve shows a non-linear characteristic. Generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is very regretfully finds that the GRNN have rarely been applied to the annual power load forecasting. Therefore, the GRNN was used for annual power load forecasting in this paper. However, how to determine the appropriate spread parameter in using the GRNN for power load forecasting is a key point. In this paper, a hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasting model. The effectiveness of this proposed hybrid model was proved by two experiment simulations, which both show that the proposed hybrid model outperforms the GRNN model with default parameter, GRNN model with particle swarm optimization (PSOGRNN), least squares support vector machine with simulated annealing algorithm (SALSSVM), and the ordinary least squares linear regression (OLS_LR) forecasting models in the annual power load forecasting.

Analysts' Motives for Rounding EPS Forecasts

ABSTRACTWe investigate analysts' motives for rounding annual EPS forecasts (placing a zero or five in the penny location of the forecast). We first show that an intuitive reason for analysts to engage in rounding is in circumstances where the penny digit of the forecast is of less economic significance. By rounding, analysts reveal that their forecasts are not intended to be precise to the penny. We also show that analyst incentives impact the likelihood of rounding. Specifically, we predict that analysts will exert less effort forecasting earnings for firms that generate less brokerage or investment banking business since such firms create less value for the analysts' employers. As a consequence of this reduced effort and attention, the analyst will be more uncertain about the penny digit of the forecast and so will round. Our results are consistent with this prediction. One implication of our findings is that a rounded forecast is a simple and easily observable proxy for a more noisy measure of the market's expectation of earnings. Consistent with this implication, we show that rounded forecasts bias down earnings response coefficients at earnings announcements.

Analysts’ Motives for Rounding EPS Forecasts

We investigate analysts’ motives for rounding annual EPS forecasts (placing a zero or five in the penny location of the forecast). We first show that an intuitive reason for analysts to engage in rounding is in circumstances where the penny location of the forecast is of less economic significance. By rounding, analysts reveal that their forecasts are not intended to be precise to the penny. We also show that analyst incentives impact the likelihood of rounding. Specifically, we predict that analysts will exert less effort forecasting earnings for firms that generate less brokerage or investment banking business since such firms create less value for the analysts’ employers. As a consequence of this lack of effort and attention, the analyst will be more uncertain about the penny digit of the forecast and so will round. Our results are consistent with this prediction. One implication of our findings is that a rounded forecast is a simple and easily observable proxy for a more noisy measure of the market’s expectation of earnings. Consistent with this implication, we show that rounded forecasts bias down earnings response coefficients at earnings announcements.

Forecasting the Onset of Genocide and Politicide: Annual Out-of-Sample Forecasts on a Global Dataset, 1988-2003

We present what is, to the best of our knowledge, the first published set of out-of-sample forecasts of genocide and politicide based on a global dataset. Our goal is to produce a prototype for a real-time model capable of forecasting one year into the future. While building on the current literature, we take several important steps towards better forecasting. We implement a two-stage modelling approach that considers both the likelihood of instability and the likelihood of genocide in a single estimate. Our sample is restricted only by the available data, rather than through selection of controlled cases, since such an approach would not be available for forecasting into the future. We explore factors exhibiting variance over time to improve year-on-year forecasting performance. And we produce annual lists of at-risk states in a format that should be of use to policy makers seeking to prevent such mass atrocities. Overall our out-of-sample forecasts for 1988-2003 predict 81.8% of genocide onsets correctly while also predicting 78.7% of non-onset years correctly, an improvement over a previous study using a case-control in-sample approach. We produce 16 annual forecasts based only on previous years’ data, which identify seven of eleven cases of genocide/politicide onset within the handful of the top 5% of at-risk countries per year. This represents a considerable step toward useful real-time forecasting of such rare events. We conclude by suggesting ways to further enhance predictive performance.

A re-examination of analysts’ superiority over time-series forecasts of annual earnings

We re-examine the widely held belief that analysts’ earnings per share (EPS) forecasts are superior to random walk (RW) time-series forecasts. We investigate whether analysts’ annual EPS forecasts are superior, and if so, under what conditions. Simple RW EPS forecasts are more accurate than analysts’ forecasts over longer horizons, for smaller or younger firms, and when analysts forecast negative or large changes in EPS. We also compare the accuracy of a third forecast of longer-term earnings based on a naive extrapolation of analysts’ 1-year-ahead forecasts. Surprisingly, this naive extrapolation provides the most accurate estimate of long-term (2- and 3-year-ahead) earnings. These findings recharacterize prior generalizations about the superiority of analysts’ forecasts and suggest that they are incomplete, misleading, or both. Moreover, in certain settings, researchers can rely on forecasts other than these explicit forecasts.

Accuracy Verification of Irradiance Forecast using Numerical Weather Prediction Data by Japan Meteorological Agency

In order to integrate a substantial amount of fluctuating solar energy into the power grid, accurate irradiance prediction systems are required to estimate the power output of solar power plants. This study presents a simple statistical approach to predict irradiance on the basis of regional numerical weather prediction data that is forecasted up to three days by the Japan Meteorological Agency. Annual irradiance forecasts of individual sites in the Hokuriku region demonstrate a root mean square error of 0.43-0.45MJ/m2 for the intra-day forecast, increasing to about 0.51MJ/m2 for the two-days-ahead forecast. Furthermore, we calculated error indexes under different conditions to estimate the magnitude of the influence of different assumptions on the results.

An assessment of electrical load forecasting using artificial neural network

The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using artificial neural networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. For annual forecasts, there should be 10 to 12 years of historical monthly data available for each electrical system or electrical buss. A case study is performed by using the proposed method of peak load data of a state electricity board of India which maintain high quality, reliable, historical data providing the best possible results. Model's quality is directly dependent upon data integrity.

Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks

Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259 km2, by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5 mm precipitation since the first of autumn (day), t47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2–6–6–10–1 and Levenberg–Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.

Security Analysts, Cash Flow Forecasts, and Turnover

We examine the relation between security analyst turnover and the relative accuracy of their annual earnings and cash flow forecasts. Controlling for self-selection in an analyst’s decision to issue a cash flow forecast, we find that relatively more accurate earnings and cash flow forecasts reduce the probability of turnover. Relative earnings forecast accuracy decreases the probability of turnover more than relative cash flow forecast accuracy. We conduct two cross-sectional tests. We find that relative cash flow forecast accuracy is more important in the analyst’s career outcome when cash flow forecasts are potentially more useful to investors. We find that relative cash flow forecast accuracy is more heavily weighted in the career outcome when the number of other analysts providing cash flow forecasts for the firm is larger. This finding is consistent with economic intuition that relative performance evaluation is more effective when larger groups of individuals are compared.

Security analysts, cash flow forecasts, and turnover

Abstract We examine the relationship between security analyst turnover and the relative accuracy of their annual earnings and cash flow forecasts. Controlling for self-selection in an analyst’s decision to issue a cash flow forecast, we find that relatively more accurate earnings and cash flow forecasts reduce the probability of turnover. Relative earnings forecast accuracy decreases the probability of turnover more than relative cash flow forecast accuracy. We conduct two cross-sectional tests. We find that relative cash flow forecast accuracy is more important in the analyst’s career outcome when cash flow forecasts are potentially more useful to investors. We find that relative cash flow forecast accuracy is more heavily weighted in the career outcome when the number of other analysts providing cash flow forecasts for the firm is larger. This finding is consistent with economic intuition that relative performance evaluation is more effective when larger groups of individuals are compared.

How do auditors view managers’ voluntary disclosure strategy? The effect of earnings guidance on audit fees

The objective of this study is to examine the relation between attributes of earnings forecasts issued by managers and audit fees. Although there is an extensive literature on managers’ disclosure of earnings forecasts, there is a paucity of research on how auditors incorporate information from these voluntary disclosures. We find that the issuance of an annual or quarterly management earnings forecast in the prior period is positively associated with the current period audit fees. Our results indicate that on average, audit fees are higher by about 7% for firm-years associated with an annual forecast. Among the firms that issue earnings forecasts, we find no association between audit fees and likelihood of updating a previously issued earnings forecast, indicating that auditors do not view such behavior negatively. Further, we find audit fees to be positively associated with the error and the bias (or optimism) in the forecasts for annual forecasts but not for quarterly forecasts. Overall, these results suggest that management’s forecast behavior captures higher business risk for the auditor via greater risk of earnings management or litigation risk.

Fuzzy Stochastic Programming Method: Capacitor Planning in Distribution Systems With Wind Generators

Capacitor planning in a distribution system (DS) must account for forecasted load and planned wind generators (WGs). In some DSs with large penetration, WGs support a significant part of the total load on an average and the power flow reverses to the substation during high-wind-low-load conditions. With annual forecasts of peak loads and generations being probabilistic in nature with differing probability distribution functions, their proper representation in a capacitor planning exercise is imperative. In this work, using probabilistic models of load and wind generation, we propose a stochastic capacitor planning formulation for DSs. The proposed formulation minimizes the total cost of newly located and sized capacitors and the annual energy loss in a DS while considering limits on load bus voltages. The starting state of the optimization process has forecasted loads, planned WGs, and inadequate capacitors that results in lower voltage limit violations. In order to handle this infeasible state in a stochastic optimization formulation, fuzzy models are used to represent load bus voltage constraints. The proposed fuzzy stochastic programming method that combines stochastic programming and fuzzy optimization procedure is solved using a robust mixed integer linear programming solver. Results on a 70-bus system are reported, compared, and discussed.

Annual Runoff Forecasting Based on Wavelet De-Noising SPA Model

The annual runoff forecasting of set pair analysis (SPA) based on SPA principle that describe error distribution of prediction model from identical, discrepancy and contrary, and to describe the precision by correlation degree, Therefore the prediction model was built. According to the nonlinear and the multi-time scale character of hydrological sequence, the establishment of hydrological forecast model is not high precision, Here we apply wavelet de-noising characteristics, and use 1959-1989 years of data of Fenhe reservoir baxia station to establish wavelet de-nosing SPA model. Through contrast between the single prediction model and the synthetic prediction model of hydrological forecasting and measured series. The results show that the synthetic prediction model is superior to the single prediction model.

EMD-KNN Model for Annual Average Rainfall Forecasting

AbstractThe prediction of rainfall is the premise of responding to water resources management and flood defense. The hydrological system is complex, and rainfall time series as a member of it has characteristics of being nonlinear and nonstationary, so accurate rainfall prediction still a much difficult job at present. The paper proposes a conjunction model named EMD-KNN for forecasting annual average rainfall. The model is improved by combining two methods, empirical mode decomposition (EMD) and the K-nearest neighbor bootstrap regressive model (K-NN). It is applied to case studies of forecasting annual average rainfall for Nanjing city and the Dahuofang reservoir basin. Where Nanjing city is water-rich area in East China, Dahuofang reservoir basin is water-deficient area in Northeast China. Three performance evaluation measures results revealed that the EMD-KNN model reduces the prediction mean absolute error (MAE), mean relative error (MRA), and root-mean square error (RMSE) with respect to the single ...

Moral Hazard, Firms’ Internal Governance and Management Earnings Forecasts

This paper investigates the role of management earnings forecasts in mitigating information asymmetry between investors and managers relating to moral hazard, and explains how earnings guidance facilitates monitoring. I demonstrate that firms that are more susceptible to moral hazard problems and more difficult to monitor are also more likely to issue annual earnings forecasts and they do so more frequently. In addition, I examine how firm internal governance drives forecasting decisions and show that stronger board governance and managerial equity incentives are associated with higher likelihood and frequency of forecast issuance. Finally, I provide robust evidence that managerial equity incentives are associated with more informative and higher quality guidance. In particular, I find that these forecasts are more accurate, unbiased, more specific and timely, consistent with equity incentives aligning shareholders’ and managers’ interests regarding disclosure decisions. However, I find mixed evidence on the association between board governance and forecast quality.

Novo modelo de reconciliação para a indústria do ouro

A reconciliação pode ser dividida em etapas, cada etapa representando o desempenho de uma das seguintes operações mineiras: estimativa de longo-prazo, estimativa de curto-prazo, planejamento, lavra e beneficiamento. Na indústria do ouro, ainda existe a etapa do budget ou orçamento, quando a empresa informa ao mercado sua previsão de produção anual. A divisão da reconciliação em etapas permite, não só aumentar a confiabilidade na informação do orçamento anual das empresas de mineração, mas, também, detectar e corrigir as etapas críticas responsáveis pelo erro global de estimativa, otimizando métodos e equipamentos de amostragem. O presente trabalho objetiva desenvolver e validar um novo modelo de reconciliação para a indústria do ouro. Esse modelo fundamenta-se em práticas corretas de amostragem e na subdivisão da reconciliação em etapas, visando a uma melhor estimativa de teores e a um controle mais eficiente dos processos de mineração, desde a estimativa de recursos até a produção final.

Factors Associated with the Year-End Decline in Working Capital

Working capital is an important indicator of firm operational efficiency. All else being equal, lower levels signal greater efficiency. Managers are thus likely to be motivated to report lower levels of working capital at times of greater external attention. We find that working capital levels decrease in the fourth fiscal quarter significantly more than expected, conditional on seasonal changes in economic activity. The decrease subsequently reverses in the following first fiscal quarter. Evidence indicates that firms manage down year-end working capital through transactions that increase year-end operating cash flow and that firms spread this activity over all working capital accounts. Finally, the temporary decrease in year-end working capital is correlated with compensation benchmarks and analysts’ annual cash flow forecasts. The temporary drop is also more pronounced for firms with industry dominance. This paper was accepted by Mary Barth, accounting.

Are Experienced Analysts More Accurate?

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">We examine the relationship between analyst experience and the accuracy of annual earnings forecasts using a 20-year sample (1983-2002) from the Thomson Financial First Call I/B/E/S database.<span style="mso-spacerun: yes;">  </span>We test for this relationship using three different measures of forecast accuracy employed by prior researchers, which are regressed against measures of general experience and specific experience, along with five other controls, for four independent 5-year subperiods, as well as for the full 20-year period.<span style="mso-spacerun: yes;">  </span>We find that general experience levels are positively associated with forecast accuracy (negatively associated with forecast error) in most subperiods for two of the three measures of forecast accuracy.<span style="mso-spacerun: yes;">  </span>We also find, in contrast with the extant literature, that for two of the three measures of forecast accuracy and for most subperiods, specific experience does not have an association with forecast accuracy beyond that provided by the general experience measure. <span style="mso-spacerun: yes;"> </span>Our results suggest that the relationship between forecast accuracy and analyst experience (as well as some other commonly examined analyst characteristics) is dependent on the measure of accuracy employed and the time period studied. </span></span></p>