Rolling Forecast Research Articles

How might hospitality organizations optimize their performance measurement systems?

PurposeThe purpose of the paper is to explore the use of “old” and “new” performance measurement (PM) techniques in a joint venture (JV) set up by two partners, a global hotel chain and a Portuguese company.Design/methodology/approachThe empirical data upon which this paper is based was collected through a prolonged contact with the specific organizational context. Qualitative or semi‐structured interviewing is the main source of information.FindingsThe paper finds that budgeting and budgetary control practices have been the cornerstone of the PM activity at the JV, where few “new” management accounting techniques are used. Yet, changes were made to the way the “old” PM techniques are drawn on and they have been supplemented with rolling forecasts prepared at the hotel level. The objectives are to encourage local managers to become more forward‐looking and to broaden the knowledge of the global partner about the future operating activities of the JV. Simultaneously, local adaptations of PM practices introduced by this partner are occurring.Research limitations/implicationsThe lack of interviews with people from headquarters and area management of the global hotel chain has to be seen as a limitation of the present study. To minimize the risk of bias, a triangulation of data was pursued by interviewing multiple people at the JV and at the Portuguese partner, and by discussing preliminary findings on several occasions.Originality/valueThe present study provides insights on how PM practices have evolved over time in a globalized organization, enriching our understanding of those practices in the context of hotel management.

Does the Adoption of Rolling Forecasts Improve Planning? An Empirical Investigation of the Consequences of Rolling Forecasts

This study investigates the consequences of adopting rolling forecasts. Using quarterly regional product-line forecasted and realized sales data from several product lines of a multinational biotechnology supplier, I find evidence of improved forecast accuracy after the adoption of rolling forecasts, particularly for more profitable product lines. However, accuracy deteriorated for product lines that operated in environments exhibiting higher demand uncertainty after rolling forecasts are put in place. Furthermore, subsequent to the use of rolling forecasts, forecast errors (defined as the scaled difference between actual and forecasted results) have become more negative, suggesting the mitigation of sandbagging behaviors in setting forecasts. However, these results are isolated only to forecasts for the immediately following quarter. Forecasts that are made for quarters further out appear sandbagged, and these forecasts are gradually walked upward over time, such that one-quarter-ahead forecasts tend to be overly optimistic.

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e-service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back-propagation neural network (BPNN)-based forex forecasting subsystem and Web-based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e-service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.

How Good are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach

This paper surveys existing factor forecast applications for output and inflation by means of a meta-analysis and assesses the determinants of the forecast performance of large factor models relative to other models. Our results suggest that factor models tend to outperform small models, whereas they perform slightly worse than alternative methods which are also able to exploit large datasets. The target variable (output/inflation and the country), the forecast horizon and the factor estimation technique matter. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre-selecting the variables included in the dataset did not improve factor forecasts in the past. Quarterly data and rolling forecasts seem to lend themselves better to factor forecasts than monthly data and recursive forecasts, respectively. Whether forecasters rely on a balanced or an unbalanced panel and how the forecasting equation is specified are found to be irrelevant.

How Good are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach

This paper surveys existing factor forecast applications for real economic activity and inflation by means of a meta-analysis and contributes to the current debate on the determinants of the forecast performance of large-scale dynamic factor models relative to other models. We find that, on average, factor forecasts are slightly better than other models' forecasts. In particular, factor models tend to outperform small-scale models, whereas they perform slightly worse than alternative methods which are also able to exploit large datasets. Our results further suggest that factor forecasts are better for US than for UK macroeconomic variables, and that they are better for US than for euro-area output; however, there are no significant differences between the relative factor forecast performance for US and euro-area inflation. There is also some evidence that factor models are better suited to predict output at shorter forecast horizons than at longer horizons. These findings all relate to the forecasting environment (which cannot be influenced by the forecasters). Among the variables capturing the forecasting design (which can, by contrast, be influenced by the forecasters), the size of the dataset from which factors are extracted seems to positively affect the relative factor forecast performance. There is some evidence that quarterly data lend themselves better to factor forecasts than monthly data. Rolling forecasts are preferable to recursive forecasts. The factor estimation technique seems to matter as well. Other potential determinants - namely whether forecasters rely on a balanced or an unbalanced panel, whether restrictions implied by the factor structure are imposed in the forecasting equation or not and whether an iterated or a direct multi-step forecast is made - are found to be rather irrelevant. Moreover, we find no evidence that pre-selecting the variables to be included in the panel from which factors are extracted helped to improve factor forecasts in the past.

Budget Type and Performance - The Moderating Effect of Uncertainty

Calls for improvements to traditional yearly corporate budgeting practices have a common thread, the failure of these practices to provide adequate plans in uncertain environments. Rolling budgets that require continuously updated forecasts have been suggested as a means to improve the traditional budget process. We hypothesise that when the environment is highly uncertain, learning effects from rolling forecasting result in superior performance. In the same environment, traditional budgeting results in poorer performance because there is no systematic method for exploring and understanding environmental uncertainty. Alternatively, rolling forecasting causes inferior performance in low uncertainty conditions because commitment to the budget goal is more important than the benefits of learning from forecasting.

Grain price volatility in a small open economy

This paper uses a multivariate autoregressive conditional heteroscedasticity model to investigate the effect of British grain prices on their Irish equivalents. We find that in the long run the law of one price holds and in the short run the model captures the salient features of Irish grain prices. The model is used to compute rolling forecasts of the conditional means, variances and covariance of Irish grain prices one year ahead. We find that this model produces superior forecasts compared with those based on a commonly used approach of an autoregressive conditional mean model where the second moments are estimated using a fixed‐weight moving average.

Is the annual budget really dead?

In recent years the traditional, annual budget has been accused of being incapable of meeting the demands of the competitive environment in the information age. Seeing that some of the most ardent critics are management consultants, with a vested interest in trying to persuade companies to change their management models, part of the criticism may perhaps be classified as hyperbole. Be this as it may, the criticism has resulted in more than just talk. Some real action can also be observed –; especially in Sweden, where several large companies have abandoned the traditional budget altogether. To date, academic researchers have shown very little interest in this phenomenon. The present study aims to investigate the validity of the criticism as perceived by chief financial officers and other high-ranking officers in large Finnish companies. The results of a postal survey indicate that relatively few companies are planning to abandon the annual budget completely. Even so, a considerable number of respondents who represent companies that intend to hold on to the annual budget indicate strong agreement with the main elements of the criticism. Comments made by several respondents also indicate that alternative, or rather, complementary, systems, such as rolling forecasts and monitoring systems similar to the Balanced Scorecard already exist, running parallel with the annual budget. Apparently, the latter still has a role to play as a means of maintaining internal effectiveness and communicating information to shareholders and other interested parties.

Managing Intellectual Capital: The Work with the Navigator in the Skandia Group

In 1994, the Skandia Group was one of the first companies noted for their work with their intellectual capital. The present article is a case study of how their work has developed since then. It is mainly based on interviews with business controllers at Skandia AFS and on written material produced by the Skandia Group. The study shows that great efforts are currently being made at Skandia to replace the budget process with rolling forecasts and the Skandia Navigator. To support the organisation in this process, the Dolphin Navigator System, which is an Intranet‐based support system, is being developed and implemented. Opportunities and risks with this type of computer‐based support systems are identified.

An empirical study of seasonal unit roots in forecasting

We assess the usefulness of pre-testing for seasonal roots, based on the HEGY approach, for out-of-sample forecasting. It is shown that if there are shifts in the deterministic seasonal components then the imposition of unit roots can partially robustify sequences of rolling forecasts, yielding improved forecast accuracy. The analysis is illustrated with two empirical examples where more accurate forecasts are obtained by imposing more roots than is warranted by HEGY. The issue of assessing forecast accuracy when predictions of any one of a number of linear transformations may be of interest is also addressed.

Exploration and Comparison of New Methods for Electric Demand Forecasting

A variety of time series forecasting methods were tested for the prediction of state-wide monthly residential electrical sales per customer, in a formal experimental design across ten states in the U.S. Models were fitted to historical data from 1962 through 1977, and evaluated for their performance over the following three-year period from January, 1978 through December, 1980. Rolling forecasts were generated on a monthly basis through the evaluation period to obtain 36 one-month forecasts, 35 two-month forecasts, etc. and were reduced to a set of summary statistics for different horizons and averaging methods. The forecasting models included several econometric techniques and several pure time series techniques. The conclusions were that there were gains in accuracy for structural models (use of exogenous variables), for dynamic models (involving lagged dependent variables), and for adaptive models. It was further concluded that the systematic use of model selection criteria such as the Akaike Information Criterion, and of diagnostic tests, led to improved accuracy in the accepted models.