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Managerial auditors (MAs) are frequently relying on mistaken mixed costs (MC) forecasting assumptions. Two such assumptions are that MC are linear within the relevant range and variable costs vary proportionately with activity levels. Departure from these assumptions can lead to understating expenses, overstating profits, and even to fraud, litigation, and mounting professional liability risk. Develops a forecasting model of mixed cost error estimates, or error difference (ED). MAs can reduce the risk of litigation and professional liability by including such an ED in the internal control structure of a company. Uses Sensormatic Corporation and the electronics security industry as sample case because of the recent prominence of litigation and professional auditor liability verdicts. Focuses on the electronics security industry, but some of the findings may apply to other industries.

Presents a case study demonstrating financial statement ratio analysis (FSRA). This analysis matches company to industry data and builds sales forecasting models. FSRA imputes forecast standards of sales and costs, and applies them to a budgeted financial statement variance analysis for the EE (electronic and electrical) industry. Develops the concept of industry base standards, integrating them into the more traditional statistical and accounting concepts of quality control standards. Provides an implementation example, and reviews possible improvements to the current methodology and approach. Uses a similar methodology to forecast the stock market value with some exceptions. Models sales and costs of an individual company and an industry based largely on aggregate industry databases. For this purpose, uses a multivariate linear trend regression analysis for the sales forecasting model. Defines and tests related hypotheses and evaluates their significance and confidence levels. For an illustration uses the EE industry and the APM company. Also demonstrates a microcomputer‐based FSRA software that speeds, facilitates, and helps to accomplish the stated objectives. The FSRA software uses industry financial statement databases, computes financial ratios and builds forecasting models.

The purpose of this paper is to evaluate a number of promises typically made by owners of professional sports franchises in the USA that are also typically ignored or underevaluated by public bureaus and their elected principals using the Barclays Center in Brooklyn, New York as a case study. Ex post subsidy outcomes are evaluated against ex ante subsidy promises in order to draw lessons that can inform and improve subsidy debates elsewhere.

The case study adopts a pre-post strategy drawing on data from multiple sources over a period of up to ten years in order to triangulate the narrative and build credibility. The franchise owner’s ex ante promises and financial projections were obtained from various media including newspaper, video and interviews between December 2003, when the arena was publicly announced, and September 2012, when the arena opened. Data on ex post outputs were obtained from financial documents and government records covering periods from September 2011 through June 2016.

The franchise owner is found to have exaggerated the arena’s financial condition, under-delivered on its employment promises, and exaggerated the scope and timeliness of ancillary real estate development. Only promises of event frequency and attendance levels, measures of the public’s demand for the facility, have been met during the first three years.

Because the evaluation is a case study, causal conclusions cannot be drawn and some aspects of the Barclays Center context may not be applicable in other jurisdictions or subsidy debates. In addition, the case study does not evaluate an exhaustive list of the promises franchise owners make.

Franchise owners have a financial incentive to overpromise public benefits, since subsidy levels are tied to what the public is perceived to receive in return. This case study demonstrates that the public sector should not take owners’ promises and projections of public benefits at face value. Moreover, the case study reveals that the public sector should put more effort into ensuring ex post policy and data transparency in order to facilitate benefit-cost analyses of such subsidies.

The data required to evaluate promises, other than economic development ones, made by franchise owners are not systematically collected across state and local governments in the USA, making large-n studies impossible. Case studies are underutilized approaches in this area of public affairs, and this paper illustrates their usefulness. By focusing on a single facility, an evaluation of the franchise owner’s less acknowledged and arguably more important promises about the facility and its local impact is possible.

A senior analyst has recently completed an on-site visit to the Las Vegas properties of MGM Mirage. She must value the enterprise after her preparation of projected financial statements. Assumptions for these statements come from a combination of standard account relations delineated in the case and from specific company projections that must be gleaned from MGM's MD&A. This case introduces students to pro forma financial statements and their relevance to cash flow and earnings-based valuation. Tools relevant to spreadsheet modeling can also be introduced. The case precedes MGM's announced acquisition of Mandalay Bay Corporation in 2004.

The article is a description of the real-life experience based on the implementation of a financial forecasting model to inform budgeting and strategic planning. The organization is a charity-based health system that has hospitals and medical centers that provide care to the community. The health system performs a central budgeting process which is typically based on aggregation of individual budgets from the various hospitals and medical centers within the system. All financial data are reported to a central financial information system. Traditionally budgeting was done based on prior year's financial performance with a slight adjustment based on the hospital or medical center finance department's educated guess. This article describes the new forecasting method instituted to predict revenue and expenses, and to improve the budget planning process. Finally, the forecasts from the model are compared with real data to demonstrate accuracy of the financial forecasts. The model is since then being used in the budgeting process.

More frequent re‐forecasting is becoming an important topic on corporate agendas and is seen by many to be the only way to keep financial performance on track at a time when revenues are becoming less predictable. The paper aims to investigate this topic.

For the past four years ALG Software has commissioned a study of the re‐forecasting practices in a sample of the top organisations in the UK by revenue. The objective of the study is to benchmark how frequently the UK's leading organisations currently re‐forecast and what their goals are for the future.

The results show that the majority of organisations remain dissatisfied with the frequency with which they re‐forecast and wish to re‐forecast more frequently. However, the findings also show that many organisations feel that they cannot re‐forecast as often or as quickly as they would like. In fact, evidence suggests that little, if any, progress has been made during the last four years since this survey was first commissioned. This is due to either the amount of time it takes operational line managers to re‐forecast their resource requirements, or the amount of time it takes the finance function to complete a round of re‐forecasting. The type of application used for budgeting and re‐forecasting appears to make little difference to the time it takes organisations to produce an annual budget or complete a re‐forecast. Central to this issue is the use of non‐financial or “operational” data that predicts future resource requirements, and the limitations of the budgeting systems that organisations currently employ. Regardless of the type of application used for budgeting or re‐forecasting, much of this modelling is still done off‐line on spreadsheets.

The paper is of value to finance managers considering choosing a new budgeting application who will need to ensure that the type of operational modelling of non‐financial driver data, currently done offline on spreadsheets by line managers, can be seamlessly integrated into the central budgeting model.

The purpose of this study is to investigate and document current US hotel management company practices in budgeting and forecasting, and to recommend a process to improve accuracy and efficiency.

Key corporate financial executives of hotel management companies operating in the USA were surveyed. Different from prior studies that surveyed the US property‐level managers, or European hotel operators, the study surveyed the authors of budget guidelines of US hotel management companies with at least ten units or 1,000 rooms, to discover and document the budgeting and forecasting practices of multi‐unit hotel management companies. Chi‐square and t‐tests for equality of means were used to identify the differences between large and small hotel management companies.

Many concepts were identified that are not found in hospitality management textbooks. Current budgeting and forecasting methods used in the industry present opportunities for improving accuracy. There are also opportunities for time efficiencies, which may lead to improved participant satisfaction. Some significant differences were identified in budgeting and forecasting processes between large and small management companies.

The findings may not apply to independently owned and operated hotels, or small hotel management companies. Future research may focus on identifying economic factors that most influence hotel revenues at the local or regional level. Also, future research may focus on corporate computer software that facilitates intranet consolidation of property level budgets and forecasts and also allows spreadsheet flexibility for exploring various scenarios.

The practical application of the study is the recommendation for a centralized budget process that enhances accuracy, improves efficiency, and reduces “gamesmanship”.

There are four main contributions of the study: the obtaining of inputs from corporate officers of hotel management companies with operations in the USA; the documenting of forecasting and budgeting practices of hotel management companies operating in the USA; the recommending of a forecasting and budgeting process that may improve accuracy and participant satisfaction; and the identifying of differences between large and small companies in relation to forecasting and budgeting practices.

Two decades of research on municipal forecasting practice suggest that it is less advanced than other sectors. Moreover, local forecasters have a greater error tolerance than peers. Survey results of Florida’s finance directors provide evidence of why this is the case. Unlike other levels of government, local finance officials receive limited political or bureaucratic scrutiny that might induce more accurate forecasts. The judgmental approaches deployed facilitate the downside bias typically found in municipal forecast practice which fosters surplus building, per Wildavsky’s (1986) description of municipal budgeting. Absent greater senior management participation, it is unlikely municipal forecast practice will change. Findings also confirm that survey-based forecast research should account for respondents’ stated levels of accuracy and their “risk adjusted” perceptions that account for a preferred downside bias of one to seven percent.

To examine, determine, investigate and compare the extent to which enterprises in Greece and Cyprus use the theoretical framework to evaluate investments which are the discouraging factors concerning the use of this framework with particular emphasis to investigate the type, size and percentage of total capital expenditures detailed cash flow estimates; cash flow estimation practices and forecasting errors experience in the businesses in Greece and Cyprus.

The investigation was conducted by the distribution of a questionnaire to adequate number of small sized companies of Greece and Cyprus. The study was based on a questionnaire that was promoted to 800 firms in Greece and 120 in Cyprus.

The results of our survey have indicated the perceived important of some financial, marketing, and production factors on the cash flow forecasting process, as borrowing and repayment of funds, sales forecast, and operating expenses. The results of our study have indicated that the majority of forecasting methods used to generate cash flow estimates is management's subjective estimates and that the majority of the Greek firms adjust capital expenditure cash flow for inflation.

This study could potentially stimulate further research on cash flow forecasting practices of firms in other countries, such as Italy, France, and other Mediterranean countries, in order to compare the results.

This study provides evidence on the forecasting practices of small and medium-sized companies of Mediterranean companies.

The purpose of this paper is twofold: primary, to argue that the profits method, specifically a discounted cash flow (DCF)-based profits method, should be the preferred method of valuation when valuing specialised property. Secondary, to make technical recommendations in the application of the method.

Literature was reviewed on the theory of the profits method as well as physical valuations performed in practice. Improvements for the profits method are suggested from the review of six valuations conducted in South Africa in the specialised property sectors. A qualitative approach is followed in the research as broad principles are extracted from the valuation reports as implications and improvements for the profits method.

The profits method is more flexible and sophisticated than the cost approach in taking into account systematic and unsystematic risk. The profits method is more accurate than the cost approach in delivering a true reflection of the value of specialised property for any purpose but specifically for mortgage lending purposes and reduces the credit exposure risk of financial institutions. It also decreases pricing inefficiencies to be exploited by buyers and sellers.

Three improvements to the profits method are suggested. First, revenue could be forecasted based on a probability-weighted approach. Second, a modified capitalisation rate is suggested to the capitalisation rate formula in the calculation of G. Third, a market rental aggregation anchoring and judgement-based approach is suggested as rationale for determining the hypothetical rental split.

There seems to be a general lack in literature on the profits method of valuation and its application to specialised properties, specifically a DCF-based approach, with this paper being a technical contribution to the body of knowledge on this topic.