Assessing the validity of the Delphi technique

Forecasting is an essential factor in policy formulation and planning. It helps determine the direction of future actions. While a number of forecasting techniques are utilized to forecast the future, it is important to know just how valid those forecasting techniques can be. The Delphi technique has been called the ‘cornerstone of futures research’. This study reports the results of the assessment of the accuracy of the forecasts derived from the Delphi technique. Twenty-five experts in the communication field assessed 24 trends and 17 events in the state of Hawaii as of 1991. The expert assessments were then compared with the forecasts made by utilizing the Delphi technique 16 years earlier. The results showed that the trend forecasts were significantly correlated with the trend assessment. They also showed that the Delphi technique had accurately forecast approximately half the events that could be evaluated as of 1991. Results from this study lend support to the use of the Delphi technique in long-range forecasting and reveal some interesting findings in forecasting the developments in communication in Hawaii.     

Long-range forecasting within organisations

The article describes a method for testing and developing personal and organisational strategies. It suggests the establishment of a group which includes the organisation's partners and competitors (the author outlines why and how objections to the latter should be overcome) and which would progress through initial “unrealistic” games to more realistic scenarios. When combined with role playing, some “uncomfortable” scenarios can lead to members leaving the group and, where they pinpoint weaknesses, can either strengthen the organisation or hasten its decline. By including the organisations' decision makers, the exercise can lead to a rehearsal of the future.     

Predicting the money multiplier: Forecasts from component and aggregate models

This paper compares the relative abilities of two money multiplier forecasting procedures. The components method models and forecasts the multiplier's individual ratios. The second procedure is simply to model and forecast the aggregate multiplier itself. It has been suggested, though untested, that the components approach is more accurate in forecasting, because it accounts for certain ratio-specific changes that are masked in the aggregate model. The evidence presented here, based on forecasts of the MI multiplier for the period January 1980 through December 1982, indicates that the aggregate model forecasts the multiplier as well as the component procedure.     

Cross-impact analysis: Extended KSIM

The authors present an extension of Kane's cross-impact simulation model (ksim) that allows the inclusion of events and trends, and discuss the basic issues of forecasting and compatibility of forecasts.     

Robust outlier detection for Asia–Pacific stock index returns

Outliers can lead to model misspecifications, poor forecasts and invalid inferences. Their identification and correction is therefore an important objective of financial modeling.This paper introduces a simple method to detect outliers in a financial series. It uses an AR(1)–GARCH(1,1) model to calculate interval forecasts for one-step ahead returns that are then compared to realized returns to determine whether or not we are in the presence of an aberrant observation. The GARCH model, however, is only used as a filter and the identification algorithm remains robust to model misspecifications.The efficiency of this outlier-correction technique is first tested with a simulation study, before being applied to five Asian stock market returns to identify the outlying observations. After an analysis of these extreme fluctuations, the out-of-sample forecasting performance of our outlier-corrected model is then compared to the classical forecasts of a GARCH model in which no account is taken of outliers.     

Long-term metals forecasting: The track record: 1910–1964

In 90 forecasts published in journals, about 70% of the individual predictions proved correct; partly because they were fairly vague. However, two major reports dealing with long-term metals forecasting were much less accurate. Unexpectedly high rates of economic growth and technological change (since 1952) contributed to the Paley Commission's inaccuracy; whereas the failure of expected developments to occur (after 1964) upset the predictions of Resources in America's Future.     

An Analysis of Brokers' and Analysts' Unpublished Forecasts of UK Stock Returns

This paper describes an empirical study of over 4000 specific share return forecasts made by 35 UK stockbrokers and by the internal analysts of a large UK investment institution. A comparison of forecast and realised returns reveals a small but potentially useful degree of forecasting ability. A large part of the information content of the forecasts, however, appears to be discounted in the market place within the first month. Nevertheless, an analysis of some 3000 transactions motivated by, and executed at the time of, the forecasts shows that the apparent predictive ability of the recommendations could be translated into superior performance by the fund's investment managers. Differences in forecasting ability between brokers do not appear to persist over time, but predictive accuracy can be improved by pooling simultaneous forecasts from different sources.     

Financial analysts' forecasts of earnings: A better surrogate for market expectations

The specification of the market expectation of accounting numbers is a common feature of many empirical studies in accounting and finance. Givoly and Lakonishok (1979) found that financial analysts' forecasts have information content. This study evaluates the quality of analysts' forecasts as surrogates for the market expectation of earnings and compares it with that of prediction models commonly used in research. Results indicate that prediction errors of analysts are more closely associated with security price movements, suggesting that analysts' forecasts provide a better surrogate for market expectations than forecasts generated by time-series models. The study also identifies factors that might contribute to the performance of the financial analysts'forecasts. The broadness of the information set employed by analysts and, to a lesser extent, their reliance on information released after the end of the fiscal year appear to be important contributors to their performance.     

Analysts’ Earnings Forecasts for DAX100 Firms During the Stock Market Boom of the 1990s

This paper examines the reliability of financial analysts’ consensus earnings forecasts in the 1990s. Analysts are often accused of having fuelled the stock market boom with exaggerated evaluations of firms’ prospects. However, this criticism primarily refers to the analysts’ buy recommendations rather than earnings forecasts. Although biases in earnings forecasts have been reported since the 1980s, a systematic study capturing the period of ‘irrational exuberance’ until 2000 on the German stock market has not yet been published. Our data set consists of DAX100 firms, leaving out the peculiarities of forecasting earnings (or rather losses) of young technology firms. To evaluate the information content of analysts’ forecasts, we confront them with five alternative forecasting models. The empirical results reveal that analysts’ forecasts were too optimistic throughout the entire sample period. However, contrary to the increase in stock prices, the optimistic bias has declined over time. If the bias is removed, the analysts’ consensus forecasts significantly outperform all other models considered. Thus, the forecasts seem to be informative with respect to earnings differences, even if the market level of earnings is optimistically overstated.     

Connecting adaptation and strategy: The role of evolutionary theory in scenario planning

In this study, evolutionary theory is used to analyze and critique the strategic process of scenario planning. We argue that scenario planning can be strengthened as a theory- and practice-oriented process through the incorporation of evolutionary theory in the scenario narrative process, and in the subsequent implementation phases in response to environmental change. First, this paper addresses scenario planning in relation to theoretical perspectives on strategic planning and forecasting. Then, the concepts of variation, selection, retention, organizational learning and inertia are used to analyze scenario planning as a strategic process. This study argues that because scenario planning mirrors modes of variation and selection at the organizational level, evolutionary theory is a useful approach for assessing the plausibility of scenario narratives and strengthening the theoretical foundation of scenario planning as a process. By utilizing an evolutionary framework throughout the scenario planning process, this method has a better chance of encouraging exploratory strategic thinking without reinforcing non-blind variation or inertial practices. Concepts including inertia can also be used to better address bias and myopia in the scenario planning process. Additionally, evolutionary theory can be used to assess how entities learn from the outcomes of scenario planning as the environment changes over time.     

Financial analysts' forecasts of earnings: Their value to investors

This paper attempts to determine whether financial analysts' forecasts of earnings are useful to investors. This is accomplished by devising and evaluating the performance of trading rules under which transactions are triggered by revisions in earnings forecasts. The main finding is that an investor who acts upon the publicly available revisions of earnings forecasts can consistently outperform a buy-and-hold policy; in fact, such an investor could more than double his return. The results are inconsistent with the efficient market-hypothesis and indicate that the market reacts gradually rather than instantaneously to new information.     

The international petroleum exchange model: Reference results and validation

The author describes the IPE model and compares its main results with the data for 1970–1978. The model's results are close to the actual consumer-import demand for those years. The model's forecasts of the future demand for oil imports, over the next 20 years, are considerably lower than the forecasts produced by some other studies.     

Risk-based forecasting and planning and management earnings forecasts

This study examines the association between a firm’s internal information environment and the accuracy of its externally disclosed management earnings forecasts. Internally, firms use forecasts to plan for uncertain futures. The risk management literature argues that integrating risk-related information into forecasts and plans can improve a firm’s ability to forecast financial outcomes. We investigate whether this internal information manifests itself in the accuracy of external earnings guidance. Using detailed survey data and publicly disclosed management earnings forecasts from a sample of publicly traded U.S. companies, we find that more sophisticated risk-based forecasting and planning processes are associated with smaller earnings forecast errors and narrower forecast widths. These associations hold across a variety of different planning horizons (ranging from annual budgeting to long-term strategic planning), providing empirical support for the theoretical link between internal information quality and the quality of external disclosures.     

Determinants of analysts’ revenue forecast accuracy

This study investigates financial analysts’ revenue forecasts and identifies determinants of the forecasts’ accuracy. We find that revenue forecast accuracy is determined by forecast and analyst characteristics similar to those of earnings forecast accuracy—namely, forecast horizon, days elapsed since the last forecast, analysts’ forecasting experience, forecast frequency, forecast portfolio, reputation, earnings forecast issuance, forecast boldness, and analysts’ prior performance in forecasting revenues and earnings. We develop a model that predicts the usefulness of revenue forecasts. Thereby, our study helps to ex ante identify more accurate revenue forecasts. Furthermore, we find that analysts concern themselves with their revenue forecasting performance. Analysts with poor revenue forecasting performance are more likely to stop forecasting revenues than analysts with better performance. Their decision is reasonable because revenue forecast accuracy affects analysts’ career prospects in terms of being promoted or terminated. Our study helps investors and academic researchers to understand determinants of revenue forecasts. This understanding is also beneficial for evaluating earnings forecasts because revenue forecasts reveal whether changes in earnings forecasts are due to anticipated changes in revenues or expenses.     

Post-industrial Canada: An econometric modelling approach

This study brings together the predominantly qualitative approach of social forecasting—as manifested by Daniel Bell's concept of the post-industrial society—and the quantitative analysis of econometric forecasting. The econometric model used here is the candide model of the Canadian national economy. The author seeks to verify the compatibility of some hypotheses of the post-industrial concept by using candide to develop a post-industrial scenario; in doing so, he obtains quantitative estimates of many critical features of the concept within the framework of a structured and reasonably consistent model.     

Putting the New Keynesian DSGE Model to the Real‐Time Forecasting Test

The paper compares the quality of real‐time forecasts from a standard medium‐scale New Keynesian dynamic stochastic general equilibrium (DSGE) model to those from the Survey of Professional Forecasters (SPF) and DSGE‐VARs. It is shown that the DSGE model is relatively successful in forecasting the U.S. economy. This is especially true for forecasts conditional on SPF nowcasts, in which case the forecasting power of the DSGE turns out to be similar or better than that of the SPF for all the variables and horizons. An important weakness of the benchmark DSGE model is the poor absolute performance of its point forecasts and rather badly calibrated forecast densities.     

Processes and Accuracy of Cash Flow Forecasting: A Case Study of a Multinational Corporation

Despite its pivotal importance in enterprise management, cash flow forecasting gets little attention from academics perhaps because few of them have access to internal processes and data. In this article, however, the authors explain how cash flow forecasting is organized at Bayer, a large multinational company headquartered in Germany, and which factors influence the accuracy of its forecasts. The research focuses on cash flow forecasts based on the direct method, prepared three times a yearat Bayer, involving about 62,000 individual forecasting items each time. These forecasts form the basis of the company's liquidity and financial risk management, in particular, its foreign exchange risk hedging. The authors explain how local managers in Bayer's entities across the world derive the forecasts, i.e., what information they use as input, how they validate it, and how they deal with potential bias caused by managerial incentive systems. They also analyze whether forecasting processes are affected by characteristics such as business area, size, region, or specific local conditions, and ultimately whether forecasting practices and entity characteristics affect forecast accuracy. The findings show that cash flow forecasting procedures vary substantially across Bayer. While the central finance department gives general guidance on the required cash flow forecasting output and provides direction on the input to be used, there are no detailed instructions on how forecasts are to be prepared. Instead, local managers are free to determine their own forecasting practices. They use different forecasting inputs and validate forecasting inputs and output with different intensities, and they also differ in how they treat possible biases in input data. These findings document the limits of standardization and central control in large multinational corporations resulting from local managers’ need for flexibility to cope with the heterogeneity and dynamism of their environments. At the same time, however, local differentiation increases complexity and may increase errors. Quantitative analysis of forecasting errors shows that forecasts of receipts from customers (cash inflows) are more accurate than forecasts of payments to suppliers (cash outflows). Moreover, forecasting practices affect forecast accuracy. Outflow forecasts are more accurate if managers intensively validate forecasting input; inflow forecasts, if they eliminate input biases that may result from internal target setting or from other managerial incentives, and if they carefully validate their forecasting output. The study provides several insights.

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Predicting the money multiplier: A case study for the U.S. and the Netherlands

A Box-Jenkins model has been developed to predict the U.S. money multiplier. The forecasts are approximately 30% more accurate than those produced by the regression methods which Burger and others have used in recent work. Similar models are then applied to three different money multipliers for the Netherlands, taken from Korteweg's reformulation of the Brunner-Meltzer money supply scheme for this open economy. The results suggest that if the Dutch Central Bank invested more resources in the collection of data from the banks, then predictions could be made sufficiently accurate for use in the control of the money stock.