Forecast quality improvement with Action Research: A success story at PharmaCo

There is a gap in the forecasting research surrounding the theory of integrating and improving forecasting in practice. The number of academically affiliated consultancies and knowledge transfer projects that there are around, due to a need for improvements in forecast quality, would suggest that many interventions and actions are taking place. However, the problems that surround practitioner understanding, learning and usage are rarely documented. This article takes the first step toward trying to rectify this situation by using the specific case study of a fully engaged company. A successful action research intervention in the Production Planning and Control work unit improved the use and understanding of the forecast function, contributing to substantial savings, enhanced communication and improved working practices.     

Evaluating FOMC forecasts

Monetary policy outcomes have improved since the early 1980s. One factor contributing to the improvement is that Federal Reserve policymakers began reporting economic forecasts to Congress in 1979. These forecasts indicate what the Federal Open Market Committee (FOMC) members think will be the likely consequence of their policies. We evaluate the accuracy of the FOMC forecasts relative to private sector forecasts, the forecasts of the Research Staff at the Board of Governors, and a naïve alternative. We find that the FOMC output forecasts were better than the naïve model and at least as good as those of the private sector and the Fed staff. The FOMC inflation forecasts were more accurate than the private sector forecasts and the naïve model; for the period ending in 1996, however, they were not as accurate as Fed staff inflation forecasts.     

Terrorism activities and long-term annual management forecasts

We study the effects of terrorist attacks on firms’ long-term annual management earnings forecasts bias. We find that the managers of firms located closer to the epicenters of attacks are more likely to issue optimistic long-term annual earnings forecasts relative to the managers of a control group of unaffected firms. The exposure effect is stronger for more severe terrorist events, and firms with more uncertain fundamentals and less geographic diversification. In addition, we document that managers’ forecast optimism intensifies for firms with stronger negative stock market reaction to the terrorist event, for CEOs with higher ability and for companies that are more likely to issue equity or engage in acquisitions following the terrorist event. Overall, our results are consistent with the idea that long-term annual earnings forecasts are used by managers to counterbalance the short-term pessimistic response to terrorist attacks.     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

Seasonal decomposition and forecasting of telecommunication data: A comparative case study

In this paper, forecasting models for the monthly outgoing telephone calls in a University Campus are presented. The data have been separated in the categories of international and national calls as well as calls to mobile phones. The total number of calls has also been analyzed. Three different methods, namely the Seasonal Decomposition, Exponential Smoothing Method and SARIMA Method, have been used. Forecasts with 95% confidence intervals were calculated for each method and compared with the actual data. The outcome of this work can be used to predict future demands for the telecommunications network of the University.     

Capitalized intangibles and financial analysts

We examine whether firms that capitalize a higher proportion of their underlying intangible assets have higher analyst following, lower dispersion of analysts’ earnings forecasts and more accurate earnings forecasts relative to firms that capitalize a lower proportion. Under Australian generally accepted accounting principles, capitalization of intangible assets has become increasingly ‘routine’ since the late 1980s. It is predicted that this experience leads Australian analysts to expect firms with relatively more certain intangible investments to signal this fact by capitalizing intangible assets. Our results are consistent with this. We find that capitalization of intangible assets is associated with higher analyst following and lower absolute earnings forecast error for firms with a stock of underlying intangible assets. Our tests suggest a weaker association between capitalization and lower earnings forecast dispersion. We conclude that there are benefits for analysts, for management to have the option to capitalize intangible assets. These findings suggest that IAS 38 Intangible Assets and AASB 138 Intangible Assets reduce the usefulness of financial statements.     

Inference with Dependent Data in Accounting and Finance Applications

We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.     

Out‐of‐sample stock return predictability in emerging markets

This article builds on the widely debated issue of stock return predictability by applying a broad range of predictor variables and comprehensively considering the in‐sample and out‐of‐sample stock return predictability of ten advanced emerging markets. It compares forecasts from models with a single predictor variable, multiple predictor variables and a combination forecast approach. The results confirm the findings of Welch and Goyal (2008) for US data that only a limited number of individual predictor variables are able to deliver significant out‐of‐sample forecasts. However, a combination forecast approach provides statistically and economically significant out‐of‐sample forecast results.     

Political patronage and analysts’ forecast precision

We test whether the channel by which the government plays the role of political patron to selected firms influences analysts’ forecast precision in Malaysia. Correcting for analysts’ self-selection bias, we find a negative relation between analysts’ forecast errors and the social dimension of political patronage, as proxied by government-controlled institutional ownership. The reverse is found for the economic dimension of political patronage, as proxied by the percentage shareholding of government-linked corporations. We find no evidence that the personal dimension of political patronage influences analysts’ forecast precision.