An intelligent business forecaster for strategic business planning

In this paper an intelligent business forecaster for strategic business planning is presented. The forecaster is basically a multi‐layered fuzzy rule‐based neural network which integrates the basic elements and functions of a traditional fuzzy logic inference into a neural network structure. It has also been shown to be superior to two commercially available business forecasters in terms of learning speed and forecasting accuracy. This paper presents the architectural design of the intelligent business forecaster and the results of a study that has been carried out to compare its performance with that of the others. Copyright © 1999 John Wiley & Sons, Ltd.     

The ability of human mothers to identify the hunger cry signals of their own new-born infants during the lying-in period

Zusammenfassung Es wurden die Fähigkeiten der Mütter geprüft, das Hungerschreisignal ihrer eigenen Neugeborenen während des Wochenbettes zu identifizieren. Man hat festgestellt, dass es für etwa 1/3 der getesteten Mütter möglich ist, vorher auf Band aufgenommene Stimmen der eigenen Kinder schon in diesem frühen Alter wiederzuerkennen.     

Forecasting UK industrial production over the business cycle

This paper examines the information available through leading indicators for modelling and forecasting the UK quarterly index of production. Both linear and non‐linear specifications are examined, with the latter being of the Markov‐switching type as used in many recent business cycle applications. The Markov‐switching models perform relatively poorly in forecasting the 1990s production recession, but a three‐indicator linear specification does well. The leading indicator variables in this latter model include a short‐term interest rate, the stock market dividend yield and the optimism balance from the quarterly CBI survey. Copyright © 2001 John Wiley & Sons, Ltd.     

Tentative business confidence indicators for the Italian economy

This paper concerns the cyclical evolution of the Italian economy and, in particular, the role of confidence indicators. In the first part of the paper various confidence indicators, computed according to the European Commission (EC) methodology, are investigated. In particular, the analysis concentrates on the dynamics of some business climate indicators referred to the supply side of the economy (i.e. manufacturing, retail and construction industries). In the second part of the analysis, new confidence indicators exploring the wide informative set characterizing the ISAE business surveys are computed. The ability of these indicators in predicting the short‐term evolution of GDP, here considered as a reference series, is compared with that of the previous EC confidence indicators. Finally, in the third part, some estimates of the relationship between the chosen business confidence indicator and some driving variables are presented. Copyright © 2003 John Wiley & Sons, Ltd.     

Autoregressive-asymmetric moving average models for business cycle data

Much business cycle research is based on an assumption of symmetric cycles, though it is frequently argued that the downturns are steeper and more short-lived than the upturns; implying cyclical asymmetries. A new class of nonlinear autoregressive-asymmetric moving average models is introduced. These models are able to deal with symmetric as well as asymmetric phenomena. A likelihood estimation procedure and a Wald test statistic for symmetry are presented. Evidence of asymmetry is found in US real GNP growth rates.     

Improved business planning through an awareness of political cycles

Can business planning be improved if more attention is paid to underlying political cycles? This paper compares practitioner and researcher perspectives on this issue. While practioners stand to gain useful insights from a careful examination of past political cycles, these insights may be disconfirmed by rigorous tests carried out by researchers. In this paper we isolate and examine five hypotheses from the literature on the political-economic cycle.     

Forecasting the business cycle without using minimum autocorrelation factors

We introduce a forecasting technique based on multivariate ideas previously applied in remote sensing. The approach has the trivial but nonetheless fundamental purpose of dividing the information inherent in the time series into important and unimportant. Important information is used for forecasting purposes while the unimportant is discarded. Although related to vector autoregression, giving asymptotically the same estimates, there are reasons to believe that the approach gives better precision of parameter estimates for finite samples as well as more precise predictions.     

Enteropathogenic Escherichia coli: a pathogen that inserts its own receptor into host cells

Enteropathogenic Escherichia coli (EPEC) is a major cause of infant diarrhea, killing hundreds of thousands of children per year worldwide. Intimate attachment to the host cell leading to the formation of actin-rich pedestals beneath the adhering bacteria is an essential feature of EPEC pathogenesis. EPEC attaches to host cells via the outer membrane adhesin, intimin. It was recently shown that EPEC inserts its own receptor for intimate adherence, Tir (translocated intimin receptor) into the host cell membrane. The focus of this review is on the discovery and characterization of this novel receptor, and our current understanding of its role in pedestal formation. Gram-negative bacterial secretion systems, including type III secretion systems, are reviewed and discussed in the context of Tir delivery into the host cell membrane. The relationship and relevance of in vitro models compared to the actual in vivo situation is essential to understanding disease. We have critically reviewed the use of animal models in studying EPEC infection. Elucidating the function of Tir will contribute to our understanding of how EPEC mediates disease.     

The accuracy of individual and group forecasts from business outlook surveys

This paper reports on the accuracy of quarterly multiperiod predictions of inflation, real growth, unemployment and percentage changes in nominal GNP and two of its more volatile components. The survey data are highly differentiated; they cover 79 professional forecasters (mostly economists, analysts and corporate executives). Combining corresponding predictions from different sources can result in significant gains; thus the group mean forecasts are on the average over time more accurate than most of the corresponding sets of individual forecasts. But there is also a moderate degree of consistency in the relative performance of a sufficient number of the survey members, as evidenced in positive rank correlations among ratios of the individual to group root mean square errors.     

Tuning up mind's pattern to nature's own idea: Eddington's early twenties case for variational derivatives

This paper sets out to show how Eddington's early twenties case for variational derivatives significantly bears witness to a steady and consistent shift in focus from a resolute striving for objectivity towards “selective subjectivism” and structuralism. While framing his so-called “Hamiltonian derivatives” along the lines of previously available variational methods allowing to derive gravitational field equations from an action principle, Eddington assigned them a theoretical function of his own devising in The Mathematical Theory of Relativity (1923). I make clear that two stages should be marked out in Eddington's train of thought if the meaning of such variational derivatives is to be adequately assessed. As far as they were originally intended to embody the mind's collusion with nature by linking atomicity of matter with atomicity of action, variational derivatives were at first assigned a dual role requiring of them not only to express mind's craving for permanence but also to tune up mind's privileged pattern to “Nature's own idea”. Whereas at a later stage, as affine field theory would provide a framework for world-building, such “Hamiltonian differentiation” would grow out of tune through gauge-invariance and, by disregarding how mathematical theory might precisely come into contact with actual world, would be turned into a mere heuristic device for structural knowledge.     

The maternal protection reaction against a mother's rejection of her own larva in Salamandra salamandra L]

In vitro experiments have shown that maternal spleen cells from Salamandra salamandra are cytotoxic to cells from their embryos. This reaction can be inhibited by maternal serum. In this paper, we show that maternal serum protection acts through two effects: by inactivating spleen cells and by protecting embryonic cells. The more numerous the embryos are in a female, the stronger the protection is. The effect of the maternal serum does not appear to be individual specific.     

Statistical surveillance of cyclical processes with application to turns in business cycles

On‐line monitoring of cyclical processes is studied. An important application is early prediction of the next turn in business cycles by an alarm for a turn in a leading index. Three likelihood‐based methods for detection of a turn are compared in detail. One of the methods is based on a hidden Markov model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve. Evaluations are made of the effect of different specifications of the curve and the transitions. The methods are made comparable by alarm limits, which give the same median time to the first false alarm, but also other approaches for comparability are discussed. Results are given on the expected delay time to a correct alarm, the probability of detection of a turning point within a specified time, and the predictive value of an alarm. Copyright © 2005 John Wiley & Sons, Ltd.     

Unemployment variation over the business cycles: a comparison of forecasting models

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non‐linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non‐linearity in the unemployment series. Only recently have there been some developments in applying non‐linear models to estimate and forecast unemployment rates. A major concern of non‐linear modelling is the model specification problem; it is very hard to test all possible non‐linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non‐linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back‐propagation model and a generalized regression neural network model to estimate and forecast post‐war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out‐of‐sample forecast results obtained by the ANN models with those obtained by several linear and non‐linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.     

The teachers/practitioners corner forecasting in business schools: A survey

The study of forecasting techniques has received increased attention in recent years. How to incorporate this topic into the business school curriculum is a frequent subject of discussion. The purpose of this study was to determine whether forecasting is being taught in business schools and how it is incorporated into the curriculum. The survey instrument was sent to 622 member institutions of the American Assembly of Collegiate schools of Business. The importance of teaching forecasting techniques at both the undergraduate and graduate level was investigated.     

Forecasting for business planning: A case study of IBM product sales

This is a case study of a closely managed product. Its purpose is to determine whether time-series methods can be appropriate for business planning. By appropriate, we mean two things: whether these methods can model and estimate the special events or features that are often present in sales data; and whether they can forecast accurately enough one, two and four quarters ahead to be useful for business planning. We use two time-series methods, Box-Jenkins modeling and Holt-Winters adaptive forecasting, to obtain forecasts of shipments of a closely managed product. We show how Box-Jenkins transfer-function models can account for the special events in the data. We develop criteria for choosing a final model which differ from the usual methods and are specifically directed towards maximizing the accuracy of next-quarter, next-half-year and next-full-year forecasts. We find that the best Box-Jenkins models give forecasts which are clearly better than those obtained from Holt-Winters forecast functions, and are also better than the judgmental forecasts of IBM's own planners. In conclusion, we judge that Box-Jenkins models can be appropriate for business planning, in particular for determining at the end of the year baseline business-as-usual annual and monthly forecasts for the next year, and in mid-year for resetting the remaining monthly forecasts.     

Forecasting and the database: An analysis of databases for international business

A major consideration in the selection of a forecasting method for a specific situation is the type of pattern in the data. Before the data pattern is identified, the forecaster must recognize the dependence of any forecasting method upon the accompanying reliable database. This issue is discussed in the paper with reference to databases for international business.     

Evaluating early warning and coincident indicators of business cycles using smooth trends

We present a composite coincident indicator designed to capture the state of the Spanish economy. Our approach, based on smooth trends, guarantees that the resulting indicators are reasonably smooth and issue stable signals, reducing the uncertainty. The coincident indicator has been checked by comparing it with the one recently proposed by the Spanish Economic Association index. Both indexes show similar behavior and ours captures very well the beginning and end of the official recessions and expansion periods. Our coincident indicator also tracks very well alternative mass media indicators typically used in the political science literature. We also update our composite leading indicator (Bujosa et al., Journal of Forecasting, 2013, 32(6), 481–499). It systematically predicts the peaks and troughs of the new Spanish Economic Association index and provides significant aid in forecasting annual gross domestic product growth rates. Using only real data available at the beginning of each forecast period, our indicator one-step-ahead forecast shows improvements over other individual alternatives and different forecast combinations.     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.     

What can we learn from the return predictability over the business cycle?

In this paper, we forecast stock returns using time‐varying parameter (TVP) models with parameters driven by economic conditions. An in‐sample specification test shows significant variation in the parameters. Out‐of‐sample results suggest that the TVP models outperform their constant coefficient counterparts. We also find significant return predictability from both statistical and economic perspectives with the application of TVP models. The out‐of‐sample R² of an equal‐weighted combination of TVP models is as high as 2.672%, and the gains in the certainty equivalent return are 214.7 basis points. Further analysis indicates that the improvement in predictability comes from the use of information on economic conditions rather than simply from allowing the coefficients to vary with time.