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.     

Forecasting for the LCD monitor market

The TFT‐LCD (thin‐film transistor–liquid crystal display) industry is one of the key global industries with products that have high clock speed. In this research, the LCD monitor market is considered for an empirical study on hierarchical forecasting (HF). The proposed HF methodology consists of five steps. First, the three hierarchical levels of the LCD monitor market are identified. Second, several exogenously driven factors that significantly affect the demand for LCD monitors are identified at each level of product hierarchy. Third, the three forecasting techniques—regression analysis, transfer function, and simultaneous equations model—are combined to forecast future demand at each hierarchical level. Fourth, various forecasting approaches and disaggregating proportion methods are adopted to obtain consistent demand forecasts at each hierarchical level. Finally, the forecast errors with different forecasting approaches are assessed in order to determine the best forecasting level and the best forecasting approach. The findings show that the best forecast results can be obtained by using the middle‐out forecasting approach. These results could guide LCD manufacturers and brand owners on ways to forecast future market demands. Copyright 2008 John Wiley & Sons, Ltd.     

Improving moving average trading rules with boosting and statistical learning methods

We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out‐of‐sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy‐and‐hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.     

Stepwise replication identifies a low-producing lymphotoxin-alpha allele as a major risk factor for early-onset leprosy

Host genetics has an important role in leprosy, and variants in the shared promoter region of PARK2 and PACRG were the first major susceptibility factors identified by positional cloning. Here we report the linkage disequilibrium mapping of the second linkage peak of our previous genome-wide scan, located close to the HLA complex. In both a Vietnamese familial sample and an Indian case-control sample, the low-producing lymphotoxin-alpha (LTA)+80 A allele was significantly associated with an increase in leprosy risk (P = 0.007 and P = 0.01, respectively). Analysis of an additional case-control sample from Brazil and an additional familial sample from Vietnam showed that the LTA+80 effect was much stronger in young individuals. In the combined sample of 298 Vietnamese familial trios, the odds ratio of leprosy for LTA+80 AA/AC versus CC subjects was 2.11 (P = 0.000024), which increased to 5.63 (P = 0.0000004) in the subsample of 121 trios of affected individuals diagnosed before 16 years of age. In addition to identifying LTA as a major gene associated with early-onset leprosy, our study highlights the critical role of case- and population-specific factors in the dissection of susceptibility variants in complex diseases.     

On estimating contemporaneous quarterly regional GDP

Subnational regional jurisdictions rarely have at their disposal a reasonable array of timely statistics to monitor their economic condition. In light of this, we develop a procedure that simultaneously estimates a quarterly time series for all regions of a country based upon quarterly national and annual regional data. While other such techniques exist, we suggest a temporal error structure that eliminates possible spurious jumps. Using our approach, regional analysts should now be able to distribute national growth among regions as soon as quarterly national figures are released. In a Spanish application, we detail some practicalities of the process and show that our proposal produces better estimates than the uniregional methods often used. Copyright © 2007 John Wiley & Sons. Ltd.     

A recombinant dromedary antibody fragment (VHH or nanobody) directed against human Duffy antigen receptor for chemokines

Fy blood group antigens are carried by the Duffy antigen receptor for chemokines (DARC), a red cells receptor for Plasmodium vivax broadly implicated in human health and diseases. Recombinant VHHs, or nanobodies, the smallest intact antigen binding fragment derivative from the heavy chain-only antibodies present in camelids, were prepared from a dromedary immunized against DARC N-terminal extracellular domain and selected for DARC binding. A described VHH, CA52, does recognize native DARC on cells. It inhibits P. vivax invasion of erythrocytes and displaces interleukin-8 bound to DARC. The targeted epitope overlaps the well-defined DARC Fy6 epitope. K _D of CA52?CDARC equilibrium is sub-nanomolar, hence ideal to develop diagnostic or therapeutic compounds. Immunocapture by immobilized CA52 yielded highly purified DARC from engineered K562 cells. This first report on a VHH with specificity for a red blood cell protein exemplifies VHHs?? potentialities to target, to purify, and to modulate the function of cellular markers.     

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.     

Nowcasting with Google Trends in an Emerging Market

Most economic variables are released with a lag, making it difficult for policy‐makers to make an accurate assessment of current conditions. This paper explores whether observing Internet browsing habits can inform practitioners about aggregate consumer behavior in an emerging market. Using data on Google search queries, we introduce an index of online interest in automobile purchases in Chile and test whether it improves the fit and efficiency of nowcasting models for automobile sales. Despite relatively low rates of Internet usage among the population, we find that models incorporating our Google Trends Automotive Index outperform benchmark specifications in both in‐sample and out‐of‐sample nowcasts, provide substantial gains in information delivery times, and are better at identifying turning points in the sales data. Copyright © 2011 John Wiley & Sons, Ltd.     

Twisting the Dollar? On the Consistency of Short‐Run and Long‐Run Exchange Rate Expectations

We examine consistency properties of the exchange rate expectation formation process of short‐run and long‐run forecasts in the dollar/euro and yen/dollar market. Applying nonlinear consistency restrictions we show that in a simple expectation formation structure short‐run forecasts are indeed inconsistent with long‐run predictions. Moreover, we establish a ‘twist’ in the dollar/euro expectation formation process, i.e. market participants expect bandwagon effects in the short run, while they have stabilizing expectations in their long‐run forecasts. Applying a panel probit analysis we find that this twisting behavior is more likely to occur in periods of excess exchange rate volatility. Copyright © 2011 John Wiley & Sons, Ltd.