84 results found
Meade N, Driver C, 2023, Differing behaviours of forecasters of UK GDP growth, International Journal of Forecasting, Vol: 39, Pages: 772-790, ISSN: 0169-2070
The literature suggests that the dispersion of agents’ forecasts of an event flows from heterogeneity of beliefs and models. Using a data set of fixed event point forecasts of UK GDP growth by a panel of independent forecasters published by HM Treasury, we investigate three questions concerning this dispersion: (a) Are agent’s beliefs randomly distributed or do agents fall into groups with similar beliefs? (b) as agents revise their forecasts, what roles are played by their previous and consensus forecasts? and (c) is an agent’s private information of persistent value? We find that agents fall into four clusters, a large majority, a few pessimists, and two idiosyncratic agents. Our proposed model of forecast revisions shows agents are influenced positively by a change in the consensus forecast and negatively influenced by the previous distance of their forecast from the consensus. We show that the forecasts of a minority of agents significantly lead the consensus.
Islam T, Meade N, Carson RT, et al., 2022, The usefulness of socio-demographic variables in predicting purchase decisions: evidence from machine learning procedures, Journal of Business Research, Vol: 151, Pages: 324-338, ISSN: 0148-2963
Research has long debated the effectiveness of socio-demographics in understanding purchase behavior, with mixed conclusions. The appeal of socio-demographic data for customer relationship marketing is based on its low acquisition cost and the growing array of variables on which marketers can condition messages and offers. We reinvestigate the value of socio-demographic variables, focusing on the potential of machine learning procedures (MLPs) to extract a stronger and reliable signal than the standard linear-in-parameters (logistic) regression models. We explore how predictive power can be increased through the nonlinearities and interactions identified with MLPs; our experimental set ranges from well-established procedures to newer entrants in this space. We also examine causality vis-à-vis predictability using a propensity scoring approach. Empirics are based on six grocery product categories and more than 7,000 panelists. We find that, relative to logistic regression models, MLPs using demographic variables yield a 20% to 33% improvement in out-of-sample predictive accuracy.
Islam T, Meade N, Sood A, 2022, Timing market entry: the mediation effect of market potential, Journal of International Marketing, Vol: 30, Pages: 40-54, ISSN: 1069-031X
Timing a multinational firm's entry into a new country is a pivotal decision with long-term impact on the firm's overall performance; thus, a deeper understanding of the drivers of the decision and their interrelationship can yield significant managerial benefits. The authors explore the mediating role of market potential by decomposing the total effects of the decision's main drivers—macroeconomic attractiveness, market concentration, social heterogeneity, and population density—into direct and indirect effects. These decompositions explain the countervailing effects of some drivers that simultaneously make both positive and negative impacts. The data set encompasses mobile 4G broadband penetration in 130 countries, including market entry timings for 28 international operators in 79 countries. The authors establish the nature of the mediation effect of market potential on the drivers of entry timing. Using early penetration data, they utilize growth mixture modeling to divide the countries into four latent segments. They validate this segmentation using machine learning with the four key drivers as classifiers; the process establishes macroeconomic attractiveness as the predominant classifier. The analysis offers entry-timing guidance at both pre- and postlaunch stages.
Meade N, Islam T, 2021, Modelling and forecasting national introduction times for successive generations of mobile telephony, Telecommunications Policy, Vol: 45, ISSN: 0308-5961
An accurate prediction of the timing of a country's introduction of a new generation of mobile telephony benefits numerous agents including suppliers of network and consumer equipment, regulators, and network planners. We consider the estimation and prediction of the time interval between the international introduction of a generation of mobile telephony and its introduction into a specific country when a decision maker judges the introduction of a newer technology a worthwhile investment. Using literature-based socio-economic and geographical variables, we examine how well variation in international introduction times of four generations of mobile telephony in 172 countries can be explained and forecast. We model and forecast introduction times at two levels of granularity: we use Cox's proportional hazards model for the introduction time; we partition countries into introduction time-based segments and model segment membership using multinomial logistic regression. Our modelling of each generation considers three subsets of explanatory variables: All variables, socio-economic Covariates only, Regional dummies only. Over successive generations, the Covariates only models reveal the changing relevance of each socio-economic covariate. Model-based forecasting of the introduction time of the next generation is performed under three hypotheses making different uses of the information available at the time the relevant generation is launched internationally. However, changing socio-economic environments coupled with changing models impair forecasting accuracy, the lower accuracy of modelled introduction times is concentrated in 20% of countries. We speculate about the nature of the unobserved factors affecting these countries' decision processes.
Meade N, Beasley JE, Adcock CJ, 2021, Quantitative portfolio selection: using density forecasting to find consistent portfolios, European Journal of Operational Research, Vol: 288, Pages: 1053-1067, ISSN: 0377-2217
In the knowledge that the ex-post performance of Markowitz efficient portfolios is inferior to that implied ex-ante, we make two contributions to the portfolio selection literature. Firstly, we propose a methodology to identify the region of risk-expected return space where ex-post performance matches ex-ante estimates. Secondly, we extend ex-post efficient set mathematics to overcome the biases in the estimation of the ex-ante efficient frontier. A density forecasting approach is used to measure the accuracy of ex-ante estimates using the Berkowitz statistic, we develop this statistic to increase its sensitivity to changes in the data generating process. The area of risk-expected return space where the density forecasts are accurate, where ex-post performance matches ex-ante estimates, is termed the consistency region. Under the 'laboratory' conditions of a simulated multivariate normal data set, we compute the consistency region and the estimated ex-post frontier. Over different sample sizes used for estimation, the behaviour of the consistency region is shown to be both intuitively reasonable and to enclose the estimated ex-post frontier. Using actual data from the constituents of the US Dow Jones 30 index, we show that the size of the consistency region is time dependent and, in volatile conditions, may disappear. Using our development of the Berkowitz statistic, we demonstrate the superior performance of an investment strategy based on consistent rather than efficient portfolios.
Felipe Brito TL, Islam T, Mouette D, et al., 2020, Fuel price elasticities of market shares of alternative fuel vehicles in Brazil, Transportation Research Part D: Transport and Environment, Vol: 89, ISSN: 1361-9209
The Brazilian Alcohol Program promotes ethanol as an alternative fuel to gasoline. Policymakers want to know the effect of relative fuel prices on demand for gasoline-fuelled and alternative fuel vehicles (AFVs). Considering vehicle engines dedicated to gasoline, ethanol or flex in the Brazilian market, we use market share models to estimate fuel price market share elasticities, both own and cross effects for each technology. In the first phase, when gasoline and ethanol are the only competing technologies, we find that variations in price profoundly impact the market share of new vehicles. The second phase shows how the efficiency of flex engines, reflected by the car cost per kilometre, significantly contributes to the widespread acceptance of this technology. The near-zero elasticities indicate that the market share of flex vehicles is hedged against price fluctuation. The study provides useful suggestions to help policymakers accelerate the socio-technological transition towards renewable and cleaner energy.
Brito TLF, Islam T, Stettler M, et al., 2019, Transitions between technological generations of alternative fuel vehicles in Brazil, Energy Policy, Vol: 134, Pages: 110915-110915, ISSN: 0301-4215
The transportation sector is responsible for nearly a quarter of greenhouse gases emissions (GHG); thus, incisive policies are necessary to mitigate the sector’s effect on climate change. Promoting alternative fuel vehicles (AFV) is an essential strategy to reduce GHG emissions in the short term. Here, we study the effects of governmental incentives on the diffusion of ethanol and flex-fuel vehicle technologies in Brazil. We use a multi-generation diffusion model which assumes that new technologies introduce fresh market potential for adopters as well as upgraders from established technologies. Our analysis indicates that tax rates affected the adoption of both gasoline and ethanol technology, but for flex vehicles, the effect of taxation is not significant. The effect of fuel price shocks during the 1990s meant that the introduction of ethanol technology made no significant impact on market potential and a negative word-of-mouth effect contributed to the technology’s failure. In contrast, the introduction of flex technology led to almost a doubling of total market potential. As policy suggestions, we emphasise the importance of tax reduction in addition to promoting versatile technologies, which insulate consumers against price fluctuations.
Driver C, Meade N, 2019, Enhancing survey-based investment forecasts, Journal of Forecasting, Vol: 38, Pages: 236-255, ISSN: 0277-6693
We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.
Islam T, Meade N, 2018, The direct and indirect effects of economic wealth on time to take-off, International Journal of Research in Marketing, Vol: 35, Pages: 305-318, ISSN: 0167-8116
Our objective is to decompose the influence of the economic wealth on the time to sales take-off into a direct effect and an indirect effect through time to introduction. We use a traditional regression based and an advanced counterfactual framework for our analysis, based on adoption data for four generations of mobile phone from 172 countries. Our study extends the sales take-off literature by better understanding how the commercialization stage (time to introduction) affects the confirmation stage (time to sales take-off) in innovation diffusion while controlling for local market structure, socio-economic, demographic and cultural variables suggested in the literature. We show that economic wealth exerts: a positive direct effect by shortening sales take-off time; a negative indirect effect by shortening time to introduction which tends to extend time to sales take-off. The uncovering of this relationship is achieved by treating time to introduction as a mediating variable, departing from previous studies where it is treated as an exogeneous variable. We further show that the negative indirect effect is diluted in the case of high income countries but not in the case of upper middle-income countries. A sensitivity analysis shows the robustness of our findings. Our findings will help firms develop optimal market entry strategies considering the resources available.
Adcock CJ, Meade N, 2016, Using parametric classification trees for model selection with applications to financial risk management, European Journal of Operational Research, Vol: 259, Pages: 746-765, ISSN: 0377-2217
We describe two parametric classification tree methods, which allow formal selection of a member of aclass of generalised distributions. In the paper we consider generalised Beta distributions for non-negativerandom variables and the generalised skew-Student distribution for random variables distributed on thereal line. We introduce a class of symmetric generalised multivariate Student distributions, membersof which may also be selected using the classification trees. We present two versions of the parametricclassification tree: specific to general and general to specific. We apply the classification methods todaily returns on stocks from a selection of 15 major, mid-cap and emerging markets. The results showthat the majority of return distributions follow Student’s t, but that a non-negligible minority follow asymmetric generalised Student distribution. We confirm a well-known stylised fact about skewness: ittends not to be persistent. By contrast, kurtosis is persistent. Using the symmetric generalised multivariateStudent distribution, we present a risk management study based on efficient portfolios constructedfrom UKFTSE250 stocks and specifically concerned with the computation of value at risk. The case studydemonstrates that the model selection procedures based on the classification trees lead to more accuratecomputation of VaR than those based on the normal distribution or on non-parametric approaches. Thestudy also shows that the normal distribution may be used for VaR computations for larger portfolioswhen the holding period is longer.
Meade N, Islam T, 2015, Forecasting in telecommunications and ICT-A review, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 31, Pages: 1105-1126, ISSN: 0169-2070
Islam T, Meade N, 2015, Firm level innovation diffusion of 3G mobile connections in international context, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 31, Pages: 1138-1152, ISSN: 0169-2070
Valle CA, Meade N, Beasley JE, 2015, Factor neutral portfolios, OR SPECTRUM, Vol: 37, Pages: 843-867, ISSN: 0171-6468
Valle CA, Meade N, Beasley JE, 2015, An optimisation approach to constructing an exchange-traded fund, OPTIMIZATION LETTERS, Vol: 9, Pages: 635-661, ISSN: 1862-4472
Meade N, Islam T, 2015, Modelling European usage of renewable energy technologies for electricity generation, TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, Vol: 90, Pages: 497-509, ISSN: 0040-1625
Valle CA, Meade N, Beasley JE, 2014, Market neutral portfolios, OPTIMIZATION LETTERS, Vol: 8, Pages: 1961-1984, ISSN: 1862-4472
Valle CA, Meade N, Beasley JE, 2014, Absolute return portfolios, OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, Vol: 45, Pages: 20-41, ISSN: 0305-0483
Islam T, Meade N, 2013, The impact of attribute preferences on adoption timing: The case of photo-voltaic (PV) solar cells for household electricity generation, ENERGY POLICY, Vol: 55, Pages: 521-530, ISSN: 0301-4215
Boomsma TK, Meade N, Fleten S-K, 2012, Renewable energy investments under different support schemes: A real options approach, European Journal of Operational Research
Islam T, Meade N, 2012, The impact of competition, and economic globalization on the multinational diffusion of 3G mobile phones, Technological Forecasting and Social Change
Islam T, Meade N, 2011, Detecting the impact of market factors on sales takeoff times of analog cellular telephones, MARKETING LETTERS, Vol: 22, Pages: 197-212, ISSN: 0923-0645
Meade N, Beasley J, 2011, Detection of momentum effects using an index out-performance strategy, Quantitative Finance, Vol: 11, Pages: 313-326
Meade N, 2010, Oil prices – Brownian motion or mean reversion? A study using a one year ahead density forecast criterion, Energy Economics
For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. A characteristic of the price of oil is its sensitivity to shocks thus the density function of future prices must be able to incorporate the uncertainty due to shocks as well as the underlying volatility of a stable market.In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts.Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation process with GARCH and no mean reversion.
Ding J, Meade N, 2010, Forecasting accuracy of stochastic volatility, GARCH and EWMAmodels under different volatility scenarios', Applied Financial Economics
Meade N, Islam T, 2010, Using copulas to model repeat purchase behaviour - An exploratory analysis via a case study, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Vol: 200, Pages: 908-917, ISSN: 0377-2217
Meade N, Islam T, 2010, Modeling and Forecasting Diffusion, Gaining Momentum, Editors: Tidd, Publisher: Imperial College Pr, Pages: 373-426, ISBN: 9781848163546
This book adopts a multi-disciplinary and managerial process approach to understandingand promoting the adoption of innovations, based on the latest research ...
Gormley FM, Meade N, 2007, The utility of cash flow forecasts in the management of corporate cash balances, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Vol: 182, Pages: 923-935, ISSN: 0377-2217
Meade N, 2006, An assessment of the comparative accuracy of time series forecasts of patent filings: The benefits of disaggregation in space or time, Forecasting Innovations: Methods for Predicting Numbers of Patent Filings, Pages: 41-72, ISBN: 9783540359913
This work is concerned with methods for forecasting the filing of patents and was earned out in conjunction with the European Patent Office. The filings data were subdivided by:. Blocs-European Patent Convention countries, Japan, US and the Rest of the World. Industries-main Fields of Technology according to headings A-H of the International Patent Classification (WIPO 2000). The issues addressed are: the benefits of multivariate models versus univariate ones in exploiting any correlations between the filings in different blocs or industries; the effect of aggregation over time (from monthly to annual data) and the effect of aggregation by bloc or by industry on the accuracy of the forecast of total EPO filings. Two approaches are used: the ARIMA framework and the dynamic linear model (DLM) in both univariate and multivariate modes. The main results are: monthly data does tend to provide greater accuracy in annual forecasts; there are no significant benefits to be gained by multivariate modelling and no significant benefits are found from aggregating over blocs or industries. There are benefits from using monthly data, rather than annual data. The best modelling approaches are, for monthly data, the univariate dynamic linear model; for annual data either the univariate ARIMA or DLM could be used. The recommended forecast-ing approach provides a benchmark against which other forecasts drawing on different data sources can be compared. The filing of an application for a patent is the first step in achieving protection for intellectual property. The three major patent offices are the European (EPO), the Japanese (JPO) and the US (USPTO) offices. The examination of each application is a labour intensive process, involving technological and legal expertise. The motivation for this study of the forecasting of patent filings with the EPO is that the forecasts are a prerequisite for manpower planning at an aggregate scale and at the level of availability of expertise in dif
MEADE N, 2006, FORECASTING WITH GROWTH-CURVES - THE EFFECT OF ERROR STRUCTURE, JOURNAL OF FORECASTING, Vol: 7, Pages: 235-244, ISSN: 0277-6693
Meade N, Islam T, 2006, Modelling and forecasting the diffusion of innovation - A 25-year review, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 22, Pages: 519-545, ISSN: 0169-2070
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