Publications
84 results found
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, Editors: Hingley, Nicolas M, Heidelberg, Publisher: Springer, Pages: 40-72, ISBN: 978-3-540-35991-3
Meade N, Maier MR, 2003, Evidence of long memory in short-term interest rates, JOURNAL OF FORECASTING, Vol: 22, Pages: 553-568, ISSN: 0277-6693
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- Citations: 17
Meade N, Islam T, 2003, Modelling the dependence between the times to international adoption of two related technologies, International Symposium on Forecasting (ISF), Publisher: ELSEVIER SCIENCE INC, Pages: 759-778, ISSN: 0040-1625
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- Citations: 17
Beasley JE, Meade N, Chang TJ, 2003, An evolutionary heuristic for the index tracking problem, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Vol: 148, Pages: 621-643, ISSN: 0377-2217
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- Citations: 191
Antipov A, Meade N, 2002, Forecasting call frequency at a financial services call centre, JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, Vol: 53, Pages: 953-960, ISSN: 0160-5682
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- Citations: 25
Meade N, 2002, A comparison of the accuracy of short term foreign exchange forecasting methods, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 18, Pages: 67-83, ISSN: 0169-2070
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- Citations: 40
Islam T, Fiebig DG, Meade N, 2002, Modelling multinational telecommunications demand with limited data, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 18, Pages: 605-624, ISSN: 0169-2070
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- Citations: 51
Meade N, 2001, Forecasting the diffusion of innovations: implications for time series extrapolation, Principles of forecasting : a handbook for researchers and practitioners, Editors: Armstrong, Boston, Mass., London, Publisher: Kluwer Academic, Pages: 577-595, ISBN: 9780792379300
Driver C, Meade N, 2001, Persistence of capacity shortage and the role of adjustment costs, Scottish Journal of Political Economy, Vol: 48, Pages: 27-47, ISSN: 0036-9292
Meade N, 2000, Evidence for the selection of forecasting methods, JOURNAL OF FORECASTING, Vol: 19, Pages: 515-535, ISSN: 0277-6693
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- Citations: 45
Chang TJ, Meade N, Beasley JE, et al., 2000, Heuristics for cardinality constrained portfolio optimisation, COMPUTERS & OPERATIONS RESEARCH, Vol: 27, Pages: 1271-1302, ISSN: 0305-0548
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- Citations: 529
Islam T, Meade N, 2000, Modelling diffusion and replacement, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Vol: 125, Pages: 551-570, ISSN: 0377-2217
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- Citations: 21
Meade N, 2000, A note on the Robust Trend and ARARMA methodologies used in the M3 Competition, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 16, Pages: 517-519, ISSN: 0169-2070
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- Citations: 10
Meade N, SALKIN GR, 2000, The selection of multinational equity portfolios: forecasting models and estimation risk, The European Journal of Finance, Vol: 6, Pages: 259-279
Fildes R, Hibon M, Makridakis S, et al., 1998, Generalising about univariate forecasting methods: further empirical evidence, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 14, Pages: 339-358, ISSN: 0169-2070
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- Citations: 88
Meade N, Islam T, 1998, Technological forecasting - Model selection, model stability, and combining models, MANAGEMENT SCIENCE, Vol: 44, Pages: 1115-1130, ISSN: 0025-1909
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- Citations: 139
Dempsey M, 1998, The Impact of Personal Taxes on the Firm’s Weighted Average Cost of Capital and Investment Behaviour: A Simplified Approach Using the Dempsey Discounted Dividends Model, Journal of Business Finance & Accounting, Vol: 25, Pages: 747-763, ISSN: 0306-686X
<jats:p>The discounted dividends model advanced by Dempsey (1996) is extended to provide a weighted average cost of capital (WACC) assessment of investment opportunities with irregular cash flows. Thereafter, the framework is extended to an assessment of the implications of government tax policy for the firm’s investment behaviour. The developed framework is consistent with the empirical evidence of Poterba and Summers (1985) which — over the period of UK tax history 1950–1983 encompassing four major tax on equity reforms — observes how the related dividend and investment politics of UK firms appear to be influenced by the level of dividend taxes.</jats:p>
Armstrong JS, Koehler AB, Fildes R, et al., 1998, Commentaries on "Generalising about univariate forecasting methods: further empirical evidence", International Journal of Forecasting, Vol: 14, Pages: 359-366
Islam T, Meade N, 1997, The diffusion of successive generations of a technology: A more general model, TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, Vol: 56, Pages: 49-60, ISSN: 0040-1625
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- Citations: 89
Adcock CJ, Meade N, 1997, A comparison of two LP solvers and a new IRLS algorithm for L1 estimation, L1 - Statistical procedures and related topics, Editors: Dodge, California, Publisher: Institute of Mathematical Statistics, Pages: 119-132
Islam T, Meade N, 1996, Forecasting the development of the market for business telephones in the UK, JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, Vol: 47, Pages: 906-918, ISSN: 0160-5682
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- Citations: 18
Vasilellis GA, Meade N, 1996, Forecasting volatility for portfolio selection, Journal of Business Finance and Accounting, Vol: 23, Pages: 125-143, ISSN: 0306-686X
The results discussed both support and extend the findings described in the literature. For an investment horizon of three months, it has been shown that an option based implied volatility is the best individual forecasting model of actual volatility. The importance of the choice of option model, weighting scheme and averaging of implied volatility estimates has been demonstrated, since both the best and the worst volatility forecasts were produced by option models. The Beckers-Latane-Rendleman weighting scheme in conjunction with averaging over five days was found to be the most successful way of estimating implied volatility. For volatility forecasts based on historical time series of returns, the GARCH model performed better than the more simple unweighted and exponentially weighted models, although not significantly so. It is possible that, since a common GARCH volatility forecasting model was used for all companies, a bespoke GARCH model with maximum likelihood estimates (and, perhaps a dummy variable to mitigate the effect of the 1987 crash (see, for example, Chan and Karolyi, 1991) would produce a better time series forecast. © Blackwell Publishers Ltd. 1996.
Meade N, Vasilellis GA, 1996, Forecasting the volatility of equities, Journal of Business, Finance and Accounting, Vol: 23, Pages: 125-143
Meade N, 1995, Neural network time series forecasting of financial markets - Azoff,EM, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 11, Pages: 601-602, ISSN: 0169-2070
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- Citations: 4
Meade N, 1995, Neural networks in the capital markets - Refenes,AP, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 11, Pages: 602-603, ISSN: 0169-2070
MEADE N, ISLAM T, 1995, PREDICTION INTERVALS FOR GROWTH CURVE FORECASTS, 14th International Symposium on Forecasting, Publisher: JOHN WILEY & SONS LTD, Pages: 413-430, ISSN: 0277-6693
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- Citations: 47
MEADE N, ISLAM T, 1995, FORECASTING WITH GROWTH-CURVES - AN EMPIRICAL-COMPARISON, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 11, Pages: 199-215, ISSN: 0169-2070
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- Citations: 88
Meade N, Islam T, 1995, Prediction intervals for growth curve forecasts, Journal of Forecasting, Vol: 14, Pages: 413-430
FLAVELL R, Meade N, 1995, The construction of a discount function using linear programming, Quantitative methods, super computers and AI in finance, Editors: Zenios, Cheltenham, Publisher: Stanley Thornes, Pages: 229-242
ADCOCK CJ, MEADE N, 1994, A SIMPLE ALGORITHM TO INCORPORATE TRANSACTIONS COSTS IN QUADRATIC OPTIMIZATION, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Vol: 79, Pages: 85-94, ISSN: 0377-2217
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- Citations: 29
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