Using investment portfolio return to combine forecasts: A multiobjective approach

This study investigates the usefulness and efficacy of a multiobjective decision method for financial trading guided by a set of seemingly diverse analysts' forecasts. The paper proposes a goal programming (GP) approach which combines various forecasts based on the performance of their previous investment returns. In our experiment, several series of financial analysts' forecasts are generated by different forecasting techniques. Investment returns on each series of forecasts are measured and then evaluated by three performance criteria, namely, mean, variance, and skewness. Subsequently, these distributional properties of the returns are used to construct a GP model. Results of the GP model provide a set of weights to compose an investment portfolio using various forecasts. To examine its practicality, the approach is tested on several major stock market indices. The performance of the proposed GP approach is compared with those of individual forecasting techniques and a number of forecast combination models suggested by previous studies. This comparison is conducted with respect to different levels of investor preference over return, variance, and skewness. Statistical significance of the results are accessed by bootstrap re-sampling. Empirical results indicate that, for all examined investor preference functions and market indices, the GP approach is significantly better than all other models tested in this study.     

A short and mean-term automatic forecasting system––application to textile logistics

In order to reduce their stocks and to limit stock out, textile companies require specific and accurate sale forecasting systems. More especially, textile distribution involves different forecast lead times: mean-term (one year) and short-term (one week in average). This paper presents two new complementary forecasting models, appropriate to textile market requirements. The first model (AHFCCX) allows to automatically obtain mean-term forecasting by using fuzzy techniques to quantify influence of explanatory variables. The second one (SAMANFIS), based on a neuro-fuzzy method, performs short-term forecasting by readjusting mean-term model forecasts from load real sales. To evaluate forecasts accuracy, our models and classical ones are compared to 322 real items sales series of an important ready to wear distributor.     

On the identification of sales forecasting models in the presence of promotions

Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.     

Combining Economic Forecasts

It is well known that no particular forecasting agency dominates when the accuracy of economic forecasts of the UK is investigated. There are good reasons for believing that if forecasts differ, some combination of them will be an improvement over the individual forecasts. The problem is to determine what weights to attach to each forecast. Various methods have been suggested in the literature, including equal weights (averaging), optimal weights (linear regression), varying weights based on past performance, and the Bayesian approach. We review these methods and examine their performance for important macro-economic variables.     

Forecasting the Development of the Market for Business Telephones in the UK

The problem of producing medium- to long-term forecasts of the market for business telephones is examined. Growth curves are generally appropriate for forecasting developing markets. However, this market is particularly sensitive to the state of business confidence and the feasibility of incorporating explanatory economic variables into the forecasting model is investigated. Three different model types are compared: growth curves with a fixed saturation level, multivariate linear models and growth curves with saturation levels determined by explanatory variables. The initial promise of models using explanatory variables is considerably diminished, once forecast rather than actual values of these variables are used. The market development model implicit in the growth curve is shown to be more robust than the linear model. Although the variable saturation level growth curve grants more insight into the maturity of the market, it does not produce significantly better forecasts than that with the fixed saturation level.     

不同矩属性波动模型对中国股市波动率的预测精度分析

金融时间序列的波动性建模经历了从一阶矩到二阶矩直到高阶矩(包含三阶矩和四阶矩)的过程,而对于高阶矩波动模型是否有助于对未来市场的波动率预测这一问题,国内外学术界尚无文献讨论。以上证综指长达7年的每5分钟高频数据样本为例,通过构建具有不同矩属性的波动模型,计算了中国股票市场波动率的预测值,并利用具有bootstrap特性的SPA检验法,实证检验了不同矩属性波动模型的波动率预测精度差异。实证结果显示:就中国股市而言,四阶矩波动模型能够取得比二阶矩波动模型更优的波动率预测精度,而三阶矩波动模型并未表现出比二阶矩波动模型更强的预测能力;在高阶矩波动模型中包含杠杆效应项并不能提高模型的预测精度。最后提出了在金融风险管理、衍生产品定价等领域引入四阶矩波动模型的研究思路。     

Forecasting Using Growth Curves—An Adaptive Approach

Growth curves such as the logistic and Gompertz are widely used for forecasting market development. The approach proposed is specifically designed for forecasting, rather than fitting available data—the usual approach with non-linear least squares regression. Two innovations form the foundation for this approach. The growth curves are reformulated from a time basis to an observation basis. This ensures that the available observations and the forecasts form a monotonic series; this is not necessarily true for least squares extrapolations of growth curves. An extension of the Kalman filter, an approach already used with linear forecasting models, is applied to the estimation of the growth curve coefficients. This allows the coefficients the flexibility to change over time if the market environment changes. The extended Kalman filter also proves the information for the generation of confidence intervals about the forecasts. Alternative forecasting approaches, least squares and an adaptive Bass model, suggested by Bretschneider and Mahajan, are used to produce comparative forecasts for a number of different data sets. The approach using the extended Kalman filter is shown to be more robust and almost always more accurate than the alternatives.     

Linear Gaussian affine term structure models with unobservable factors: Calibration and yield forecasting

This paper provides a significant numerical evidence for out-of-sample forecasting ability of linear Gaussian interest rate models with unobservable underlying factors. We calibrate one, two and three factor linear Gaussian models using the Kalman filter on two different bond yield data sets and compare their out-of-sample forecasting performance. One-step ahead as well as four-step ahead out-of-sample forecasts are analyzed based on the weekly data. When evaluating the one-step ahead forecasts, it is shown that a one factor model may be adequate when only the short-dated or only the long-dated yields are considered, but two and three factor models performs significantly better when the entire yield spectrum is considered. Furthermore, the results demonstrate that the predictive ability of multi-factor models remains intact far ahead out-of-sample, with accurate predictions available up to one year after the last calibration for one data set and up to three months after the last calibration for the second, more volatile data set. The experimental data denotes two different periods with different yield volatilities, and the stability of model parameters after calibration in both the cases is deemed to be both significant and practically useful. When it comes to four-step ahead predictions, the quality of forecasts deteriorates for all models, as can be expected, but the advantage of using a multi-factor model as compared to a one factor model is still significant.     

Medium‐term horizon volatility forecasting: A comparative study

In this paper, volatility is estimated and then forecast using unobserved components‐realized volatility (UC‐RV) models as well as constant volatility and GARCH models. With the objective of forecasting medium‐term horizon volatility, various prediction methods are employed: multi‐period prediction, variable sampling intervals and scaling. The optimality of these methods is compared in terms of their forecasting performance. To this end, several UC‐RV models are presented and then calibrated using the Kalman filter. Validation is based on the standard errors on the parameter estimates and a comparison with other models employed in the literature such as constant volatility and GARCH models. Although we have volatility forecasting for the computation of Value‐at‐Risk in mind the methodology presented has wider applications. This investigation into practical volatility forecasting complements the substantial body of work on realized volatility‐based modelling in business. Copyright © 2007 John Wiley & Sons, Ltd.     

Forecasting model with asymmetric market response and its application to pricing of consumer package goods

This paper presents a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable—price promotion—by threshold models. As a threshold variable to generate a mechanism for different market responses, we use the counterpart to the concept of a price threshold applied to a representative consumer in a store. A Bayesian approach is taken for statistical modelling because of advantages that it offers over estimation and forecasting. The proposed model incorporates the lagged effects of a price variable. Thereby, myriad pricing strategies can be implemented in the time horizon. Their effectiveness can be evaluated using the predictive density. We intend to improve the forecasting performance over conventional linear time series models. Furthermore, we discuss efficient dynamic pricing in a store using strategic simulations under some scenarios suggested by an estimated structure of the models. Empirical studies illustrate the superior forecasting performance of our model against conventional linear models in terms of the root mean square error of the forecasts. Useful information for dynamic pricing is derived from its structural parameter estimates. This paper develops a dynamic forecasting model that accommodates asymmetric market responses to marketing mix variable—price promotion—by the threshold models. Copyright © 2005 John Wiley & Sons, Ltd.     

A new approach of forecasting intermittent demand for spare parts inventories in the process industries

Accurate demand forecasting is of vital importance in inventory management of spare parts in process industries, while the intermittent nature makes demand forecasting for spare parts especially difficult. With the wide application of information technology in enterprise management, more information and data are now available to improve forecasting accuracy. In this paper, we develop a new approach for forecasting the intermittent demand of spare parts. The described approach provides a mechanism to integrate the demand autocorrelated process and the relationship between explanatory variables and the nonzero demand of spare parts during forecasting occurrences of nonzero demands over lead times. Two types of performance measures for assessing forecast methods are also described. Using data sets of 40 kinds of spare parts from a petrochemical enterprise in China, we show that our method produces more accurate forecasts of lead time demands than do exponential smoothing, Croston's method and Markov bootstrapping method.     

Top Management Systems for Forecasting and Monitoring Pay Costs and Staff Numbers

The size of the Department of Trade and Industry has mainly been controlled by a ceiling on the number of staff. Recently, the Government also introduced limits on running cost budgets, the largest component of which is pay cost. In 1986 these were delegated to 12 Deputy Secretaries. The budgets have to be set very tightly in order to remain within the Departmental limit on running costs; so it is essential that top management are able to forecast pay costs. Two models are described. One provides short term forecasts of staff numbers, the other forecasts their cost. The model forecasting staff numbers can be used to ensure that the ceiling on staff numbers is not exceeded. Its main use is as an input to the second model which forecasts pay costs. The models are now in use by top management; firstly, to allocate budgets throughout the Department, and secondly, to monitor the spend through the year.     

Forecasting the demand for banknotes: some empirical results for the Netherlands

The desired production of banknotes is the product of the demand for banknotes at any particular time, and the average lifetime. While the latter might be relatively constant, the first shows a steady increase over time. Various techniques are available to generate forecasts of banknote demand. They have in common that they are extrapolative methods which assume that the future may be derived from the pattern of the past. One major class of forecasting methods of this type are time series models, which are particularly useful in a limited information environment. Causal or regression models provide another class of forecasting methods and are particularly valuable for annual or medium-term prediction, and in cases where explanatory variables are controlled by the policy maker. Contrary to these quantitative models, qualitative forecasting methods do not require as many data but are sometimes very beneficial in guiding intuitive thinking about future developments.The present paper presents some examples of forecasting models studied at the Netherlands Bank as well as some results obtained from qualitative research undertaken in the recent past.     

Multi-item sales forecasting with total and split exponential smoothing

Efficient supply chain management relies on accurate demand forecasting. Typically, forecasts are required at frequent intervals for many items. Forecasting methods suitable for this application are those that can be relied upon to produce robust and accurate predictions when implemented within an automated procedure. Exponential smoothing methods are a common choice. In this empirical case study paper, we evaluate a recently proposed seasonal exponential smoothing method that has previously been considered only for forecasting daily supermarket sales. We term this method ‘total and split’ exponential smoothing, and apply it to monthly sales data from a publishing company. The resulting forecasts are compared against a variety of methods, including several available in the software currently used by the company. Our results show total and split exponential smoothing outperforming the other methods considered. The results were also impressive for a method that trims outliers and then applies simple exponential smoothing.     

A new look at forecasting annual corporate earnings in the U.S.A.

In this study, composite earnings per share models are estimated for 35 chemical, food, and utility firms during the 1981–1982 period. Although it is generally held that financial analysts produce superior earnings forecasts when compared to time series model forecasts, the results of this study indicate that analysts fared very poorly in 1982 and the average mean square forecasting error of analyst forecasts may be reduced by 74.2 percent by combining analyst and univariate time series model forecasts. This reduction is very interesting when one finds that the univariate time series model forecasts do not substantially deviate from those produced by random walk drift models, the ARIMA (0, 1, 1) process. Moreover, despite the high degree of correlation existing among analyst and time series forecasts, the ordinary least squares estimation of the composite earnings model is a better forecasting model than the composite earnings models estimated with ridge regression and latent root regression techniques.     

Mittelfristige Produktionsplanung bei ungewissen Szenarien

In medium term production planning at a highly aggregated level the uncertainty about future demand plays a central role. A widely used method to take the uncertainty into account is to investigate the same model with different scenarios. This approach produces only suboptimal results. In the first part of this paper some principles of optimality are formulated where forecasting is incorporated and future scenarios are treated as a stochastic process. The resulting models are of the type of a Markovian decision process. They have the property of actualization of forecasts (adaption), of looking ahead production smoothing (anticipation) and of efficient risk balancing. The different models are formulated in view of some typical situations occuring in practice. As a byproduct it is shown that the separation of long term forecasting and short term production planning may be disadvantageous. The theory developed so far will then be applied to a concrete situation in the automotive industriy. In particular the problem investigated is how to control the production rate throughout an imminent period of recession of unknown severity and duration. The computational results demonstrate that the model with a stochastic scenario yields smoother production lines than the model with a fixed scenario. This is due to an additional cost minimizing inertia caused by the stochastic law of motion.     

Towards a methodology for measuring the true degree of efficiency in a speculative market

Betting markets have drawn much attention in the economics, finance and operational research literature because they provide a valuable window on the manner in which individuals use information in wider financial markets. One question that has received particular attention is to what extent individuals discount information in market prices. The predominant approach to explore this issue involves predictive modeling to forecast market outcomes and examining empirically whether abnormal returns can be made by employing these forecasts. It is argued here that present practices to assess such forecasting models, including the use of point estimates and information, which would not be available in practice (at the forecasting stage) and failing to update forecasting models with information from the recent past, may give rise to misleading conclusions regarding a market's informational efficiency. Hypotheses are developed to conceptualize these views and are tested by means of extensive empirical experimentation using real-world data from the Hong Kong horserace betting market. Our study identifies several sources of bias and confirms that current practices may not be relied upon. A more appropriate modeling procedure for assessing the true degree of market efficiency is then proposed.     

Statistical merging of rating models

In this paper we introduce and discuss statistical models aimed at predicting default probabilities of Small and Medium Enterprises (SME). Such models are based on two separate sources of information: quantitative balance sheet ratios and qualitative information derived from the opinion mining process on unstructured data. We propose a novel methodology for data fusion in longitudinal and survival duration models using quantitative and qualitative variables separately in the likelihood function and then combining their scores linearly by a weight, to obtain the corresponding probability of default for each SME. With a real financial database at hand, we have compared the results achieved in terms of model performance and predictive capability using single models and our own proposal. Finally, we select the best model in terms of out-of-sample forecasts considering key performance indicators.     

Experimental study of a Bayesian method for daily electricity load forecasting

The method of Bayesian model discrimination is investigated for the possible contributions it may provide in the area of automatically forecasting the daily electricity demand cycle. A set of differing demand models have probabilities attached to them in such a way that these would be continuously updated with the available data and the actual forecasts obtained as expectations across all the models. Simulation experiments indicate significantly improved forecasting performance over a commonly used rescaling type of approach. Some practical issues in implementation are discussed.     

Stochastic Models for Oil Prices and the Pricing of Futures on Oil

Abstract

In this article, we investigate and compare the performance of various one-factor diffusion models in their ability to capture the behaviour of Brent crude oil prices. New proposed models, which have a three-quarters power in the diffusion term, are found to outperform all other popular models tested. Analytic solutions for futures prices under the new models are found and used to calibrate market prices. Results from the calibration show that one of the new three-quarters models with a mean-reverting property outperforms other popular models in fitting and forecasting futures prices.