Information acquisition in new product introduction

In the presence of huge losses from unsuccessful new product introductions, companies often seek forecast information from various sources. As the information can be costly, companies need to determine how much effort to put into acquiring the information. Such a decision is strategically important because an insufficient investment may cause lack of knowledge of product profitability, which in turn may lead to introducing a loss-making product or scrapping a potentially profitable one. In this paper, we use decision analytical models to study information acquisition for new product introduction. Specifically, we consider a decision maker (DM) who, prior to introducing a new product, can purchase forecasts and use the information to update his knowledge of the market demand. We analyze and compare two approaches: The first approach is to determine the total amount of forecasts to purchase all at once. The second one is to purchase forecasts sequentially and, based on the purchased forecasts, determine whether those forecasts are informative enough for making an introduction decision or an additional forecast is needed. We present dynamic programming formulations for both approaches and derive the optimal policies. Via a numerical study, we find the second approach, i.e., purchasing forecasts sequentially, can generate a significant profit advantage over the first one when (1) the cost of acquiring forecasts is neither too high nor too low, (2) the precision of the forecasts is of a moderate level, and (3) the profit margin of the new product is small.     

Incorporating Managerial Thinking in Prediction and Control: Case Study of Market Penetration

Managerial strategies, especially at the higher echelons of management, are often linguistically stated. This is because they need to be based on information which often defies quantification. Such verbal strategies and qualitative information have often been found to be difficult to incorporate in quantitative models. Thus, the quantitative effects of implementing one strategy as opposed to another have generally been difficult to forecast.In this paper, we show that, through the use of fuzzy logic, we can incorporate such qualitative (linguistically stated) information. Furthermore, we show that a fuzzy controller can be designed so as to reach desired goals while being cognizant of linguistically stated strategies, scenarios, and decision rules as well as quantitative data types.The approach is applied to the modeling and control of market penetration, a field which has attracted considerable attention in recent years.     

A systematic comparison of professional exchange rate forecasts with the judgemental forecasts of novices

Professional forecasters in foreign exchange markets are not able to beat naive forecasts. In order to find reasons for this phenomenon we compare the empirical forecasts of experts with the experimentally generated forecasts of novices for the EUR/USD exchange rate in three different forecast horizons. Although the subjects are only provided with the realizations of the exchange rate and are not supported by any statistical procedures they outperform experts in accuracy. Professionals consistently expect a reversal of forgoing exchange rate changes whereas novices extrapolate trends. The judgemental forecasts appear to be unbiased and professionals appear to be biased. We demonstrate that professionals are influenced by the fundamental value—an irrelevant anchor in speculative exchange markets. The poor performance of the experts is not a common failure of human decision-making in market environments but caused by misleading information.     

基于GARCH模型的人民币汇率波动规律研究

自人民币汇率体制改革以来,汇率波动日趋复杂.鉴于GARCH模型能够较好地拟合汇率时间序列的尖峰厚尾特征,本文采集了2003～2007年之间的1069个美元兑人民币汇率日值,应用GARCH模型进行分析,证实了我国外汇市场确实存在ARCH效应,且GARCH模型能够较好地拟合汇改后的人民币汇率数据.     

基于EMD-GA-BP与EMD-PSO-LSSVM的中国碳市场价格预测

由于碳交易市场价格的波动性大及相互影响关系的复杂性,本文试图构建碳价格长期和短期的最优预测模型。考虑到碳交易价格波动的趋势性和周期性特点,基于经验模态分解算法(EMD)、遗传算法(GA)—神经网络(BP)模型、粒子群算法(PSO)—最小二乘支持向量机(LSSVM)模型及由它们构建的组合预测模型,对中国碳市场交易价格进行短期预测和长期预测。实证分析中将影响碳交易价格的不同宏观经济因素和碳价格时间序列因素做为输入变量,分别代入组合模型进行预测。研究结果表明,在短期预测中,EMD-GA-BP模型预测效果优于GA-BP模型和PSO-LSSVM模型;而在长期预测中,组合模型EMD-PSO-LSSVM模型预测效果优于只考虑碳价格波动趋势性或周期性预测效果。     

主成分分析与支持向量机相结合的区域降水预测应用

将主成分分析和支持向量机回归相结合,以广西5、6月区域平均日降水量作为预报对象,进行区域日降水量预测研究.首先,整理分析大量的T213数值预报产品信息数据进行主成分分析,得到主成分数据序列;其次,根据主成分数据序列建立训练集训练支持向量机,并利用遗传算法优化参数;最后,输入支持向量机所需数据,得到主成分预测结果,建立广西日降水预报模型.实例计算结果表明,支持向量机回归模型比逐步回归模型有更好的预测能力.     

中国交易所债券市场分形特征的实证研究

分形是自然系统和社会经济系统中存在的一种非线性现象.对我国金融市场分形特征的现有研究集中于股票市场和外汇市场,本文研究了我国金融市场的另一重要组成部分-交易所债券市场的分形特征.实证结果显示,中国交易所债券市场的价格变动是以分数布朗运动方式进行的,所形成的运动轨迹呈现出典型的特征指数α<2的稳定帕累托分布,分形特征广泛存在于我国交易所债券市场各时间标度下的收益率中.最后提出了从市场的分形特征出发进行风险管理的思路.     

汇改与外汇市场波动性:大玩家理论的视角

央行在外汇市场的行动对市场波动性有重要影响。本文从主观预期概念出发,基于“大玩家”理论解释了2005年和2015年中国外汇管理体制两次改革前后人民币外汇市场的波动性差异。汇改前,央行积极地承担着“大玩家”的角色,人民币对美元汇率波动性较强;汇改后,央行的“大玩家”角色减弱,汇率波动性程度显著下降,市场效率增强。利用汇率数据进行的R/S分析支持了前述解释。     

Developing an early warning system to predict currency crises

The purpose of this paper is to develop an early warning system to predict currency crises. In this study, a data set covering the period of January 1992–December 2011 of Turkish economy is used, and an early warning system is developed with artificial neural networks (ANN), decision trees, and logistic regression models. Financial Pressure Index (FPI) is an aggregated value, composed of the percentage changes in dollar exchange rate, gross foreign exchange reserves of the Central Bank, and overnight interest rate. In this study, FPI is the dependent variable, and thirty-two macroeconomic indicators are the independent variables. Three models, which are tested in Turkish crisis cases, have given clear signals that predicted the 1994 and 2001 crises 12 months earlier. Considering all three prediction model results, Turkey’s economy is not expected to have a currency crisis (ceteris paribus) until the end of 2012. This study presents uniqueness in that decision support model developed in this study uses basic macroeconomic indicators to predict crises up to a year before they actually happened with an accuracy rate of approximately 95%. It also ranks the leading factors of currency crisis with regard to their importance in predicting the crisis.     

A class of multi-period semi-variance portfolio selection with a four-factor futures price model

Considering the stochastic exchange rate, a four-factor futures model with the underling asset, convenience yield, instantaneous risk free interest rate and exchange rate, is established. These processes follow jump-diffusion processes (Wiener process and Poisson process). The corresponding partial differential equation (PDE) of the futures price is derived. The general solution with parameters of the PDE is drawn. The weight least squares approach is applied to obtain the parameters of above PDE. Variance is substituted by semi-variance in Markovitz’s portfolio selection model. Therefore, a class of multi-period semi-variance model is formulated originally. A hybrid genetic algorithm (GA) with particle swarm optimizer (PSO) is proposed to solve the multi-period semi-variance model. Finally, an example, which are fuel futures in Shanghai exchange market, is selected to demonstrate the effectiveness of above models and methods.     

Study on network traffic forecast model of SVR optimized by GAFSA

There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.     

Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northeastern Greece

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision aid (MCDA) methods for incorporating conflicting objectives and decision makers’ preferences into spatial decision models. In this paper, we present spatial UTASTAR (S-UTASTAR), a raster-based MC-SDSS for land-use suitability analysis. The multicriteria component of the system is based on the UTA-type disaggregation-aggregation approach. S-UTASTAR is applied in a raster-based case study concerning land-use suitability analysis to identify appropriate municipal solid waste landfill (MSW) sites in Northeast Greece. Moreover, robustness analysis tools are implemented to guarantee robust decision support results. More specifically, during the aggregation phase, the Stochastic Multiobjective Acceptability Analysis (SMAA) is used to indicate the frequency at which a site achieves the best ranking positions within a large set of alternative landfill sites.     

The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties

Many multiple attribute decision analysis (MADA) problems are characterised by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) approach has been developed in the 1990s and in the recent years to support the solution of MADA problems with ignorance, a kind of probabilistic uncertainty. In this paper, the ER approach is further developed to deal with MADA problems with both probabilistic and fuzzy uncertainties.In this newly developed ER approach, precise data, ignorance and fuzziness are all modelled under the unified framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A utility-based grade match method is proposed to transform both numerical data and qualitative (fuzzy) assessment information of various formats into the fuzzy belief structure. A new fuzzy ER algorithm is developed to aggregate multiple attributes using the information contained in the fuzzy belief matrix, resulting in an aggregated fuzzy distributed assessment for each alternative. Different from the existing ER algorithm that is of a recursive nature, the new fuzzy ER algorithm provides an analytical means for combining all attributes without iteration, thus providing scope and flexibility for sensitivity analysis and optimisation. A numerical example is provided to illustrate the detailed implementation process of the new ER approach and its validity and wide applicability.     

基于R-vine-copula-CoVaR模型的金融市场风险溢出效应研究

为了挖掘国际金融市场与中国金融市场的风险溢出效应,本文首先通过ARJI-GARCH模型捕捉单个市场收益率的跳跃等典型事实特征,然后采用最大生成树(Maximum Spanning Tree,MST)算法优化的R-vine来刻画多维金融资产的复杂相依结构;最后构建R-vine-copula-CoVaR模型,测度了国际原油市场、国际黄金市场、美国股票市场与中国股票市场、外汇市场之间的风险溢出效应。实证结果表明:各市场之间均存在双向风险溢出效应,但溢出程度差别很大,国际黄金市场是风险溢出的最大爆发源,仅有中国外汇市场与中国股票市场、国际黄金市场间存在负向风险溢出;市场之间的双向风险溢出效应呈非对称性,国际原油市场与黄金市场的风险溢出效应远大于中国股票市场与外汇市场风险溢出效应;Rosenb-Latt检验表明基于R藤的CoVaR风险溢出测度更具有灵活性和有效性;后验测试结果表明R-vine-copula-CoVaR模型能有效地测度国际金融市场对中国金融市场风险溢出效应,而对中国金融市场风险溢出效应的CoVaR测度存在被高估的可能。     

Simultaneity and non‐linear variability in financial markets: simulation and forecasting

Non‐linear variability in financial markets can emerge from several mechanisms, including simultaneity and time‐varying coefficients. In simultaneous equation systems, the reduced‐form coefficients that determine the behaviour of jointly dependent variables are products and ratios of the original structural coefficients. If the coefficients are stochastic, the resulting multiplicative interactions will result in high degrees of non‐linearity. Processes generated in this way will scale as fractals: they will exhibit intermittent outliers and scaling symmetries, i.e. proportionality relationships between fluctuations at different separation distances. A model is specified in which both the exchange rate itself and the exchange rate residual exhibit simultaneity. The exchange rate depends on other exchange rates, while the residual depends on the other residuals. The model is then simulated using embedding noise from a t‐distribution. The simulations replicate the observed properties of exchange rates, heavy‐tailed distributions and long memory in the variance. A forecasting algorithm is specified in two stages. The first stage is a model for the actual process. In the second stage the residuals are modelled as a function of the predicted rate of change. The first and second stage models are then combined. This algorithm exploits the scaling symmetry: the residual is proportional to the predicted rate of change at separation distances corresponding to the forecast horizon. The procedure is tested empirically on three exchange rates. At a daily frequency and a 1‐day forecast horizon, two‐stage models reduce the forecast error by one fourth. At a 5‐day horizon, the improvement is 10–15 percent. At a weekly frequency, the improvement at the 1‐week horizon is on the order of 30–40 percent. Copyright © 2006 John Wiley & Sons, Ltd.     

Estimating value at risk with semiparametric support vector quantile regression

Value at Risk (VaR) has been used as an important tool to measure the market risk under normal market. Usually the VaR of log returns is calculated by assuming a normal distribution. However, log returns are frequently found not normally distributed. This paper proposes the estimation approach of VaR using semiparametric support vector quantile regression (SSVQR) models which are functions of the one-step-ahead volatility forecast and the length of the holding period, and can be used regardless of the distribution. We find that the proposed models perform better overall than the variance-covariance and linear quantile regression approaches for return data on S&P 500, NIKEI 225 and KOSPI 200 indices.     

Usage of 3D geometry descriptions and transformation techniques in log bucking and curve sawing

Detailed three dimensional models are nowadays frequently used in cross-cutting (bucking) tree stems into logs and in breakdown processes of logs into boards and flitches. Such models require increasingly sophisticated optimization models to assist planners (or automated decision support systems) in decision making. In this paper we develop two techniques that are linked to each other. The first is concerned with establishing high quality analytic approximations of full trees that are needed in full stem bucking applications. One important aspect is that inaccuracies due to measurement error can be reduced. The second is a transformation technique that makes it possible to apply curve sawing on logs in a standard straight sawing system. Numerical results based on real data are presented that support the usefulness of the techniques.     

Degeneracy in infinite horizon optimization

We consider sequential decision problems over an infinite horizon. The forecast or solution horizon approach to solving such problems requires that the optimal initial decision be unique. We show that multiple optimal initial decisions can exist in general and refer to their existence as degeneracy. We then present a conceptual cost perturbation algorithm for resolving degeneracy and identifying a forecast horizon. We also present a general near-optimal forecast horizon.This material is based on work supported by the National Science Foundation under Grants ECS-8409682 and ECS-8700836.     

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.