A Method for Pooling Forecasts

A group of experts is to produce a joint forecast of a set of unknowns. Each expert is asked to distribute subjectively a given sum of confidence weights over his own forecasts. A joint forecast is computed as the product sum of the individual forecasts and weights deduced from the individual's weights. A probabilistic interpretation of this procedure is provided and a measure of the reliability of the joint forecasts is suggested. A Bayesian variant can be constructed by introducing sample information.     

Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions

New product development involves several critical decisions. A key decision making area in new product development is the evaluation of the viability and the market potentials of a new product. In the absence of any relevant historical data, companies ask the potential buyers of their products about their intentions to buy those products when assessing their viability. Despite the popularity of the use of behavioral intentions in predicting the market acceptance of new product ideas, both survey and empirical studies suggest that the accuracy of such predictions is usually very low. Although earlier case-based studies suggest that a number of factors can affect the quality of new product decisions, it is still empirically unclear how product knowledge and the type of new products might impact the predictive accuracy of intentions-based new product forecasting. This study utilized a longitudinal research design and empirically tested the hypotheses across two new products. The study first collected purchase intentions data about the new products. Second, it collected subsequent actual purchase data about the new products. The results of series of hierarchical regression analyses comparing the initial purchase intentions and subsequent actual behaviors showed that while product knowledge is positively related to the predictive accuracy and consistency of intentions-based new product forecasting, product type is negatively related to them.     

Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains

Operational forecasting in supply chain management supports a variety of short-term planning decisions, such as production scheduling and inventory management. In this respect, improving short-term forecast accuracy is a way to build a more agile supply chain for manufacturing companies. Demand forecasting often relies on well-established univariate forecasting methods to extrapolate historical demand. Collaboration across the supply chain, including information sharing, is suggested in the literature to improve upon the forecast accuracy of such traditional methods. In this paper, we review empirical studies considering the use of downstream information in demand forecasting and investigate different modeling approaches and forecasting methods to incorporate such data. Where empirical findings on information sharing mainly focus on point-of-sale data in two-level supply chains, this research empirically investigates the added value of using sell-through data originating from intermediaries, next to historical demand figures, in a multi-echelon supply chain. In a case study concerning a US drug manufacturer, we evaluate different methods to incorporate this data and consider both time series methods and machine learning techniques to produce multi-step ahead weekly forecasts. The results show that the manufacturer can effectively improve its short-term forecast accuracy by integrating sell-through data into the forecasting process and provide useful insights as to the different modeling approaches used. The conclusion holds for all forecast horizons considered, though it is most pronounced for one-step ahead forecasts. Therefore, our research provides a clear incentive for manufacturers to assess the forecast accuracy that can be achieved by using sell-through data.     

A model of product line design and introduction sequence with reservation utility

Cannibalization is a major concern for a firm when designing a product line. In addition, external options from outside the firm’s product line may also play a significant role. In this paper, we investigate the impact of external options, represented by reservation utility, on product line design and introduction sequence. We find that: (a) heterogeneous reservation utility defines the relative attractiveness of segments and corresponding product line; (b) reservation utility makes it more favorable to introduce products sequentially rather than simultaneously; (c) aggregating segments is an effective way to mitigate cannibalization when it becomes too difficult to manage with different values of reservation utility across multiple segments; and (d) introducing products in a non-monotone order of quality can improve profit from simultaneous introduction when the value of reservation utility of a middle segment is particularly high.     

Using clustering to improve sales forecasts in retail merchandising

Given sales forecasts for a set of items along with the standard deviation associated with each forecast, we propose a new method of combining forecasts using the concepts of clustering. Clusters of items are identified based on the similarity in their sales forecasts and then a common forecast is computed for each cluster of items. On a real dataset from a national retail chain we have found that the proposed method of combining forecasts produces significantly better sales forecasts than either the individual forecasts (forecasts without combining) or an alternate method of using a single combined forecast for all items in a product line sold by this retailer.     

一种使用在线评论信息的商品购买决策分析方法

在线评论信息对消费者的商品购买决策影响非常显著,如何使用数据体量较大的在线评论信息来进行有针对性的商品购买决策分析是近年来尤为需要关注的一个新研究内容。本文提出一种使用在线评论信息的商品购买决策分析方法。首先,通过在线评论信息的挖掘来确定了消费者所重点关注的关于候选商品的商品特征及其权重;然后,通过消费者情感的分析来构建了关于各候选商品的商品购买决策矩阵;在此基础上,通过给出的一种基于随机TOPSIS的方案排序方法来进行了各候选商品的排序。最后,依据携程网提供的关于三家客栈的在线评论信息进行了数据实验,从而说明了本文提出方法的实用性与可行性。     

Estimating VAR models for the term structure of interest rates

In this paper we follow the work of Evans and Marshall and propose new approaches for modelling the joint development of macro variables and the returns of government bond yields of several maturities. The models are estimated and compared with other forecasting schemes previously proposed in the literature, especially those relying on univariate, VAR and error correction methods. The models are then used to judge the hypothesis that the information content of macro variables and the term structure of interest rates as a whole help improving forecasting performance. Our main conclusion is quite simple: if one is interested in computing short-term forecasts, then there is no significant improvement in incorporating information other than the one already present in past observations of the yield at hand; however, if one worries about long-term forecasts (which is frequently the case with pension insurance companies), then the information content of macro variables and the term structure can improve forecasting performance.     

Statistical post-processing of forecasts for extremes using bivariate brown-resnick processes with an application to wind gusts

To improve the forecasts of weather extremes, we propose a joint spatial model for the observations and the forecasts, based on a bivariate Brown-Resnick process. As the class of stationary bivariate Brown-Resnick processes is fully characterized by the class of pseudo cross-variograms, we contribute to the theorical understanding of pseudo cross-variograms refining the knowledge of the asymptotic behaviour of all their components and introducing a parsimonious, but flexible parametric model. Both findings are of interest in classical geostatistics on their own. The proposed model is applied to real observation and forecast data for extreme wind gusts at 119 stations in Northern Germany.     

Additive aggregation with variable interdependent parameters: the VIP analysis software

We consider the aggregation of multicriteria performances by means of an additive value function under imprecise information. The problem addressed here is the way an analysis may be conducted when the decision makers are not able to (or do not wish to) fix precise values for the importance parameters. These parameters can be seen as interdependent variables that may take several values subject to constraints. Firstly, we briefly classify some existing approaches to deal with this problem. We argue that they complement each other, each one having its merits and shortcomings. Then, we present a new decision support software—VIP analysis—which incorporates approaches belonging to different classes. It proposes a methodology of analysis based on the progressive reduction of the number of alternatives, introducing a concept of tolerance that lets the decision makers use some of the approaches in a more flexible manner.     

技术不确定环境下新产品策略的设计应用

技术的动态发展和企业间的竞争对企业新产品策略有很大影响,直接决定新产品的引进周期。本文在产业技术动态变化的随机环境下构建随机动态规划模型,关注产业技术进步、投资成本和产品市场竞争等影响因素,探讨企业进行新产品引进的周期选择,对新产品引进的周期和质量决策进行方法设计和应用举例。利用随机动态规划模型得出新产品引进的最优时间周期,用算例分析技术进步和产品研发成本对企业引进周期策略的影响,采取策略迭代的方法进行求解,发现技术进步较快时企业的新产品引进步伐也较快,研发成本的提高使企业的新产品引入步伐降低。     

Short-run electricity load forecasting with combinations of stationary wavelet transforms

Short-term forecasting of electricity load is an essential issue for the management of power systems and for energy trading. Specific modeling approaches are needed given the strong seasonality and volatility in load data. In this paper, we investigate the benefit of combining stationary wavelet transforms to produce one day-ahead forecasts of half-hourly electric load in France. First, we assess the advantage of decomposing the aggregate load into several subseries with a wavelet transform. Each component is predicted separately and aggregated to get the final forecast. One innovation of this paper is to propose several approaches to deal with the boundary problem which is particularly detrimental in electricity load forecasting. Second, we examine the benefit of combining forecasts over individual models. An extensive out-of-sample evaluation shows that a careful treatment of the border effect is required in the multiresolution analysis. Combinations including the wavelet predictions provide the most accurate forecasts. This result is valid with several assumptions about the forecast error in temperature and for different types of hours (peak, normal, off-peak), different days of the week and various forecasting periods.     

Optimal procurement strategies for online spot markets

Spot markets have emerged for a broad range of commodities, and companies have started to use them in addition to their traditional, long-term procurement contracts (forward contracts). In comparison to forward contracts, spot markets offer products at essentially negligible lead time, but typically command a higher expected price for this added flexibility while also exhibiting substantial price uncertainty. In our research, we analyze the resulting procurement challenge and quantify the benefits of using spot markets from a supply chain perspective. We develop and solve mathematical models that determine the optimal order quantity to purchase via forward contracts and the optimal quantity to purchase via spot markets. We analyze the most general situation where commodities can be both bought and sold via a spot market and derive closed-form results for this case. We compare the obtained results to the reference scenario of pure contract sourcing and we include results for situations where the use of spot markets is restricted to either buying or selling only. Our approaches can be used by decision makers to determine optimal procurement strategies based on key parameters such as, demand and spot price volatilities, correlation between demand and spot prices, and risk aversion. The results of our analysis demonstrate that significant profit improvements can be achieved if a moderate fraction of the commodity demand is procured via spot markets. The results also show that companies who use spot markets can offer a higher expected service level, but that they might experience a higher variability in profits than companies who do not use spot markets. We illustrate our analytical results with numerical examples throughout the paper.     

Brand positioning under lexicographic choice rules

This paper discusses a brand positioning model in which two brands of a product are to be positioned in a price-quality space under a new behavioral assumption. This assumption asserts that customers determine the highest-quality product within their reservation price and purchase it, provided its quality does not fall short of a minimum standard. The model also includes producers' costs that are incurred for delivering a certain quality. We first delineate reaction functions for the optimal location of one brand, give a location of its competitor. We then show that Nash equilibria do not exist as long as price and quality are both variable. Finally, we consider a two phase model: in the first phase, the duopolists sequentially choose their quality levels under the assumption that both competitors know that in the second phase, a Nash equilibrium in prices follows. Single-variable mathematical programming formulations are presented to solve the problem. A numerical example is also given to illustrate the working of the model.     

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.     

信息不对称条件下竞价排名机制的信息匹配效率研究

本文聚焦消费者依赖搜索引擎平台的关键词检索实施购买商品行为的现象,尝试将消费者购买策略引入搜索平台竞价排名机制,构造内嵌搜索引擎平台、销售商、消费者三方主体的博弈模型,考察在互联网信息不对称条件下,竞价排名机制的信息匹配效率,阐释产品质量信号改善竞价排名机制信息匹配效率的理论机理。研究发现,在信息不对称及无产品质量信号机制的条件下,竞价排名均衡结果完全不能匹配消费者的购买策略,致使消费者福利因销售商逆向选择而遭受双重损失;植入产品质量信号后,搜索引擎平台能够通过识别销售商自带的产品质量信号,提高竞价排名机制的均衡结果与消费者购买策略的匹配度,进而部分地改善竞价排名机制的信息匹配效率。     

Experts' adjustment to model-based SKU-level forecasts: does the forecast horizon matter?

Experts (managers) may have domain-specific knowledge that is not included in a statistical model and that can improve short-run and long-run forecasts of SKU-level sales data. While one-step-ahead forecasts address the conditional mean of the variable, model-based forecasts for longer horizons have a tendency to convert to the unconditional mean of a time series variable. Analysing a large database concerning pharmaceutical sales forecasts for various products and adjusted by a range of experts, we examine whether the forecast horizon has an impact on what experts do and on how good they are once they adjust model-based forecasts. For this, we use regression-based methods and we obtain five innovative results. First, all horizons experience managerial intervention of forecasts. Second, the horizon that is most relevant to the managers shows greater overweighting of the expert adjustment. Third, for all horizons the expert adjusted forecasts have less accuracy than pure model-based forecasts, with distant horizons having the least deterioration. Fourth, when expert-adjusted forecasts are significantly better, they are best at those distant horizons. Fifth, when expert adjustment is down-weighted, expert forecast accuracy increases.     

管理层业绩预告会影响定增价格吗？——基于中介效应的视角

本文研究上市公司是否以及如何利用业绩预告根据定增对象的不同操纵新股发行价格。实证结果表明,管理层会根据定增对象的不同,策略性地发布信息含量不一样的业绩预告以影响定增新股发行价,业绩预告在定增对象与发行价格之间发挥了显著的中介效应。具体来说,若大股东参与认购新股,则倾向于发布坏消息的业绩预告,以压低新股发行价,若大股东不参与认购新股,则倾向于发布好消息的业绩预告,以抬高新股发行价;进一步分类分析发现,非国有上市公司比国有上市公司、主动披露业绩预告的上市公司比强制披露业绩的上市公司这种行为表现更为强烈,业绩预告的中介效应也更为显著。研究结论为投资者的投资决策和监管部门的监管活动提供了一定的参考价值。     

Full-line or specialization strategy? The negative effect of product variety on product line strategy

This paper studies the product line decisions of firms under two consumer segments differing in their quality-sensitivity. We focus on a negative impact of the product variety on the consumers’ motivation to purchase, while each product is horizontally differentiated. In the presence of this impact and high fixed costs relative to variable costs, it is shown that when a highly quality-sensitive segment exists, it is always advantageous for the monopoly to specialize in only one product serving this segment. However, the appearance of a competitor can drastically change the product line in the market. Under the duopolistic setting where two firms sequentially determine their product lines, we show that the leader gains a better financial result by offering its product to the low segment for many cases, including the case where no product is offered to the high segment by either of the firms who are in equilibrium. Furthermore, we obtain another interesting result that indicates that the follower’s profit can exceed the leader’s profit when the quality-sensitivities between the two consumer segments are sufficiently different, even though the two firms are symmetric except for the order of their product offerings.     

搜寻成本下可替代产品的定价策略与库存决策

为探寻存在搜寻成本情况下消费者购买可替代产品时的定价与库存问题,从消费者效用出发,对厂商收益构建了基于马尔可夫决策过程的优化模型。在消费者方面,分析了其购买与继续搜寻的条件,并分别在搜寻成本不变和搜寻成本边际递减的情况下研究了消费者保留效用的变化情况以及购买相应产品的概率。此外,与很多相关文献不同的是,由于搜寻成本的存在,该情形下的消费者并不一定会在完成购买之前搜寻完所有的产品。在厂商方面,根据实际情况构建不同搜寻顺序下的收益模型并求解出最优定价策略与库存策略,并将定价模型与库存策略扩展到了动态的环境,为厂商制定价格及库存方案提供相应的决策支持。     

非参数假设检验在证券投资分析中的应用

通过引入一种新的估计方法——非参数假设检验方法,以达到对证券投资咨询机构对证券市场大盘走势预测准确度的估计.通过对估计结果的分析得出结论,有99%的把握认为,中国证券市场投资咨询机构所提供的对大盘涨跌的预测,每次有一半家数正确的概率没有达到45%,因而投资者应慎重对待投资咨询机构的大盘预测.