Optimal investment under operational flexibility, risk aversion, and uncertainty

Traditional real options analysis addresses the problem of investment under uncertainty assuming a risk-neutral decision maker and complete markets. In reality, however, decision makers are often risk averse and markets are incomplete. We confirm that risk aversion lowers the probability of investment and demonstrate how this effect can be mitigated by incorporating operational flexibility in the form of embedded suspension and resumption options. Although such options facilitate investment, we find that the likelihood of investing is still lower compared to the risk-neutral case. Risk aversion also increases the likelihood that the project will be abandoned, although this effect is less pronounced. Finally, we illustrate the impact of risk aversion on the optimal suspension and resumption thresholds and the interaction among risk aversion, volatility, and optimal decision thresholds under complete operational flexibility.     

A survey of decision support system applications (1988–1994)

To extend a previous survey of specific decision support system (DSS) applications over the period (January 1971–April 1988), we have conducted a follow-up survey of DSS applications published between May 1988 and December 1994. Two hundred seventy-one published applications are identified. This survey reveals that there appear to be more creative applications of optimisation and suggestion model-based DSS than simulation-based applications. This is evidenced by a proportional increase of optimisation and suggestion models and a decrease of representation models. Moreover, group decision support systems, executive support systems, and knowledge-based systems applications are becoming more prevalent in many organisations. Although management science (MS)/operational research (OR) models continue to play critical roles, there is a clear observable trend in the DSS model area that three non-MS/OR tools are emerging as powerful DSS tools: graphics, artificial intelligence, and visual interactive modeling.     

新兴产业两阶段期权规划决策模型

新兴产业中处于横向竞争地位的初创企业,会呈现渐变和突变两种不同的演化状态。不同状态下的横向竞争企业,价值变化都具有高度的不确定性。为了降低投资决策的不确定性风险,获取稳定的投资收益,对处于渐变和突变状态下的初创企业,首先运用期权组合的方法,进行第一阶段的变量预估决策;然后依据期权投资的收益情况,再运用线性规划技术进行第二阶段的补偿优化决策。论文通过阿里巴巴、京东、苹果和诺基亚四家样本公司的数据,检验了两阶段期权规划决策模型的实际效果,结论显示该方法能降低新兴产业投资中的不确定性干扰,在获取稳定收益的同时,使风险处于可控状态。     

An approximation algorithm for the location of dairy enterprises under time constraints

The facility location problem constitutes a major part of the strategic planning of a dairy enterprise. The decision making process for such a long term investment is rather complicated, with many of the issues involved belonging to the NP – Hard class of algorithms and therefore the challenge for operational researchers who attempt to mathematically model the problem is significant. A number of models have appeared in the last few years in an attempt to find a solution to the problem. This paper introduces a model pertaining to saturated and competitive markets, which include a high investment risk as well as a small margin for incorrect decisions of the newcomers. Time is a crucial element, especially for sensitive products with a limited life span cycle. An approximation algorithm is used and the solution is then compared with the optimal one. Emphasis was given to the running time of the software program, both in the designing and the implementation of the algorithm.     

Expert Systems and Operational Research-Mutual Benefits

The aim of much operational research and expert systems work is similar-helping a decision maker do his job. Liaison will produce mutual benefits. Expert systems development will benefit from the experience of operational research. The operational research community is well placed to support and develop the use of such systems by management.     

The Validation of Expert Systems — Contrasts with Traditional Methods

Recent research has demonstrated that little validation is carried out during and after the building of operational research models for decision support. In a subject area that has been of great interest for more than a decade, this reflects the fact that validation is not yet firmly embedded into the methodology of mathematical modelling that underpins conventional decision support systems. With the interest now being shown in expert systems, it is important to devise an appropriate methodology of validation for such systems. This paper contributes to this development by describing the types of validation that should be performed with decision support systems, and compares and contrasts the features found in the validation of mathematical models with those required of validation of expert systems.     

市场风险值VaR的算法与应用

进行金融风险管理时可以将风险划分为四类，即信用风险、经营风险、流动性风险和市场风险。其中市场风险是指金融市场价格（包括股票价格、利率、汇率和大宗可交易商品的价格）波动而引起的未来收益的不确定性。市场风险值VaR(Value at Risk）就是用来评价给定资产所面临的市场风险大小。本文介绍了VaR的定义、相关的计算方法和在证券投资决策中的应用实例。     

An analytical study of a swapping operation in the money market

Investors in money markets have found a class of transaction in which return from investment can be increased without increasing the risk taken. We formally analyse one of these operations (usually referred to as a ‘swap’) and obtain the condition under which it is possible to obtain a swap profit.From this condition, an indicator is obtained to aid the decision of when to engage a swap. The indicator evidences the role played by the spread between yield rates, time to maturity and timing, in a swap.Finally the indicator is used to derive some mathematical properties of the swapping operation. We show that a trend toward widening spreads is neither a necessary nor a sufficient condition for a swap-profit to be obtained. We give examples using hard data to support our theoretical results.     

Investment Decisions under Uncertainty and Variability — Some Practical Experiences of Using Forecasts and Probabilities

This paper illustrates by three practical examples the use of forecasts and probabilities in making actual investment decisions. In each case the use of a simple forecast would lead to a wrong decision. The emphasis throughout is on the interaction between management and the operational research scientist. The three examples discussed in the paper are: (a) Study of future production capacity, where future demand is uncertain; (b) Study of storage facilities, where demand is variable and there is a risk of losing custom; (c) Study of future pipe-line capacity, where throughput is variable and there was uncertainty when to invest. The paper concludes with a discussion on the effects of variability, uncertainty and risk on the decision criterion, and on the decisions made.     

在线网络借贷投资决策模型及实证研究

P2P网络借贷作为电子商务在金融领域的延伸与应用,近年来得到广大学者的关注.但是目前的理论研究中,鲜有从投资者信息挖掘的角度进行投资决策分析.本文提出一个新颖的方法,即投资者构成分析方法,通过分析贷款的众多投资者信息遴选出最有价值的投资,辅助投资者进行投资决策.首先从投资者的历史投资收益率、风险偏好以及投资经验三个维度构建投资者档案(investor profile),进而基于投资者档案构建投资者构成分析模型,最后通过美国最大的在线网络借贷网站Prosper的数据,对本文提出的构想及模型进行了实证研究.实验结果表明本文提出的利用投资者构成分析的方法辅助投资者进行投资决策是可行的,文中构建的模型表现出良好的预测能力,能够有效地筛选出有价值的投资.     

“政策市”下的中国企业实物投资机理研究

在我国市场经济发展的过程中,某些具有不确定性和投资不可逆性的产业市场容易出现投资过热问题.这时政府往往频繁出台一系列宏观调控政策以规范市场,但效果不佳.为此从研究该类市场的投资主体—企业的投资决策机理的角度出发,通过引入实物期权的理论,给出了在完善和平稳发展的市场条件下及在政府政策冲击的市场条件下,价格分别服从单纯的几何布朗运动及混合的几何布朗运动/泊松跳跃过程的企业最佳投资规则及其临界价格,并进行了比较,结果表明:“政策市”下的企业投资更富“冒进性”.     

Modelling locational price spreads in competitive electricity markets; applications for transmission rights valuation and replication

The restructuring of the electric utilities industry has forcedindustry participants to rethink their approach to a numberof decision processes. To manage risk and plan investment ingeneration assets, as well as to examine the efficient expansionof the current transmission grid, one needs to have a clearunderstanding of the interaction between the grid propertiesand the behaviour of the regional power markets. In this paperwe discuss a fundamental modelling approach which extracts thestochastic properties of electricity prices by modelling theimpact of physical and economic drivers affecting the production,delivery, and consumption of electricity. If the fundamentalinputs are directly observable, we can use historical data tocalibrate the model parameters. In the case of electricity,this simple and abundant set of training data can make a crucialdifference. We present the bid-based stochastic model (BSM) and look intoits application to valuing of financial derivatives, especiallyoptions based on the locational spread in electricity pricebetween two markets. The advantage of the bid-based model isthat one is able to link the capacity of the transmission line,in megawatts, directly to the correlation between electricityprices at the end nodes. This leads us to a valuation methodfor a locational spread option, the financial equivalent ofa physical transmission right. The model represents an improvementover standard spread option formulation in that it accountsfor the effect of the nonlinear flows in the transmission networkon the correlation and distribution of locational prices. Wealso address the question of whether financial transmissionrights can be replicated with a dynamic portfolio of forwardcontracts at the end nodes. This poses the possibility of model-based arbitrage betweenexisting forward markets and the emerging transmission rightsmarkets. Furthermore, it allows users to simulate the effectof transmission outages or expansion. For example, a for-profittransmission provider who is contemplating addition of a newtransmission line between two markets needs to know whetherhe will be able to recover the fixed cost of investing in theline by selling transmission rights to market participants.By calibrating the bid-based model according to current pricelevels and adding the capacity of the new transmission line,the transmission owner can simulate future cash flows and estimatethe profitability of the investment.     

Risk analysis and decision theory: A bridge

The risk-triplet approach pioneered by Kaplan and Garrick is the keystone of operational risk analysis. We perform a sharp embedding of the elements of this framework into the one of formal decision theory, which is mainly concerned with the methodological and modeling issues of decision making. The aim of this exercise is twofold: on the one hand, it gives operational risk analysis a direct access to the rich toolbox that decision theory has developed, in the last decades, in order to deal with complex layers of uncertainty; on the other, it exposes decision theory to the challenges of operational risk analysis, thus providing it with broader scope and new stimuli.     

Long term capacity decisions in uncertain markets for advanced manufacturing systems incorporating scope economies

By examining the literature in the field of manufacturing flexibility many researchers have located several form of flexibility by distinguishing them into strategic and operational flexibility forms. Furthermore, many measures and design methods have been proposed for flexible manufacturing systems. Surely scope economies are one of the most important strategic form of flexibility in manufacturing systems and their strategic impact has been acknowledged by the very beginning of the flexible manufacturing era. However, despite several researches are available to address scope economies measurement and design methodologies, very few researches investigate their economic convenience in front of dedicated manufacturing systems depending on the competitive market conditions.This paper proposes a theoretical model whose main aim is the general understanding of the convenience conditions of scope economies-based manufacturing systems. The results of the theoretical model are very interesting, because they locate market conditions that make scope economies manufacturing systems less profitable than dedicated manufacturing ones; moreover, the proposed model sets some general criteria to guide the entrepreneur in making right investment decision regarding this kind of manufacturing investments. Such results can explain the reason of many failures of flexible manufacturing systems and it suggests the use of this kind of approach to investigate other flexibility forms of manufacturing systems. The ultimate goal of this research is the construction of a Decision Support System for supporting the entrepreneur in making decision on Advanced Manufacturing Systems investment decisions.     

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes.     

Scenario-based portfolio selection of investment projects with incomplete probability and utility information

In the selection of investment projects, it is important to account for exogenous uncertainties (such as macroeconomic developments) which may impact the performance of projects. These uncertainties can be addressed by examining how the projects perform across several scenarios; but it may be difficult to assign well-founded probabilities to such scenarios, or to characterize the decision makers’ risk preferences through a uniquely defined utility function. Motivated by these considerations, we develop a portfolio selection framework which (i) uses set inclusion to capture incomplete information about scenario probabilities and utility functions, (ii) identifies all the non-dominated project portfolios in view of this information, and (iii) offers decision support for rejection and selection of projects. The proposed framework enables interactive decision support processes where the implications of additional probability and utility information or further risk constraints are shown in terms of corresponding decision recommendations.     

风险调整收益及其在投资决策中的应用——对均值—方差决策准则一点补充

指出传统的均值 方差决策规则在度量风险方面的不足,认为风险与投资收益的偏差有关;指出了一种新的风险度量指标———风险组合偏差,同时提出了一种能够根据风险大小进行投资的决策指标———风险调整收益,并举例说明了二者在分析投资项目时的应用。     

风险调整收益及其在投资决策中的应用--对均值-方差决策准则一点补充

指出传统的均值一方差决策规则在度量风险方面的不足，认为风险与投资收益的偏差有关；指出了一种新的风险度量指标——风险组合偏差，同时提出了一种能够根据风险大小进行投资的决策指标——风险调整收益，并举例说明了二者在分析投资项目时的应用。     

A fuzzy approach to using expert knowledge for tuning paper machines

Paper machines are very complex production systems, but their scope is simple: they consume materials and resources, called factors, to produce paper, which in turn can be described by its characteristics. In this paper, a decision support system is developed in cooperation with an industrial partner to help them with operational decision making when tuning a paper machine. The decision support system was developed in two phases. Firstly, the knowledge of experts is collected and stored in the form of a fuzzy ontology. Secondly, this knowledge is made usable so that a user of the decision support system can specify what characteristics of the produced paper to increase or to decrease and be returned with a recommendation on what factors to change. In this paper, we will work out the optimization problems on which the system is based. Additionally to a basic goal programming model, two extensions are explored, accounting for uncertainty and non-linearity, respectively.     

Planning performance based contracts considering reliability and uncertain system usage

This paper investigates a novel quantitative approach for planning and contracting performance-based logistics in the presence of uncertain system usage. Our efforts focus on an integrated service delivery environment where the manufacturer develops capital-intensive systems and also provides after-sales support. We propose an analytical model to characterize system operational availability by comprehending five performance drivers: inherent failure rate, usage rate, spare parts inventory, repair time, and the fleet size. This analytical insight into the system performance allows the service supplier to minimize the total cost across system design, production, maintenance, and repair. Two contracting schemes are investigated under cost minimization and profit maximization schemes. For the first time in literature, reliability design and service parts logistics are seamlessly integrated into one decision support model for improving operational availability while lowering the lifecycle cost. Numerical examples are provided to demonstrate the applicability and the effectiveness of the proposed decision support tool.