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.