Some Extensions of Domain Criteria in Decision Making under Uncertainty*

This paper examines some approaches to decision problems under uncertainty. Starr's [29] domain criterion is presented and modified to take into account different philosophies concerning the desirability of winning versus the importance of avoiding losses. The concept of expected value of distribution information is defined and its computation is illustrated with a numerical example. Target values are then introduced into the model and a parametric procedure is used to maximize the chances of achieving a certain level of the given objective. Finally, we show how the concepts developed in this paper might provide further insight into some decision situations reported in the literature.     

A Decision-Theoretic Valuation of Information in Sealed-Bid Auctions for Items of Known Value

This paper considers the question of how much time and effort should be spent in preparing a bid for a single item of known value sold at a first-price sealed-bid auction. A decision-theoretic approach to this bid decision summarizes the decision maker's knowledge of the competitive environment through his or her subjective probability distribution of the highest competing bid. Research activities such as collecting and analyzing bid histories are efforts to obtain additional information that reduces the uncertainty in the highest competing bid. The decision-theoretic concepts of expected value of perfect and imperfect information are used to place an economic value on such research activities. The results presented allow the decision maker to quantify the expected value of imperfect information when the uncertainty is normally distributed. The results show that additional research is most valuable prior to auctions the bidder expects to win.     

Insignificant Betas and the Efficacy of the Sharpe Diagonal Model for Portfolio Selection*

When both practitioners and theorists apply Sharpe's diagonal model [15] to simplify the portfolio selection problem, they assume that the entire covariation structure of each stock (i.e., with all other stocks) is captured in that stock's covariance with the market (or β). Furthermore, it is well known that the selection algorithm itself has a marked tendency to select stocks with the lowest βs, ceteris paribus. When a stock's β is statistically indistinguishable from zero, it is an empirical issue whether the market model is (a) less appropriate for that particular stock relative to those with statistically significant βs; or is (b) a viable model in that the covariance of this stock's rate-of-return with all other stocks' rates-of-return vanishes. The objective of this paper is to distinguish empirically between (a) and (b), and to propose a heuristic which will improve the ex-post performance of the diagonal model. The possible benefits of this heuristic are also demonstrated in a rigorous statistical framework.     

A Comprehensive Model for Managing Credit Risk on Home Mortgage Portfolios*

Managing credit risk in financial institutions requires the ability to forecast aggregate losses on existing loans, predict the length of time that loans will be on the books before prepayment or default, analyze the expected performance of particular segments in the existing portfolio, and project payment patterns of new loans. Described in this paper are tools created for these functions in a large California financial institution. A forecasting model with Markovian structure and nonstationary transition probabilities is used to model the life of a mortgage. Logistic and regression models are used to estimate severity of losses. These models are integrated into a system that allows analysts and managers to depict the expected performance of individual loans and portfolio segments under different economic scenarios. With this information, analysts and managers can establish appropriate loss reserves, suggest pricing differentials to compensate for risk, and make strategic lending decisions.     

Does IO Economics Really Make the Case for Investments in IT: Comment on West and Courtney

West and Courtney [18] rely on industrial organization economics in general, and the economics of uncertainty in particular, for insights into the benefits of overcoming information constraints through information systems (IS) and improved information technologies (IT). This Comment argues that the IS researcher who looks to the economics of uncertainty to make a case for improved IT will only find support from a selective reading of a constantly expanding literature whose aims and inferences are easily misunderstood.     

The Development Manager's Advisory System: A Knowledge-Based DSS Tool for Project Assessment*

This research investigates whether the knowledge-based decision support system (KBDSS) paradigm provides the necessary supporting structure and developmental framework for product development evaluation. To address the research questions posed in this study, it is necessary to develop and implement KBDSS's at specific decision points along the product development cycle. This paper describes the design, development, and implementation of a KBDSS to support a product development manager's decision concerning full-scale development of a new product. From the systems design perspective, this paper addresses the integration and innovative use of a variety of techniques for knowledge acquisition, modeling, and processing. The approach utilized obtains the benefits of normative modeling as well as the flexibility and developmental advantages of knowledge-based systems. Since its implementation, the system has been successfully used by a development manager to support his recommendation for an ongoing project. His complete satisfaction with this system served as the impetus for the design and development of a multi-expert system which was implemented at the strategic level.     

Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative Methods

In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables. We investigate four types of models: univariate‐linear, multivariate‐linear, univariate‐neural network, and multivariate‐neural network using a sample of 283 firms spanning 41 industries. This study shows that the application of the neural network approach incorporating fundamental accounting variables results in forecasts that are more accurate than linear forecasting models. The results also reveal limitations of the forecasting capacity of investors in the security market when compared to neural network models.     

中国制造业创新技术进步要素偏向及其影响因素研究

现有研究对创新技术进步要素偏向关注甚少。本文基于超越对数生产函数的随机前沿分析方法测算了中国制造业部门创新技术进步的要素偏向指数,进而通过系统广义矩估计方法考察了创新要素结构、科研人员工资扭曲等因素对创新技术进步资本偏向的影响效果,我们发现：创新资本的产出弹性远远大于科研人员的产出弹性,制造业创新技术进步整体偏向于资本,且具有明显的路径依赖特征;创新投入要素结构的资本化程度、政府资助行为削弱了创新技术进步的资本偏向;科研人员工资扭曲、企业自有资金对创新活动的支持强化了资本偏向。优化创新方向、提高科研人员工资待遇对国家创新战略的实施至关重要。     

中小企业贷款信用评价研究

面对大量中小企业的贷款需求,商业银行首先需要按照一定的标准进行初步筛选,直接淘汰那些不符合自身要求的中小企业。对于经过初步筛选保留下来的中小企业,则进行信用评价。利用建立的中小企业信用评价指标和因子分析评价模型,可以将参加信用评价的中小企业分为两类,一类可以直接予以贷款,另一类则暂时不予贷款。