A dynamic bank portfolio planning model with multiple scenarios,multiple goals and changing priorities

The paper discusses a practical application of a two-stage linear goal programming model to the management of the domestic and foreign currency denominated assets and liabilities of a large bank in Finland. The planning horizon includes three one-year planning periods. A number of alternative scenarios are used to describe uncertainty concerning future developments in these periods. The scenarios are related to general economic conditions as well as the state of the domestic and foreign financial markets. The bank is assumed to have multiple conflicting goals with different and changing priorities. The goals deal with expected profits, risk, liquidity, capital adequacy, growth, customer relationships and several other aspects of the bank's operations.     

A fuzzy goal programming approach to integrated loading and scheduling of a batch processing machine

This paper concerns a real-life problem of loading and scheduling a batch-processing machine. The integrated loading and scheduling problem is stated as a multicriteria optimization problem where different types of objectives are included: (1) short-term objectives of relevance to the shop floor, such as throughput maximization and work-in-process inventory minimization, and (2) long-term objectives such as balancing of end product inventory levels and meeting financial targets imposed by the higher production planning level. Two types of uncertainty are considered: (1) uncertainty inherent in loading and scheduling objective targets (goals) such as the allocated budget and end product demand, and (2) uncertainty in importance relations among the objectives. These two types of uncertainty are modelled using fuzzy sets and fuzzy relations, respectively. A fuzzy goal programming model and the corresponding method are developed which handle both fuzzy and crisp goals and fuzzy importance relations among the goals. Numerical examples are given to illustrate the effectiveness of the developed model.     

Short-term financial planning with uncertain receipts and disbursements

The problem of short-term financial planning is to determine an optimal credit mix to meet the short-term cash needs and an optimal investment plan for excess cash. A number of linear optimization models have been developed to solve this problem, some of which are in practical use. The purpose of this paper is to generalize the assumptions of these models concerning the available information about future receipts and disbursements. It is presupposed that the financial officer has some idea as to the amount involved which, however, cannot be specified by a probability distribution. On the contrary, we assume that these ideas only permit qualitative probability statements such as the following:“That the difference between disbursements and receipts in a certain period lies in an interval I₁ is no less probable than that it lies in an interval I₂”.For this level of information we formulate a model for short-term financial planning, and we develop a solution procedure to determine the optimum financial alternatives. Finally, the entire procedure is demonstrated by a medium sized example.     

The municipal financial planning model: A simultaneous regression equations and goal programming approach

Most of today's city managers are concerned about municipal financial problems. In trying to resolve these problems, scientific planning tools are needed to examine the optimality of resource allocation. For the municipal financial policy planners, the following two points are important.

• 1.(1) Statistical aspects. Since there are many economic variables in municipal financial problems, it is necessary to clarify the relationship among these variables and to infer the parameters through a statistical approach.
• 2.(2) Mathematical aspects. Policy-planners must specify the optimal value of these variables so as to attain the multiple goals of a local government.

Econometric models, especially the simultaneous equations approach, are appropriate for statistical analysis; whereas a goal-programming formulation may be used for mathematical aspects of the problem.In this paper, we propose and show that these two models can be combined. We call this the GPE model.The GPE model is applied to Urawa City. The Urawa model is composed of 6 structural equations 9 variables and 4 scenarios from the standpoint of future insight of Urawa City.From the Urawa Case, we conclude that the GPE model may be a practical tool for the municipal financial planning of other local governments.     

Closed-loop supply chain network design: A financial approach

The more common approaches used in the SCM consider only the physical logistic operations and ignore the financial aspects of the chain. This paper presents a financial approach to model a closed-loop supply chain design in which financial aspects are explicitly considered as exogenous variables. The model decides to determine the strategic decisions as well as the tactical decisions. The main contribution of this paper is to incorporate the financial aspects (i.e. current and fixed assets and liabilities) and a set of budgetary constraints representing balances of cash, debt, securities, payment delays, and discounts in the supply chain planning. Moreover, the financial approach applies the change in equity (instead of the measure of profit/cost in traditional approaches) as the objective function to be optimized in the presented model.To show the advantages of the presented approach, the results attributed to the financial approach and the traditional approach are compared, where the latter firstly decides on operations and fits finances afterwards. The results indicate that the traditional approach leads to lower change in equity compared to the financial approach. This fact illustrates the inadequacy of treating process operations and finances in isolated environments and pursuing as objective myopic performance indicators such as profit or cost. Moreover, a sensitivity analysis of the parameters using ANOVA for different levels of the parameters under different customer order patterns is performed to enhance the managerial insights of the study. The results clearly reveal the better improvement of using the financial approach over the traditional approach, and convince the decision makers to take advantage of the proposed approach.     

A Multi-criteria Approach to Formulating International Project-financing Strategies

Despite their commercial feasibility, many international projects have failed primarily because of poor financial planning. When formulating a project-financing strategy, financial managers of multinational corporations must consider a variety of conflicting objectives. The existence of conflicting goals makes the design of international project-financing strategies very difficult. A goal programming model is presented as an aid to financial managers for analysing trade-offs among conflicting financing goals and identifying the best multi-source financing package for major international projects.     

Introduction to financial optimization: Mathematical Programming Special Issue

Optimization models are effective for solving significant problems in finance, including long-term financial planning and other portfolio problems. Prominent examples include: asset-liability management for pension plans and insurance companies, integrated risk management for intermediaries, and long-term planning for individuals. Several applications will be briefly mentioned. Three distinct approaches are available for solving multi-stage financial optimization models: 1) dynamic stochastic control, 2) stochastic programming, and 3) optimizing a stochastic simulation model. We briefly review the pros and cons of these approaches, discuss further applications of financial optimization, and conclude with topics for future research. Published online December 15, 2000     

Exact methods for large-scale multi-period financial planning problems

A relevant financial planning problem is the periodical rebalance of a portfolio of assets such that the portfolio’s total value exhibits certain characteristics. This problem can be modelled using a transition graph G to represent the future state space evolution of the corresponding economy and mathematically formulated as a linear programming problem. We present two different mathematical formulations of the problem. The first considers explicitly the set of the possible scenarios (scenario-based approach), while the second considers implicitly the whole set of scenarios provided by the graph G (graph-based approach). Unfortunately, for both the formulations the size of the corresponding linear programs can be huge even for simple financial problems. However, the graph-based approach seems to be a more powerful model, since it allows to consider a huge number of scenarios in a very compact formulation. The purpose of this paper is to present both heuristic and exact methods for the solution of large-scale multi-period financial planning problems using the graph-based model. In particular, in this paper we propose lower and upper bounds and three exact methods based on column, row and column/row generation, respectively. Since the methods based on column/row generation exploits simultaneously both the primal and the dual structure of the problem we call it Criss-Cross generation method. Computational results are given to prove the effectiveness of the proposed methods.      

Comparing the adaptive control methodology (ACM) to the financial planning practice of a large international group

The present paper is a contribution to the validation of the newly developed methodological framework called “adaptive control methodology” (ACM) introduced in a previous paper. ACM is a tool for planning and on-line control of complex socio-economic processes. It combines the techniques of adaptive control theory with system dynamics (SD) and group multicriteria decision aid (GMCDA). In the following, the usefulness and practical use of this framework are set in perspective by comparing it with recurrent planning and control of financial processes in the BSN/DANONE group. As a result of this comparison, some lessons drawn on the way of practical implementation of the theoretical framework can be eased. As by-product recommendations are made on how to influence in a constructive way common planning processes in the real world.     

Simulating the strategic financial planning process

Strategic financial planning should (1) involve the key variables in the corporate growth process, (2) reflect the dynamic interaction in the system and (3) incorporate the dimension of uncertainty. Decision makers need a stochastic model that links the interaction between the investment and financing process for the planning period. The model presented in this paper integrates the investment and financing process by the use of simulation. The measure used to link these two systems is the rate of return required on new investment in order for decision makers to achieve their desired earnings-per-share growth goal. The model provides top management a tool to communicate their expectations to lower levels of management, thereby allowing them to measure and evaluate the impact of various sets of assumptions on the company's strategic plans.     

Assessing the impact of derived behavior information on customer attrition in the financial service industry

The value of the customer has been widely recognized in terms of financial planning and efficient resource allocation including the financial service industry. Previous studies have shown that directly observable information can be used in order to make reasonable predictions of customer attrition probabilities. However, these studies do not take full account of customer behavior information. In this paper, we demonstrate that efficient use of information can add value to financial services industry and improve the prediction of customer attrition. To achieve this, we apply an orthogonal polynomial approximation analysis to derive unobservable information, which is then used as explanatory variables in a probit–hazard rate model. Our results show that derived information can help our understanding of customer attrition behavior and give better predictions. We conclude that both researchers and the financial service industry should gather and use derived financial information in addition to directly observable information.     

Scheduling Personal Finances via Integer Programming

Personal financial planning is the preparation of a series of ongoing target-oriented decisions concerning personal assets, incomes and expenses. An important task of personal financial planning is the generation of a financial schedule defining suitable times for realization of the expenses and incomes at a given point of time. We describe an integer programming model and algorithms for scheduling personal finances via the examination of a linear combination of the weighted objectives to be maximized. Solving the integer programming problem provides an optimal financial schedule which reflects an individual’s goals and preferences. We describe a worst-case scenario for a personal financial schedule with respect to possible variation of credit and interest rates. Computational results demonstrate the largest problems that are solvable via the algorithm based on schedule enumeration which is needed for a stability analysis.     

The lifetime of a financial bubble

We combine both a mathematical analysis of financial bubbles and a statistical procedure for determining when a given stock is in a bubble, with an analysis of a large data set, in order to compute the empirical distribution of the lifetime of financial bubbles. We find that it follows a generalized gamma distribution, and we provide estimates for its parameters. We also perform goodness of fit tests, and we provide a derivation, within the context of bubbles, that explains why the generalized gamma distribution might be the natural one to expect for the lifetimes of financial bubbles.     

The co-evolution of integrated corporate financial networks and supply chain networks with insolvency risk

In this paper, we develop a modeling framework that integrates supply chain networks and corporate financial networks, and studies the co-evolution of the two types of networks. The framework allows one to investigate the following questions: (1) How do the physical product flows, demands and prices interplay with the corporate financial networks, and evolve over time with varying financial and economic conditions? (2) How do the financial flows, profits, and insolvency risks interplay with the supply chain networks, and evolve over time with varying financial and economic conditions? We illustrate the modeling framework with computational studies. In particular, the computational results demonstrate how the two types of networks interplay and evolve in a business environment similar to the one during the 2008–2009 financial crisis in which the credit markets tightened up and the product demands continued to decline.     

Forecasting call frequency at a financial services call centre

A forecasting model is developed for the number of daily applications for loans at a financial services telephone call centre. The purpose of the forecasts and the associated prediction intervals is to provide effective staffing policies within the call centre. The model building process is constrained by the availability of only 2 years and 7 months of data. The distinctive feature of the data is that demand is driven in the main by advertising. The analysis given focuses on applications stimulated by press advertising. Unlike previous analyses of broadly similar data, where ARIMA models were used, a model with a dynamic level, multiplicative calendar effects and a multiplicative advertising response is developed and shown to be effective.     

突发公共卫生事件对系统性金融风险的冲击及传染效应研究

首先基于面板向量自回归模型考察了突发公共卫生事件对系统性金融风险的冲击影响,接着综合考虑突发公共卫生事件的影响及其所导致的收益率的非对称性构建单指标非对称CoVaR模型,最后借助LASSO惩罚函数与局部估计法进行求解,以此构建有向网络分析金融机构间的传染效应.研究发现:(1)突发公共卫生事件冲击会使系统性金融风险水平短...     

Structure and dynamics of the global financial network

In this paper, we study the evolution of the network topology for the global financial market. We evaluate the level of diversification and participation of developed and emerging economies in cross-border exposures and find that the gross exposure network is dense, the vulnerability matrix is sparse, and the network’s fragility changes over time. Prior to the financial crisis in 2008, the network was relatively fragile, whereas it became more resilient afterwards, showing a reduction in financial institutions’ risk appetite. Our results suggest that financial regulators should track down the network evolution in their systemic risk assessment.     

Bayesian kernel based classification for financial distress detection

Corporate credit granting is a key commercial activity of financial institutions nowadays. A critical first step in the credit granting process usually involves a careful financial analysis of the creditworthiness of the potential client. Wrong decisions result either in foregoing valuable clients or, more severely, in substantial capital losses if the client subsequently defaults. It is thus of crucial importance to develop models that estimate the probability of corporate bankruptcy with a high degree of accuracy. Many studies focused on the use of financial ratios in linear statistical models, such as linear discriminant analysis and logistic regression. However, the obtained error rates are often high. In this paper, Least Squares Support Vector Machine (LS-SVM) classifiers, also known as kernel Fisher discriminant analysis, are applied within the Bayesian evidence framework in order to automatically infer and analyze the creditworthiness of potential corporate clients. The inferred posterior class probabilities of bankruptcy are then used to analyze the sensitivity of the classifier output with respect to the given inputs and to assist in the credit assignment decision making process. The suggested nonlinear kernel based classifiers yield better performances than linear discriminant analysis and logistic regression when applied to a real-life data set concerning commercial credit granting to mid-cap Belgian and Dutch firms.     

Effectiveness in strategic planning

There is a considerable body of literature on how to do strategic planning effectively. There is however no concise statement of effectiveness in strategic planning. One definition of an effective strategic planning system would be a system that sets goals and achieves them within a specified time. In this paper it is argued that this goals/achievement view of effectiveness has serious deficiencies. As an alternative to an ends oriented view of effectiveness, a process orientation is adopted. Twelve attributes of effectiveness for strategic planning systems are proposed.     

A sequential goal programming model with fuzzy hierarchies to sustainable and responsible portfolio selection problem

Sustainable and responsible (SR) investors have to address two criteria types: both financial ones and those pertaining to sustainability and social responsibility. We present a comfortable tool for SR investors that allow them to express their preferences at two levels: first, by comparing criteria of the same nature, and second, via the comparison between the two superior level criteria (the financial and the SR objectives). Owing to the difficulty involved in determining a precise preference between the conflicting objectives, we address this by goal programming with fuzzy hierarchies (GPFH) modelling. This methodology is a modification of the lexicographic GP approach whereby the relative importance relations among the criteria are modelled by fuzzy relations. The proposed sequential handling for the SR portfolios selection provides information to the investors on the best result they can achieve in regard to their goals. An application to a set of UK-SR mutual funds is presented.