Viable Stabilising Non-Taylor Monetary Policies for an Open Economy

We extend Krawczyk and Kim (Macroecon Dyn 13(1):46–80, 1999) and apply a viability approach to a small-open economy where the exchange rate works as an additional monetary policy transmission channel. A continuous-time version of the model presented in Batini and Haldane (In: Monetary policy rules. National Bureau of Economic Research, Cambridge, pp. 157–202, 1999a) is used. The model comprises the IS equation, a supply curve and the interest parity condition. We modify the third equation to capture an impact of a domestic interest-rate hike on the speedy appreciation of local currency. We calibrate this modified model using available literature results and apply specialised software (VIKAASA) to compute the open-economy viability kernel that is a set of economic states, from which the central bank can control the economy so that it remains within a nominal constraint set. We then analyse the kernel topology and show a few stablising policies that keep the economy within the constraint set. We also discuss the robustness of such polices to shocks and parameter uncertainty and observe that viability-based policies come from models, which do not require explicit weights on the variables of interest of a central bank. We also contend that in general, viability-based policies are less likely to do damage, if the policy-maker is wrong about some aspects of the environment.     

Central banking in a transitory economy: an adaptive control model for India

The purpose of this paper is to formulate a policy model for the Indian economy, incorporating an expectation generating mechanism in a formal way. The aim is to see how policies will change over time when the expectation of the private sector is changing. In a mixed economy planning has to take into account how the private sector formulates and revises its expectation regarding various government policies and their possible impacts on the endogenous variables (or target variables) in the economy. Normally in an economy like India's the government formulates its plan for the public sector over the next five years. The government can regulate the private sector by various means, such as licences, investment quotas, tax-subsidy rates, bank interest rates and by various monetary controls. The private sector, knowing the targets of the government, formulates its own expectations regarding the fulfulments of the targets (because it never expects that the government can fulfil its targets exactly) and possible movements of various policies. It behaves according to its expectations and realization of past expectations and thus allocates its resources. So the optimum design of public policies should be to direct the private sectors towards the desired goals defined by the planners, taking into account the reactions of the private sector. The purpose of public policy in such an environment should be to formulate different central bank policies regarding money stock, credit expansion and components of the money multiplier along with government discount rate, and the exchange rate so that the private sector would react in the desired way, given the goals set by the planners regarding national income, balance of payments, domestic absorptions and prices.     

Enhancing decisions with life cycle analysis for risk management

Although corporate financial distress is an infrequent occurrence, it has an extremely debilitating effect on the stability of a firm when it does occur. For this reason, an accurate risk assessment mechanism is needed in numerous industry sectors, particularly in financial institutions and banking. Based on corporation life cycle theory and risk management, this study develops a risk pre-warning model, namely the RSVMDT model, to eliminate serious financial punching and to examine the effectiveness of transparency and the full disclosure index (TFDI) during each life cycle stage. The RSVMDT model includes three techniques: random forest (RF), support vector machines (SVMs), and decision trees (DTs). The RF is used to determine the essential attributes of firms and therefore decrease the computational complexity of financial analysis and improve the classification accuracy. The SVM is employed as a classifier to identify corporations in financial distress. Finally, the DT is utilized as a rule generator that allows decision makers to adjust the financial structures of firms at specific life cycle stages. Together, these three techniques can increase the probability of corporate survival in a highly competitive environment. Additionally, the study further evaluates the importance of the TFDI during a turbulent economy. The public sectors can benefit from this evaluation by formulating future policies based on the rules derived from the developed RSVMDT model.     

Optimal maintenance policies for systems subject to a Markovian operating environment

Many stochastic models of repairable equipment deterioration have been proposed based on the physics of failure and the characteristics of the operating environment, but they often lead to time to failure and residual life distributions that are quite complex mathematically. The first objective of our study is to investigate the potential for approximating these distributions with traditional time to failure distribution. We consider a single-component system subject to a Markovian operating environment such that the system’s instantaneous deterioration rate depends on the state of the environment. The system fails when its cumulative degradation crosses some random threshold. Using a simulation-based approach, we approximate the time to first failure distribution for this system with a Weibull distribution and assess the quality of this approximation. The second objective of our study is to investigate the cost benefit of applying a condition-based maintenance paradigm (as opposite to a scheduled maintenance paradigm) to the repairable system of interest. Using our simulation model, we assess the cost benefits resulting from condition-based maintenance policy, and also the impact of the random prognostic error in estimating system condition (health) on the cost benefits of the condition-based maintenance policy.     

A novel corporate credit rating system based on Student’s-t hidden Markov models

Corporate credit rating systems have been an integral part of expert decision making of financial institutions for the last four decades. They are embedded into the pricing function determining the interest rate of a loan contact, and play crucial role in the credit approval process. However, the currently employed intelligent systems are based on assumptions that completely ignore two key characteristics of financial data, namely their heavy-tailed actual distributions, and their time-series nature. These unrealistic assumptions definitely undermine the performance of the resulting corporate credit rating systems used to inform expert decisions. To address these shortcomings, in this work we propose a novel corporate credit rating system based on Student’s-t hidden Markov models (SHMMs), which are a well-established method for modeling heavy-tailed time-series data: Under our approach, we use a properly selected set of financial ratios to perform credit scoring, which we model via SHMMs. We evaluate our method using a dataset pertaining to Greek corporations and SMEs; this dataset includes five-year financial data, and delinquency behavioral information. We perform extensive comparisons of the credit risk assessments obtained from our method with other models commonly used by financial institutions. As we show, our proposed system yields significantly more reliable predictions, offering a valuable new intelligent system to bank experts, to assist their decision making.     

系统云灰色宏观调控预测模型及其应用研究

根据系统云灰色预测模型,针对具有"贫"信息、小样本序列数据的社会经济系统,分析了外部环境变化或外部政策调控对系统预测的影响机理.在系统动态结构相对稳定的条件下,结合系统动力学和系统云灰色模型,提出了一种系统云灰色宏观调控预测模型,并对该模型的求解方法进行了研究.最后,以国民经济增长速度的宏观调控预测为例,阐述了上述调控预测模型的使用方法、合理性和准确性.     

Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modelling approaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organising modelling methods for the daily prediction of the exchange rate market. We also propose a combined approach where the parametric and nonparametric self-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchange rates: the American Dollar and the Deutche Mark against the British Pound.     

Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization

Improvement of dead reckoning accuracy is essential for robotic localization systems and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using an adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy and works better in slip detection and compensation compared to the conventional multiple model approach.     

A joint model for cash and inventory management for a retailer under delay in payments

As retail companies continue to navigate through the economy downturn, it becomes critical to find innovative cost reduction methods. Cash management is a cost-intensive process for retailers, who are currently focusing on effective cash management, such as deciding on the maximum cash level to keep in their business accounts and how much to borrow to finance inventories and pay suppliers. In this paper, we consider the problem of finding the optimal operational (how much to order and when to pay the supplier) and financial decisions (maximum cash level and loan amount) by integrating the cash management and inventory lot sizing problems. We consider a supplier offering a retailer an interest-free credit period for settling the payment. Beyond this period, the supplier charges interest on the outstanding balance. Whenever the cash exceeds a certain limit, it will be invested in purchasing financial securities. At the time when the retailer pays the supplier for the received order, cash is withdrawn from the account, incuring various financial costs. If the cash level becomes zero or not sufficient, the retailer obtains an asset-based loan at interest. We model this problem as a nonlinear program and propose a solution procedure for finding the optimal solution. We perform a numerical study to analyze the impact of optimal cash management on the inventory decisions. The results indicate that the optimal order quantity decreases as the retailer’s return on cash increases. We compare our model to a model that ignores financial considerations of cash management, and show numerically that our model lowers the retailer’s cost. Also, we illustrate the effect of changing various model parameters on the optimal solution and obtain managerial insights.     

Modeling client profiles for order promising and delivery

In order to stay competitive in the global economy which provides clients with a wide range of potential suppliers, businesses offer highly parameterizable products with increasingly shorter delays. We propose a simulation approach that allows businesses to estimate their capacity to honor promised delivery dates in a make-to-order environment. This paper presents a framework as well as the needs that arise when considering client profiles in a modeling and simulation approach. We focus here on our methodology for representing and generating client behaviors, as well as on the demand and supply simulation approach and environment developed to conduct experiments on delivery time promising policies and delivery capacity.     

Visual Exploration of Financial Data with Incremental Domain Knowledge

Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation-wide aggregate data. Our solution is coupled with a pipeline for the generation of firm-to-firm financial transaction networks, fusing information about individual firms with sector-to-sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight-based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.     

Fuzzy pricing of geometric Asian options and its algorithm

Owing to the fluctuations of the financial market, input data in the options pricing formula cannot be expected to be precise. This paper discusses the problem of pricing geometric Asian options under the fuzzy environment. We present the fuzzy price of the geometric Asian option under the assumption that the underlying stock price, the risk-free interest rate and the volatility are all fuzzy numbers. This assumption makes the financial investors to pick any geometric Asian option price with an acceptable belief degree. In order to obtain the belief degree, the interpolation search algorithm has been proposed. Some numerical examples are presented to illustrate the rationality and practicability of the model and the algorithm. Finally, an empirical study is performed based on the real data. The empirical study results indicate that the proposed fuzzy pricing model of geometric Asian option is a useful tool for modeling the imprecise problem in the real world.     

Triggers and barriers to financial inclusion: The use of ICT-based branchless banking in an Amazon county

Financial inclusion can be defined as the access to formal financial services at an affordable cost for all members of an economy, favoring mainly low-income groups. It has been recognized as a critical element in policies for poverty reduction and economic growth. Some successful experiences with financial inclusion reported in developing countries are associated with the use of information and communication technology (ICT)-based branchless banking. One of these experiences is the Brazilian correspondent model, an ICT-based network responsible for delivering financial services to tens of millions of poor Brazilians, most of them having no other way to access banking services. This article presents a case study of financial inclusion in Autazes, a county in the Amazon region not served by banks until 2002, when a correspondent started its operations there. Since then, Autazes has experienced economic and social changes, due in part to government social benefits and other banking services delivered at the local level. The results of our field study in Autazes suggest that financial inclusion through the correspondents’ process positively contributes to local socio-economic development but, at the same time, presents clear negative signs such as low-income population over-indebtedness, reproduction of social exclusion practices and reinforcement of power asymmetries. We conclude that although access to financial resources is a fundamental way to promote local development to low-income population, such access should be accompanied by other inclusive mechanisms like financial education in order to be effective.     

An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: A case on manufacturing industry

Globally, supply chains compete in a complex and rapidly changing environment. Hence, sustainable supplier selection has become a decisive variable in the firm’s financial success. This requires reliable tools and techniques to select the best sustainable supplier and enhance understanding about how supplier behavior evolves with time. System dynamics (SD) is an approach to investigate the dynamic behavior in which the system status alterations correspond to the system variable changes. Fuzzy logic usually solves the challenges of imprecise data and ambiguous human judgment. Thus, this work presents a novel modeling approach of integrating information on supplier behavior in fuzzy environment with system dynamics simulation modeling technique which results in a more reliable and responsible decision support system. Supplier behavior with respect to relevant sustainability criteria in the past, current and future time horizons were sourced through expert interviews and simulated in Vensim to select the best possible sustainable supplier. Simulation results show that an increase in the rate of investment in sustainability by the different suppliers causes an exponential increase in total sustainability performance of the suppliers. Also, the growth rate of the total performance of suppliers outruns their rate of investment in sustainability after about 12 months. A dynamic multi-criteria decision making model was presented to compare results from the systems dynamics model.     

Component Verification with Automatically Generated Assumptions

Model checking is an automated technique that can be used to determine whether a system satisfies certain required properties. The typical approach to verifying properties of software components is to check them for all possible environments. In reality, however, a component is only required to satisfy properties in specific environments. Unless these environments are formally characterized and used during verification (assume-guarantee paradigm), the results returned by verification can be overly pessimistic. This work introduces an approach that brings a new dimension to model checking of software components. When checking a component against a property, our modified model checking algorithms return one of the following three results: the component satisfies a property for any environment; the component violates the property for any environment; or finally, our algorithms generate an assumption that characterizes exactly those environments in which the component satisfies its required property. Our approach has been implemented in the LTSA tool and has been applied to the analysis of two NASA applications.This paper is an expanded version of Giannakopoulou et al. (2002).     

Optimizing replenishment polices using Genetic Algorithm for single-warehouse multi-retailer system

Coordinating inventory and transportation policies can lead to substantial cost savings and improved service levels especially when the companies relay on third-party logistics providers to transport the products across the supply chain. In this paper, therefore focus has been given on a supply chain system of multi-supplier, single warehouse and multi-retailer with backlogging and transportation capacity. The paper aims to suggest replenishment policies that can minimize system-wide cost by taking advantage of quantity discounts in the transportation cost structures. The problem considered in this paper has been formulated as an integer programming model. The supply chain problem is usually complex and involves massive calculations hence it is difficult to obtain an optimal solution. Therefore, to overcome this issue a Genetic Algorithm (GA) based approach has been suggested to resolve the problem. The computational results demonstrate the robustness and efficacy of the GA in optimizing replenishment policies.     

On the construction and complexity of the bivariate lattice with stochastic interest rate models

Complex financial instruments with multiple state variables often have no analytical formulas and therefore must be priced by numerical methods, like lattice ones. For pricing convertible bonds and many other interest rate-sensitive products, research has focused on bivariate lattices for models with two state variables: stock price and interest rate. This paper shows that, unfortunately, when the interest rate component allows rates to grow in magnitude without bounds, those lattices generate invalid transition probabilities. As the overwhelming majority of stochastic interest rate models share this property, a solution to the problem becomes important. This paper presents the first bivariate lattice that guarantees valid probabilities. The proposed bivariate lattice grows (super)polynomially in size if the interest rate model allows rates to grow (super)polynomially. Furthermore, we show that any valid constant-degree bivariate lattice must grow superpolynomially in size with log-normal interest rate models, which form a very popular class of interest rate models. Therefore, our bivariate lattice can be said to be optimal.     

Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting

This paper presents a simultaneous optimization method of a case-based reasoning (CBR) system using a genetic algorithm (GA) for financial forecasting. Prior research proposed many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most research used the GA for improving only a part of architectural factors of the CBR model. However, the performance of the CBR model may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of the CBR model outperforms other conventional approaches for financial forecasting.     

STRUCTURING AND MODELING DATA FOR REPRESENTING THE BEHAVIOR OF AGENTS IN THE GOVERNANCE OF THE BRAZILIAN FINANCIAL SYSTEM

This article proposes a method to collect and structure data in order to model the behavior of agents in an agent-based simulation model. This model aims to study the regulatory governance of the Brazilian financial system. In this article, the regulatory governance is understood as a phenomenon that results from several interactions and transactions among all actors that influence or are influenced by the activities of the regulation policies. The study focuses on the short-term interest rates and incorporates behavioral aspects and no-explicit interests of social and economical agents. The developed method integrates content analysis research and in-depth interviews to model the agent' behavior by means of fuzzy logic rules. It provides systematic collection and interpretation of data produced in textual form as well as knowledge from experts. The results of the model validation have shown that this approach contributes to the development of a methodology for the modeling and representation of agent behavior in social applied simulation models.     

The use of hybrid manifold learning and support vector machines in the prediction of business failure

The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. This paper proposes a hybrid manifold learning approach model which combines both isometric feature mapping (ISOMAP) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the ISOMAP algorithm to perform dimension reduction, is then utilized as a preprocessor to improve business failure prediction capability by SVM. To create a benchmark, we further compare principal component analysis (PCA) and SVM with our proposed hybrid approach. Analytic results demonstrate that our hybrid approach not only has the best classification rate, but also produces the lowest incidence of Type II errors, and is capable of achieving an improved predictive accuracy and of providing guidance for decision makers to detect and prevent potential financial crises in the early stages.