Kin Keung Lai

Credit risk assessment with a multistage neural network ensemble learning approach

In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the first stage, a bagging sampling approach is used to generate different training data subsets especially for data shortage. In the second stage, the different neural network models are created with different training subsets obtained from the previous stage. In the third stage, the generated neural network models are trained with different training datasets and accordingly the classification score and reliability value of neural classifier can be obtained. In the fourth stage, a decorrelation maximization algorithm is used to select the appropriate ensemble members. In the fifth stage, the reliability values of the selected neural network models (i.e., ensemble members) are scaled into a unit interval by logistic transformation. In the final stage, the selected neural network ensemble members are fused to obtain final classification result by means of reliability measurement. For illustration, two publicly available credit datasets are used to verify the effectiveness of the proposed multistage neural network ensemble model.

A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear auto-regression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ameliorate forecasting performances. We compare the new models performance with the two individual forecasting models-GLAR and ANN-as well as with the hybrid model and the linear combination models. Empirical results obtained reveal that the prediction using the nonlinear ensemble model is generally better than those obtained using the other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for exchange rates to achieve greater forecasting accuracy and improve prediction quality further.

Manufacturer’s revenue-sharing contract and retail competition

This paper investigates a revenue-sharing contract for coordinating a supply chain comprising one manufacturer and two competing retailers. The manufacturer, as a Stackelberg leader, offers a revenue-sharing contract to two competing retailers who face stochastic demand before the selling season. Under the offered contract terms, the competing retailers are to determine the quantities to be ordered from the manufacturer, prior to the season, and the retail price at which to sell the items during the season. The process of pricing and ordering is expected to result in an equilibrium as in the Bayesian Nash game. On the basis of anticipated responses and actions of the retailers, the manufacturer designs the revenue-sharing contract. Adopting the classic newsvendor problem model framework and using numerical methods, the study finds that the provision of revenue-sharing in the contract can obtain better performance than a price-only contract. However, the benefits earned under the revenue-sharing contract by different supply chain partners differ because of the impact of demand variability and price-sensitivity factors. The paper also analyses the impact of demand variability on decisions about optimal retail price, order quantity and profit sharing between the manufacturer and the retailers. Lastly, it investigates how the competition (between retailers) factor influences the decision-making of supply chain members in response to uncertain demand and profit variability.

A model for portfolio selection with order of expected returns

Abstract This paper proposes a new model for portfolio selection in which the expected returns of securities are considered as variables rather than as the arithmetic means of securities. A genetic algorithm is designed to solve the optimization problem which is difficult to solve with the existing traditional algorithms due to its nonconcavity and special structure. We illustrate the new model by a numerical example and compare the results with those derived from the traditional model of Markowitz. Scope and purpose Portfolio selection, originally articulated by Markowitz, has been one of the most important research fields in modern finance. Several new models and extensions such as the inclusion of transaction costs and taxes have been proposed to improve the performance of portfolio investment. All those models and extensions have advantages and disadvantages in both theory and practical applications. The purpose of this paper is to describe the return and risk of a portfolio more accurately. On the basis of an order of expected returns of securities, we propose a new model for portfolio selection.

A fuzzy approach to the multiobjective transportation problem

The aim of this paper is to present a fuzzy compromise programming approach to multiobjective transportation problems. A characteristic feature of the approach proposed is that various objectives are synthetically considered with the marginal evaluation for individual objectives and the global evaluation for all objectives. The decision-maker’s preference is taken into account by his/her assigning the weights of objectives. With the global evaluation for all objectives, a compromise programming model is formulated. This model covers a wide spectrum of methods with Zimmermann’s fuzzy programming approach essentially equivalent to one of its special cases. Using ordinary optimization technique, we solve the fuzzy compromise programming model to obtain a non-dominated compromise solution at which the synthetic membership degree of the global evaluation for all objectives is maximum. A numerical example is given to demonstrate the efficiency of the proposed fuzzy compromise programming approach. Scope and purpose In many real-world situations for transportation problems, decisions are often made in the presence of multiple, conflicting, incommensurate objectives. Intensive investigations on multiobjective linear transportation problem have been made. Among them Zimmermann’s fuzzy programming appears to be an ideal approach for obtaining the optimal compromise solution to a multiobjective transportation problem. However, due to the ease of computation, the aggregate operator used in Zimmermann’s fuzzy programming is the “min” operator, which does not guarantee a non-dominated solution. In this paper, we propose a new fuzzy compromise programming approach to multiobjective transportation problems. It is shown that the approach proposed can give a compromise solution that is not only non-dominated but also optimal in a certain sense. The proposed approach is robust enough to cover a wide spectrum of methods. Zimmermann’s fuzzy programming approach is essentially equivalent to one of the proposed method’s special cases.

An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.

Evolving Least Squares Support Vector Machines for Stock Market Trend Mining

In this paper, an evolving least squares support vector machine (LSSVM) learning paradigm with a mixed kernel is proposed to explore stock market trends. In the proposed learning paradigm, a genetic algorithm (GA), one of the most popular evolutionary algorithms (EAs), is first used to select input features for LSSVM learning, i.e., evolution of input features. Then, another GA is used for parameters optimization of LSSVM, i.e., evolution of algorithmic parameters. Finally, the evolving LSSVM learning paradigm with best feature subset, optimal parameters, and a mixed kernel is used to predict stock market movement direction in terms of historical data series. For illustration and evaluation purposes, three important stock indices, S&P 500 Index, Dow Jones Industrial Average (DJIA) Index, and New York Stock Exchange (NYSE) Index, are used as testing targets. Experimental results obtained reveal that the proposed evolving LSSVM can produce some forecasting models that are easier to be interpreted by using a small number of predictive features and are more efficient than other parameter optimization methods. Furthermore, the produced forecasting model can significantly outperform other forecasting models listed in this paper in terms of the hit ratio. These findings imply that the proposed evolving LSSVM learning paradigm can be used as a promising approach to stock market tendency exploration.

A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support

In this paper, a distance-based group decision-making (GDM) methodology is proposed to solve unconventional multi-person multi-criteria emergency decision-making problems. In this model, some decision-makers are first identified to formulate a group decision-making framework. Then a standard multi-criteria decision-making (MCDM) process is performed on specific decision-making problems and different decision results are obtained from different decision-makers. Finally, these different decision results are aggregated into a group consensus to support the final decision-making. For illustration and verification purposes, a numerical example and a practical unconventional emergency decision case are presented. Experimental results obtained demonstrate that the proposed distance-based multi-criteria GDM methodology can improve decision-making objectivity and emergency management effectiveness.

Portfolio rebalancing model with transaction costs based on fuzzy decision theory

The fuzzy set is one of the powerful tools used to describe an uncertain environment. As well as quantifying any potential return and risk, portfolio liquidity is taken into account and a linear programming model for portfolio rebalancing with transaction costs is proposed. The level of return that an investor might aspire to, the risk and the liquidity of portfolio are vague in an uncertain financial environment. Considering them as fuzzy numbers, we propose a portfolio rebalancing model with transaction costs based on fuzzy decision theory. An example is given to illustrate the behavior of the proposed model using real data from the Shanghai Stock Exchange.

A class of linear interval programming problems and its application to portfolio selection

This paper discusses a class of linear programming problems with interval coefficients in both the objective functions and constraints. The noninferior solutions to such problems are defined based on two order relations between intervals, and can be found by solving a parametric linear programming problem. Considering the uncertain returns of assets in capital markets as intervals, we propose a model for portfolio selection based on the semiabsolute deviation measure of risk, which can be transformed to a linear interval programming model studied in the paper. The method is illustrated by solving a simplified portfolio selection problem.