We study asset pricing dynamics in artificial financial markets model. The financial market is populated with agents following two heterogeneous trading beliefs, the technical and the fundamental prediction rules. Agents switch between trading rules with respect to their past performance. The agents are loss averse over asset price fluctuations. Loss aversion behaviour depends on the past performance of the trading strategies in terms of an evolutionary fitness measure. We propose a novel application of the prospect theory to agent-based modelling, and by simulation, the effect of evolutionary fitness measure on adaptive belief system is investigated. For comparison, we study pricing dynamics of a financial market populated with chartists perceive losses and gains symmetrically. One of our contributions is validating the agent-based models using real financial data of the Egyptian Stock Exchange. We find that our framework can explain important stylized facts in financial time series, such as random walk price behaviour, bubbles and crashes, fat-tailed return distributions, power-law tails in the distribution of returns, excess volatility, volatility clustering, the absence of autocorrelation in raw returns, and the power-law autocorrelations in absolute returns. In addition to this, we find that loss aversion improves market quality and market stability.

Kamal Samy Selim

Ahmed Okasha

Heba M. Ezzat

Loss Aversion, Adaptive Beliefs, and Asset Pricing Dynamics

The main purpose of this paper is to improve on the conceptual as well as the methodological aspects of BSC as a quantitative model by combining elements from traditional balanced scorecard (BSC) thinking with the Systems Thinking. This is done by combining short and long term aspects of measurements. The result is then used to build and construct a balanced scorecard model for strategic learning with the specific aim to maintain satisfied customers and motivated employees. Strategic planning, operational execution, feedback, and learning are some of the most important key features of any performance measurement model. This paper aims to address not only the conceptual domain related to BSC, that is, learning and system dynamics causality and feedback, but also the methodological domain concept of precision solved by differential equations. Our results show how a potential move from a static strategic vision map to a linked and dynamic understanding may be not fully realistic but very useful for learning purposes. The new knowledge obtained from the learning feedbacks fertilizes both decision discussion and decision-making and what may be required in order to move to the next level of BSC and system dynamics integration.

Steen Nielsen

Erland H. Nielsen

The Balanced Scorecard and the Strategic Learning Process: A System Dynamics Modeling Approach

This paper proposes a new hybrid multiattribute decision making (MADM) model which deals with the interactions that usually exist between hostel attributes in the process of measuring the students&#x2019; satisfaction towards a set of hostels and identifying the optimal strategies for enhancing their satisfaction. The model uses systematic random stratified sampling approach for data collection purpose as students dwelling in hostels are &#x201c;naturally&#x201d; clustered by block and gender, factor analysis for extracting large set of hostel attributes into fewer independent factors, <i>&#x3bb;</i>-measure for characterizing the interactions shared by the attributes within each factor, Choquet integral for aggregating the interactive performance scores within each factor, Mikhailov&#x2019;s fuzzy analytical hierarchy process (MFAHP) for determining the weights of independent factors, and simple weighted average (SWA) operator to measure the overall satisfaction score of each hostel. A real evaluation involving fourteen Universiti Utara Malaysia (UUM) hostels was carried out in order to demonstrate the model&#x2019;s feasibility. The same evaluation was performed using an additive aggregation model in order to illustrate the effects of ignoring the interactions shared by attributes in hostel satisfaction analysis.

Anath Rau Krishnan

Engku Muhammad Nazri Engku Abu Bakar

Maznah Mat Kasim

A Hybrid Multiattribute Decision Making Model for  Evaluating Students&#x2019; Satisfaction towards Hostels

Almost all of the today&#x2019;s modern daily life conditions of humankind depend on the electricity. The countries either by only themselves or sometimes with some international intuitions and/or organizations have been trying to find the best methods, ways, and projects to supply the electricity to their societies. One of the important tools for the countries to increase the amount and quality of the electricity generation is to activate/ignite/initiate the private investment capabilities/opportunities. The electricity generation market in Turkey is a free/open market for both the foreign and domestic private investors. Hence, both the foreign and domestic private investors have been looking for the most suitable electricity generation plant projects. Small hydropower plant (SHPP) investments (SHPPIs) are one of the alternatives in the Turkish electricity generation market especially for the private investors searching for the renewable energy investments. This experimental research study investigates the possibility of using the ELECTRE III/IV, Shannon&#x2019;s Entropy, and Saaty&#x2019;s Analytic Hierarchy Process (AHP) subjective weighting (for criteria) methods for the solution of this problem. In the experimental case study, the most appropriate SHPPIs amongst five alternative SHPPIs at the SHPPIs&#x2019; predevelopment investment stages in Turkey were evaluated and ranked in order.

Burak Omer Saracoglu

An Experimental Research Study on the Solution of a Private Small Hydropower Plant Investments Selection Problem by ELECTRE III/IV, Shannon&#x2019;s Entropy, and Saaty&#x2019;s Subjective Criteria Weighting

Challenges for text processing in ancient document images are mainly due to the high degree of variations in foreground and background. Image binarization is an image segmentation technique used to separate the image into text and background components. Although several techniques for binarizing text documents have been proposed, the performance of these techniques varies and depends on the image characteristics. Therefore, selecting binarization techniques can be a key idea to achieve improved results. This paper proposes a framework for selecting binarizing techniques of palm leaf manuscripts using Support Vector Machines (SVMs). The overall process is divided into three steps: (i) feature extraction: feature patterns are extracted from grayscale images based on global intensity, local contrast, and intensity; (ii) treatment of imbalanced data: imbalanced dataset is balanced by using Synthetic Minority Oversampling Technique as to improve the performance of prediction; and (iii) selection: SVM is applied in order to select the appropriate binarization techniques. The proposed framework has been evaluated with palm leaf manuscript images and benchmarking dataset from DIBCO series and compared the performance of prediction between imbalanced and balanced datasets. Experimental results showed that the proposed framework can be used as an integral part of an automatic selection process.

Rapeeporn Chamchong

Chun Che Fung

A Framework for the Selection of Binarization Techniques on Palm Leaf Manuscripts Using Support Vector Machine

Albert Vexler

Yang Zhao

Hadi Alizadeh Noughabi

Comment on &#x201C;Comparison of Some Tests of Fit for the Inverse Gaussian Distribution&#x201D;

Organizations typically employ the ABC inventory classification technique to have an efficient control on a huge amount of inventory items. The ABC inventory classification problem is classification of a large amount of items into three groups: A, very important; B, moderately important; and C, relatively unimportant. The traditional ABC classification only accounts for one criterion, namely, the annual dollar usage of the items. But, there are other important criteria in real world which strongly affect the ABC classification. This paper proposes a novel methodology based on a common weight linear optimization model to solve the multiple criteria inventory classification problem. The proposed methodology enables the classification of inventory items via a set of common weights which is very essential in a fair classification. It has a remarkable computational saving when compared with the existing approaches and at the same time it needs no subjective information. Furthermore, it is easy enough to apply for managers. The proposed model is applied on an illustrative example and a case study taken from the literature. Both numerical results and qualitative comparisons with the existing methods reveal several merits of the proposed approach for ABC analysis.

S. M. Hatefi

S. A. Torabi

A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification

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Advances in Decision Sciences