Potential of psychological information to support knowledge discovery in consumer debt analysis

In this work, we develop a Data Mining framework to explore the multifaceted nature of consumer indebtedness. Data Mining with its numerous techniques and methods poses as a powerful toolbox to handle the sensitivity of these data and explore the psychological aspects of this social phenomenon. Thus, we begin with a series of transformations that deal with any inconsistencies the data may contain but more importantly they capture the essential psychological information hidden in the data and represent it in a new feature space as behavioural data. Then, we propose a novel consensus clustering framework to uncover patterns of consumer behaviour which draws upon the ability of cluster ensembles to reveal robust clusters from diffcult datasets. Our Homals Consensus, models successfully the relationships between different clusterings in the cluster ensemble and manages to uncover representative clusters that are more suitable for explaining the complex patterns of a socio-economic dataset. Finally under a supervised learning approach the behavioural aspects of consumer indebtedness are assessed. In more detail, we take advantage of the exibility Neural Networks provide in determining their architecture in order to propose a novel Neural Network solution, named TopDNN, that can handle non-linearities in the data and takes into account the extracted behavioural knowledge by incorporating it in the model. All the above sketch an elaborate framework that can reveal the potential of the behavioural data to support Knowledge Discovery in Consumer Debt Analysis on one hand and the ability of Data Mining to supplement existing models and theories of complex and sensitive nature on the other

The Effect of Outward Foreign Direct Investment on Exports in Japan during The Lost Decade, 1985-2005

This study will utilize an OLS analysis of the panel data coming from various databases such as the World Bank, Federal Reserve and etc. to determine if there exists a significant correlation between the exports of Japan and its foreign direct investment (FDI) in the period of 1985-2005 with nearby trading partners of which include the countries of ASEAN, China and South Korea. In addition, a selection of supplemental factors will be used to determine if they also possess any significance with exports. These factors include: the exchange rate of the Japanese yen with the currency of the trading partner in relation to the US Dollar, the magnitude of trade between Japan and the trading partner (YA * YB), and GDP growth. The signing of the Plaza Accord in 1985 caused the appreciation of the Japanese yen that inflated the economy to a substantial growth period that rivalled post-war revival. Japan’s transition to becoming the leading economy in East Asia enabled itself to become a provider of capital by initiating outward FDI in less developed countries to penetrate into those foreign markets. This study analyzes Japan’s trade relations with the Association of Southeast Asian Nations (ASEAN), South Korea, Taiwan and China by testing empirically how Japan’s FDI in those host countries spur its export growth in those countries. Japan pursued an export-led growth strategy in its economic development in the post-World War II period, which led to an insurmountable trade surplus from 1960 to the mid 1980’s. Applying trade theories such as the border effect and the gravity model, this study will evaluate what prompted Japan’s export growth in those host countries in the region after currency appreciation under the Plaza Accord. This paper proposes that Japan’s exports are indeed significantly correlated with FDI, relative exchange rate, trade intensity and GDP growth. It is concluded that Japan’s outward FDI contributes to its export growth during the period under study

Indebted households profiling: a knowledge discovery from database approach

A major challenge in consumer credit risk portfolio management is to classify households according to their risk profile. In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk. The objective of this work is to employ a knowledge discovery from database process to identify groups of indebted households and describe their profiles using a database collected by the Consumer Credit Counselling Service (CCCS) in the UK. Employing a framework that allows the usage of both categorical and continuous data altogether to find hidden structures in unlabelled data it was established the ideal number of clusters and such clusters were described in order to identify the households who exhibit a high propensity of excessive debt levels

Potential of psychological information to support knowledge discovery in consumer debt analysis

In this work, we develop a Data Mining framework to explore the multifaceted nature of consumer indebtedness. Data Mining with its numerous techniques and methods poses as a powerful toolbox to handle the sensitivity of these data and explore the psychological aspects of this social phenomenon. Thus, we begin with a series of transformations that deal with any inconsistencies the data may contain but more importantly they capture the essential psychological information hidden in the data and represent it in a new feature space as behavioural data. Then, we propose a novel consensus clustering framework to uncover patterns of consumer behaviour which draws upon the ability of cluster ensembles to reveal robust clusters from diffcult datasets. Our Homals Consensus, models successfully the relationships between different clusterings in the cluster ensemble and manages to uncover representative clusters that are more suitable for explaining the complex patterns of a socio-economic dataset. Finally under a supervised learning approach the behavioural aspects of consumer indebtedness are assessed. In more detail, we take advantage of the exibility Neural Networks provide in determining their architecture in order to propose a novel Neural Network solution, named TopDNN, that can handle non-linearities in the data and takes into account the extracted behavioural knowledge by incorporating it in the model. All the above sketch an elaborate framework that can reveal the potential of the behavioural data to support Knowledge Discovery in Consumer Debt Analysis on one hand and the ability of Data Mining to supplement existing models and theories of complex and sensitive nature on the other

Using clustering to extract personality information from socio economic data

It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours. In this work, we present a method to extract Behavioural Groups by using simple clustering techniques that can potentially reveal aspects of the Personalities for their members. We believe that this is very important because the psychological information regarding the Personalities of individuals is limited in real world applications and because it can become a useful tool in improving the traditional models of Knowledge Economy

Augmented neural networks for modelling consumer indebtness

Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models. In this work we show that Computational Intelligence can offer a more holistic approach that is more suitable for the complex relationships an indebtness dataset has and Linear Regression cannot uncover. In particular, as our results show, Neural Networks achieve the best performance in modelling consumer indebtness, especially when they manage to incorporate the significant and experimentally verified results of the Data Mining process in the model, exploiting the flexibility Neural Networks offer in designing their topology. This novel method forms an elaborate framework to model Consumer indebtness that can be extended to any other real world application

A data mining framework to model consumer indebtedness with psychological factors

Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of Psychological Factors, like Impulsivity to the analysis of Consumer Debt. Our results confirm the beneficial impact of Psychological Factors in modelling Consumer Indebtedness and suggest a new approach in analysing Consumer Debt, that would take into consideration more Psychological characteristics of consumers and adopt techniques and practices from Data Mining

A data mining framework to model consumer indebtedness with psychological factors

Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of Psychological Factors, like Impulsivity to the analysis of Consumer Debt. Our results confirm the beneficial impact of Psychological Factors in modelling Consumer Indebtedness and suggest a new approach in analysing Consumer Debt, that would take into consideration more Psychological characteristics of consumers and adopt techniques and practices from Data Mining

Integrating peer-to-peer functionalities and routing in mobile ad-hoc networks

Mobile Ad-hoc Networks (MANETs) impose strict requirements in terms of battery life, communication overhead and network latency, therefore optimization should be made to applications and services such as domain name service (DNS), dynamic host configuration protocol (DHCP) and session initiation protocol (SIP) if they are to be considered for use on MANETs. Due to the decentralized and self-organizing nature of MANETs, such applications could utilize a distributed name resolution/data storage service. Distributed Hash Tables (DHTs) enable these features by virtually organizing the network topology in a peer-to-peer (P2P) overlay. P2P overlays have been designed to operate on the application layer without knowledge of the underlying network thus causing poor performance. To address this problem, we propose and evaluate two different DHTs integrated with MANET routing in order to optimize the overall performance of MANET communications when P2P applications and services are used. Both architectures share the same functionality such as decentralization, self-reorganization, and self-healing but differ in MANET routing protocol. Performance evaluation using the NS2 simulator shows that these architectures are suited to different scenarios namely increasing network size and peer velocity. Comparisons with other well-known solutions have proven their efficiency with regard to the above requirements