Euro area banking sector integration: using hierarchical cluster analysis techniques

In this study we apply cluster analysis techniques, including a novel smoothing method, to detect some basic patterns and trends in the euro area banking sector in terms of the degree of homogeneity of countries. We find that in the period 1998-2004 the banking sectors in the euro area countries seem to have become somewhat more homogeneous, although the results are not unequivocal and considerable differences remain, leaving scope for further integration. In terms of clustering, the Western and Central European countries (like Germany, France, Belgium, and to some extent also the Netherlands, Austria and Italy) tend to cluster together, while Spain and Portugal and more recently also Greece usually are in the same distinct cluster. Ireland and Finland form separate clusters, but overall tend to be closer to the Western and Central European cluster. JEL Classification: C49, F36, G21banking sector, cluster analysis, financial integration

SURVEY ON ADVISOR INTELLIGENCE THROUGH PURCHASE PATTERNS AND SALES ANALYTICS

In mutual fund, an individual or a firm that is in the business of giving advice about securities to clients is an investment advisor. Investment advisers are individuals or firms that receive compensation for giving advice on investing in stocks, bonds, mutual funds, or exchange-traded funds. Investment advisors manage portfolios of securities. Advisors can use new cognitive and analytics capabilities to better understand their clients and needs and have a stronger ability to deepen relationships with a better portfolio. In this paper, we analyze data points foreach advisor, and distinguish the best prospects, obtain insight into their experience and credentials, and learn about their portfolio, in other words, to recognize the pattern of portfolio of the advisors. Such analysis helps the sales people to sell the fund company products to the suitable advisors based on the nature of the product they want to sell. This is done by investigating what kind of products advisors have been buying, and what kind of products they might be looking for. This helps to increase the sales of the products as sales people will be reaching the appropriate advisors

An assessment of the application of cluster analysis techniques to the Johannesburg Stock Exchange

Includes bibliographical references.Cluster analysis is becoming an increasingly popular method in modern finance because of its ability to summarise large amounts of data and so help individual and institutional investors to make timeous and informed investment decisions. This is no less true for investors in smaller, emerging markets - such as the Johannesburg Stock Exchange - than it is for those in the larger global markets. This study examines the application of two clustering techniques to the Johannesburg Stock Exchange. First, the application of Salvador and Chan's (2003) L method stopping rule to a hierarchical clustering of time series return data was analysed as a method for determining the number of latent groups in the data set. Using Ward's method and the Euclidean distance function, this method appears to be able detect the correct number of clusters on the JSE. Second, the ability of three different clustering algorithms to generate consistent clusters and cluster members over time on the Johannesburg Stock Exchange was analysed. The variation of information was used to measure the consistency of cluster members through time. Hierarchical clustering using Ward's method and the Euclidean distance measure proved to produce the most consistent results, while the K-means algorithms generated the least consistent cluster members

Cluster analysis approach for banks’ risk profile : the Romanian evidence

Cluster analysis, as an exploratory technique, by gathering together those credit institutions sharing similar features in terms of financial intermediation activity, proves to be a complementary tool for the peer group analysis, accomplished at the off-site supervision level. The aim of our study was to include a representative sample of Romanian credit institutions into smaller, homogenous clusters, in order to assess which credit institutions have similar patterns according to their risk profile and profitability. We found that, over the period 2004-2006, the clusters remained relatively stable in terms of similarity of exposure to risks and profitability.peer-reviewe

A generic hierarchical clustering approach for detecting bottlenecks in manufacturing

The advancements in machine learning (ML) techniques open new opportunities for analysing production system dynamics and augmenting the domain expert\u27s decision-making. A common problem for domain experts on the shop floor is detecting throughput bottlenecks, as they constrain the system throughput. Detecting throughput bottlenecks is necessary to prioritise maintenance and improvement actions and obtain greater system throughput. The existing literature provides many ways to detect bottlenecks from machine data, using statistical-based approaches. These statistical-based approaches can be best applied in environments where the statistical descriptors of machine data (such as distribution of machine data, correlations and stationarity) are known beforehand. Computing statistical descriptors involves statistical assumptions. When the machine data doesn\u27t comply with these assumptions, there is a risk of the results being disconnected from actual production system dynamics. An alternative approach to detecting throughput bottlenecks is to use ML- based techniques. These techniques, particularly unsupervised ML techniques, require no prior statistical information on machine data. This paper proposes a generic, unsupervised ML-based hierarchical clustering approach to detect throughput bottlenecks. The proposed approach is the outcome of systematic and careful selection of ML techniques. It begins by generating a time series of the chosen bottleneck detection metric and then clustering the time series using a dynamic time-wrapping measure and a complete-linkage agglomerative hierarchical clustering technique. The results are clusters of machines with similar production dynamic profiles, revealed from the historical data and enabling the detection of bottlenecks. The proposed approach is demonstrated in two real-world production systems. The approach integrates the concept of humans in-loop by using the domain expert\u27s knowledge