Of Division & Agglomeration - With Some Financial Insight

The main purpose of taking up Business Analytics as a course for me was to study its varied applications in the field of finance.The first technique taught was Cluster Analysis.

Cluster Analysis is the first type of second level analysis done when we have large amount of data to be analysed.It is of two types

• Clustering

• K-Means Clustering

Hierarchical clustering is of two types:

• Divisive Clustering

• Agglomerative Clustering

Bottom-up hierarchical clustering is hierarchical agglomerative clustering or HAC.This method builds the hierarchy from the individual objects by progressively merging clusters.

In single linkage clustering the distance between two clusters is defined as the least distance between an item in one cluster and an item in the other cluster.
The hierarchy within the final cluster has the following properties:

• Clusters generated in early stages are nested in those generated in later stages.
• Clusters with different sizes in the tree can be valuable for discovery.

Process

• Agglomerative hierarchical clustering starts with every single object in a single cluster. Then, in each successive iteration, it merges the closest pair of clusters by satisfying some similarity criteria, until all of the data is in one cluster.

Advantages

• It can produce an ordering of the objects, which may be informative for data display.

• Smaller clusters are generated, which may be helpful for discovery.

Disadvantages

• No provision can be made for a relocation of objects that may have been 'incorrectly' grouped at an early stage. The result should be examined closely to ensure it makes sense.

• Use of different distance metrics for measuring distances between clusters may generate different results. Performing multiple experiments and comparing the results is recommended to support the veracity of the original results.

Application in area of finance:

HAC is used widely in the field of finance such as stock market prediction which is an appealing application not only for research but commercial applications as well.Stock market prediction is based on structured data such as price,trading volumes and accounting volumes.Cluster analysis can be used quantitative and qualitative information in financial reports to predict stock price movements.First we convert the data into clusters using HAC and then use representative feature vectors(centroid of each cluster) to predict the stock price movements.

Author: Vrishti Garg

Posted by: Finance 2