A software toolkit for stock data analysis using social network analysis approach
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Date
2014
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Publisher
University of New Brunswick
Abstract
In this work, we design an online analytical toolkit benefiting from the domain
of Social Network Analysis. The objective is to provide a networkcentric
perspective for analyzing stock data in facilitating portfolio management.
The core process of this toolkit is to create a network of stocks from
New York Stock Exchange (NYSE) and National Association of Securities
Dealers Automated Quotations (NASDAQ). Each node in this network represents
a stock and the weight of linked edges between any two stocks is
decided by the correlation coefficient calculated based on the historical daily
returns between the two stocks involved. With this network, there are several
embedded functionalities designed for further analysis. Users can write their
own scripts on top of this network, generate the specific portfolios, simulate
the history trend with another index and visualize the result for comparison.
The software architecture of the toolkit is a client-server architecture in
which the user interface, functional process logic, data storage and access are
developed and maintained as independent modules. This toolkit is evaluated
through a case study on simulating the history trend of the Dow Jones Industrial Average (DJIA), along with multiple experimental scenarios tested
on this toolkit for system performance evaluation. As an important observation
from this case study, a careful selection of alternative stock portfolios
based on network criteria shows similar trends with DJIA. While the latter
is a portfolio constructed mainly based on the importance ( or size) of the
constitutive stocks, our network-centric construction of alternative portfolios
illustrates that the phenomenon of "too-connected-to-be-included" is as
important as (if not more) "too-big-to-be-included". This new observation
possesses great potentials in portfolio management by offering an alternative
way of stocks selection: size matters, but connection may matter more!