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!
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