Article Preview
Top1. Introduction
Due to the massive need to process raw financial and economic data in the global financial market such as daily stocks prices, volume of stocks traded, stock price fluctuations, the movement of major stock indices, there has been a growing need for new ways to generate both visual and textual knowledge representations, so that non-professional investors and traders can make more informed and intelligent investment and trading decisions in managing their own finances.
Knowledge representation is how information or data is represented to enable human beings to grasp data and information in meaningful terms. We sometimes think human knowledge is communicated mostly through textual information, but other types of knowledge are more effectively communicated visually in terms of graphs, pictorials, symbols, and images. Information in graphic or symbolic form is not only expressing what is being shown, but how it is being shown to improve the effectiveness and speed of human cognition. Sometimes text, numbers, graphs, animation, and symbols can all be combined in a rich form of knowledge representation to amplify human cognition, speed up the cognitive process, and enhance intelligent perception.
For investors and traders, how massive amounts of financial data is processed and presented in both textual and graphic form is very important when making daily buying and selling decisions. The math wizards of Wall Street often referred to as the “quants” (Crooks, Slayton, Burbridge, 2011; Patterson, 2010) use mathematical algorithms (Deng, Wang, & Dong, 2012) and fast super-computers (Kelly, 2012) to generate stock price predictions that are able to process massive amounts of raw financial data and transform them into intelligent stock price predictions that can be read by both humans and machines. While these data can be presented in both text and visual representations, many investors and traders seem to be able to make more intelligent split-second decisions on the buying and selling of stocks based on easy-to-interpret real-time visual data representations that they see on their screen such as “candlestick charts” (Nisson, 2001).
The problem that analytics software designers are currently facing is determining which features are highly desirable and effective from an end-user’s perspective. Because stock traders and investors have a very limited time window to analyze and intelligently digest financial data before they decide to buy or sell stocks and options, designing effective software interface for financial analytics is critical for this particular end-user group. According to Daugherty (2013), Chief Technology Officer of Accenture, “In a recent Accenture Analytics survey of 600 executives representing large organizations in the UK and US … only 20% are “very satisfied” with the business outcomes from their existing analytics programs. These are underwhelming responses to say the least.”
The main contribution of this paper is to determine what software design, interface features, and functions are considered most desirable in the domain of financial analytics from an end-user’s perspective. With the findings of this research, software analytics designers will have a better grasp at how users rate the usefulness of different software features found in financial web portals.
This paper is organized starting with Section 2 discussing the research objective. After that, Section 3 is dedicated to the literature reviews on analytics and knowledge representations, which are related to the research objective. Section 4 discusses the methodology for gathering and analyzing data. Section 5 introduces the basic objectives of financial web portals, while Section 6 provides the critical elements that make financial web portals successful in addressing the needs of the users. Section 7 threshes out four cases of financial web portals, while Section 8 looks at the survey results gauging how users are satisfied with the different features of the four financial web portals being compared in this study. Section 9 summarizes the study with a concluding discussion.