A 3D Stock Heatmap for Virtual Reality

Abstract

With the increasing complexity of stock markets, there is a growing need for more intuitive data visualization to aid the decision-making of stock market participants. Traditional two-dimensional (2D) stock visualizations, such as tree- and heatmap-based visualization, also known as stock heatmaps, can be effective in representing stock market data, though only in showing one new metric at a time from a range of metrics like market capitalization, price-earnings ratio, revenue, earnings per share, average volume, and year-to-date change. In the fast-paced stock environment, viewing additional metrics would provide a more holistic view of the stock and enable informed decision-making. By visualizing the data in three dimensions, market participants would be able to see additional data and easily identify correlations and anomalies that may not be apparent in traditional 2D stock heatmaps. This study aims to explore the feasibility of transforming 2D heatmaps into three-dimensional (3D) heatmaps by allowing more metrics to be included to enhance understanding of stock market trends and patterns. Using key stock metrics, including market capitalization (MCAP), earnings per share (EPS), and change in stock price (YTD), a 3D stock heatmap was developed using Python and HTML and was tested in virtual reality environments. The initial findings suggest that a 3D stock heatmap could give investors and financial advisors an enhanced perspective, like viewing twice as many metrics and reducing the time spent on charts by half that they had spent previously. This will allow for better and more comprehensive decision-making, as the developed 3D stock heatmap provides a more holistic view of stocks. This will enable stock market participants to better analyze the data and, through these insights, make better financial decisions.

Keywords

• data visualization
• decision-making
• heatmap
• virtual reality
• data analysis
• data management