Visualization of Quantitative Thresholds in Stocks
Technical analysis comprised by various technical indicators is a holistic way of representing price movement of stocks in the market. Various forms of indicators have evolved from the primitive ones in the past decades. There have been many attempts to introduce volume as a major determinant to determine strong patterns in market forecasting. The law of demand defines the relationship between the volume and price. Most of the traders are familiar with the volume game. Including the time dimension to the law of demand provides a different visualization to the theory. While attempting the same, it was found that there are different thresholds in the market for different companies. These thresholds have a significant influence on the price. This article is an attempt in determining the thresholds for companies using the three dimensional graphs for optimizing the portfolios. It also emphasizes on the magnitude of importance of volumes as a key factor for determining of predicting strong price movements, bullish and bearish markets. It uses a comprehensive data set of major companies which form a major chunk of the Indian automotive sector and are thus used as an illustration.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1093412Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 Wright, William. "Business visualization applications”, Computer Graphics and Applications, IEEE 17,vol. 4, pp 66-70, 1997.
 Lee, K. H., and G. S. Jo. "Expert system for predicting stock market timing using a candlestick chart." Expert Systems with Applications 16, no. 4: pp 357-364, 1997.
 Wattenberg, Martin. "Visualizing the stock market." In CHI'99 extended abstracts on Human factors in computing systems, ACM, pp. 188-189, 1999.
 Westphal, Christopher, Teresa Blaxton. "Data mining solutions: methods and tools for solving real-world problems.", "Wiley Online Library”, vol. 4,1998.
 Kargupta, Hillol, Byung-Hoon Park, Sweta Pittie, Lei Liu, Deepali Kushraj, and Kakali Sarkar. "MobiMine: Monitoring the stock market from a PDA." ACM SIGKDD Explorations Newsletter 3, no. 2: 37-46, 2002.
 Nesbitt, Keith V., Stephen Barrass, "Finding trading patterns in stock market data." Computer Graphics and Applications, IEEE 24, no. 5:pp 45-55, 2009.
 Thomas, Jim, and Pak Chung Wong. "Visual analytics." IEEE Computer Graphics and Applications 24, vol. 24, no. 5, pp. 20-21, 2004.
 Šimunić, Krešimir, "Visualization of stock market charts", 11th International Conference in Central Europe on Computer Graphics, pp: 129-132, 2003.
 Blume, Lawrence, David Easley, Maureen O'hara. "Market statistics and technical analysis: The role of volume." The Journal of Finance 49, no. 1:pp 153-181, 1994.
 Antoniou, Antonios, NurayErgul, Phil Holmes, Richard Priestley. "Technical analysis, trading volume and market efficiency: evidence from an emerging market." Applied Financial Economics 7, no. 4:pp 361-365, 1997.
 Acker, Daniella, Mathew Stalker, Ian Tonks, "Daily closing inside spreads and trading volumes around earnings announcements." Journal of Business Finance & Accounting 29, no. 9‐10:pp 1149-1179, 2002.
 Bollerslev, Tim, and Dan Jubinski, "Equity trading volume and volatility: Latent information arrivals and common long-run dependencies", Journal of Business & Economic Statistics 17, no. 1:pp 9-21, 1999.
 Lee, Charles, Bhaskaran Swaminathan. "Price momentum and trading volume." The Journal of Finance 55, no. 5:pp 2017-2069, 2002.
 Bessembinder, Hendrik, and Paul J. Seguin. "Price volatility, trading volume, and market depth: Evidence from futures markets",Journal of Financial and Quantitative Analysis 28, no. 01:pp 21-39, 1993.
 Lo, Andrew W., Jiang Wang. "Trading volume: definitions, data analysis, and implications of portfolio theory." Review of Financial Studies 13, no. 2:pp 257-300, 2000.
 Chordia, Tarun, and Bhaskaran Swaminathan. "Trading volume and cross‐autocorrelations in stock returns." The Journal of Finance 55, no. 2 :pp 913-935, 2002.