Clustering and anomalies of USA stock market volatility index data
Main Article Content
Abstract
Actuality. Investing money is an important way to improve the financial condition of both an individual and society as a whole. The problem of understanding financial data and making decisions regarding the investment of money in a certain project at this moment in time is relevant. The object of the study is the process of establishing the dependence of the value of the asset on time. The subject of research is mathematical models of data processing, data clustering and anomaly detection. The purpose of this work is to develop a method for effective investment of money using data processing and analysis methods for CVOE volatility index values in the USA, determination of clusters based on actions with assets, as well as checking the presence of anomalous data. Research results. Official data of volatility index values were selected and prepared for further analysis by removing incomplete sets and further normalization. Clustering of time series was carried out and the array was divided into five homogeneous groups. Clusters determine the ranges of the volatility index, which reflect the different sentiments of investors in the market and encourage appropriate actions with assets: to sell, to wait of index increasing, to buy, to remove money from developing projects and invest in stable ones, to wait of index decreasing. Segmentation of data, application of window function, centroids for segments were determined and signal reconstruction was carried out. Data anomaly points were identified. A comparative analysis was carried out based on the results of constructed initial data, reconstructed data and reconstruction error.
Article Details
References
(2022), On investment activity, Law of Ukraine dated September 18, 1991 No. 1560-XII: as of October 10, 2022, available at: https://zakon.rada.gov.ua/laws/show/1560-12#Text
(2023), SPX | S&P 500 Index Overview | MarketWatch, URL: https://www.marketwatch.com/investing/index/spx
Wolf, Andrew (2022), Machine Learning Simplified:A gentle introduction to supervised learning, 199 p., available at: https://themlsbook.com
Maheshwari, A. (2014), “Business intelligence and data mining”, Business Expert Press, available at: https://www.amazon.com/Business-Intelligence-Data-Mining-Analytics/dp/1631571206
Yang, X. S. (2019), Introduction to Algorithms for Data Mining and Machine Learning, Academic Press, doi: https://doi.org/10.1016/C2018-0-02034-4
Fernandes, M. (2008), Statistics for business and economics, Bookboon, available at: https://bookboon.com/en/statistics-for-business-and-economics-ebook?mediaType=ebook
Vercellis, C. (2009), Business intelligence: data mining and optimization for decision making, Wiley, New York, 420 p., available at: https://www.amazon.com/Business-Intelligence-Mining-Optimization-Decision/dp/0470511397
(2023), What is FRED? | Getting To Know FRED, available at: https://fredhelp.stlouisfed.org/fred/about/about-fred/what-is-fred/
Smith, L. I. (2002), A tutorial on Principal Components Analysis, Computer Science Technical Report No. OUCS-2002-12, available at: http://hdl.handle.net/10523/7534
(2023), Chicago Board Options Exchange, CBOE Volatility Index: VIX [VIXCLS], retrieved from FRED, Federal Reserve Bank of St. Louis; Economic Research, February 3, 2023, available at: https://fred.stlouisfed.org/series/VIXCLS