Regresar

Stock Markets Risk Analysis Performance by Self Organized Maps AI Techniques: Comparison and Implementation of Three SOM Methods

Abstract:

Despite the exponential increase in the use of AI tools, the financial field has become a target just in the latest years. The stock markets meant a decisive factor for economic growth as it works as a management mechanism for money generated by the industrial force of the countries. In order to obtain the improved algorithm, this work focus on establishing the best SOM architecture for stock market treatment in an initial step. Therefore, after the literature review, the data extraction was performed using Yahoo Finance open source to get the historical data of the selected financial index. The ISOM SP40 proposed in this work uses an adequate combination of hexagonal SOM architecture and neighbor function based on Manhattan distance. Moreover, two SOM methods more denominated SOM IBEX35 and SOM NYSE were tested by the same conditions for compare, and determinate the best scenario for SP Latin America 40 data set. Thus the risk investment was analyzed with density correlations of profit, industrial area, and geography detected with an 80% of success rate using the top 9 companies in the stock index, also it was verified in a time-frequency analysis developed here with the top 6 companies reference companies from 2014–2019. The training time in the proposed ISOM SP40 method also improves two decimal places in comparison with the other tested techniques. In this sense, there is appropriated to establish that the improved algorithm was found, and it succeeds in the adaptation to SP Latin America 40 index data set.