Uso de kernels en series tiempo para la detección de prácticas manipulativas en mercados financieros

Abstract

Intuitively, one might think that any deviation in trading data could be easily detected due to the statistical basis on which finance sciences are based. However, the markets in which financial assets are traded operate under the principle of supply and demand, as well as the principle of opportunity. Elements that make them very susceptible to price manipulation. For this reason, it is increasingly relevant to consider techniques that allow the identification of elements in financial time series that can deliver information that show whether a stock has been subject of manipulative practices or not. The use of kernels for signals decomposition and filtering in financial time series is then proposed. By using this technique elements of the time series such as power and frequency can be obtained, which can later facilitate the characterization of a stock that has been subject of fraudulent or manipulative trading. Then considering diverse machine learning techniques, achieve a timelier detection based on said characterization, particularly in dynamic and constantly evolving trading environments. For this purpose, the performance of the kernels will be contrasted against traditional techniques, choosing the most appropriate ones. In the same way, various machine learning techniques will be evaluated and the one that best learns and represents the patterns or artifacts in fraudulent operations will be chosen. Trying in this way to raise trading standards in financial markets, as well as delving into the applications that the decomposition and filtering of signals with kernels can have, not only as a data visualization tool, but also as inputs. for machine learning techniques.