| Título: | Comparison between traditional risk measurement methods and machine learning methods in Mexican equity investment funds |
| Autores: | GRECIA MARIA CORTES ESPINOSA |
| Fecha: | 2017-01-18 |
| Publicador: | CIMAT |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/masterThesis info:eu-repo/semantics/acceptedVersion |
| Tema: |
info:eu-repo/classification/MSC/Fondos de Inversión info:eu-repo/classification/cti/7 info:eu-repo/classification/cti/33 info:eu-repo/classification/cti/3304 info:eu-repo/classification/cti/120317 |
| Descripción: | Among the investment instruments available to the general public, mutual funds offer the advantage of being a diversified and managed investment instrument that, on average, produces profits above those of bank notes, a favorite investment instrument among the Mexican population. These days, online brokers offer a low cost alternative to invest in Mexican mutual funds, but without the guidance of a financial advisor and at the investor’s own risk. Any investment in a financial instrument (stocks, bonds, metals, etc.) relies on the investor’s previous knowledge on the risks and liabilities of making such investments, or on paying for the services of a professional to advise them on the adequate investments instruments to accomplish their financial goals. Different automated learning techniques and algorithms have been used in the analysis of data to discover unknown patterns of behavior in different fields, such as: engineering, medicine, biology, economy and finance. In finance, the daily price of the titles of investment instruments, such as mutual funds, are studied as time series of data. Applying known machine learning algorithms to the daily prices of Mexican mutual funds can uncover previously ignored patterns of behavior, which could help in the selection of mutual funds for new investors. |
| Idioma: | |
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