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Title: Optimización de cartera de activos financieros aplicando aprendizaje automático
Author: Caparrini López, Antonio Javier  
Tutor: Escayola, Jordi  
Keywords: asset management
machine learning
multifactor model
Issue Date: 3-Jan-2021
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The time value of money has always driven holders of capital to invest for profitability. These investments generally seek to maximize returns while minimizing the amount of risk, which has been studied extensively. The capital markets have grown enormously over the last century and the amount of information held on companies and the market, in addition to its sheer volume, continues to grow. In order to reduce this enormous amount of data to information that can be used to make decisions, numerous studies have identified what they call factors. A factor seeks to identify a common characteristic among assets in order to identify which produce the highest returns. Currently, an increasingly common investment style are funds that replicate an index (passive management), generating exposure to a particular market (e.g. SP500) that historically as a whole has always produced returns, reducing risk through diversification, as the index is composed of numerous assets. In addition, this management style has low costs that make it attractive to investors. On the other hand, we have active management, where funds are managed in such a way that through analysis and their own criteria they seek to achieve a higher return than the market in exchange for higher management costs. Recent technological developments (both hardware and software) make it possible to solve problems and generate complex statistical machine learning models using large amounts of data. These techniques can be used to search for common patterns in the factors to automatically facilitate the analysis of the optimal assets for an investment portfolio. These models are currently used to automate the process of selecting assets called Smart Beta, which have lower costs than active management and higher returns than index investing. The purpose of this project is to use machine learning to make a model that selects, from the characteristics of the assets (reflected in the factors present in the literature), those that will have better performance to add to the portfolio.
Language: Spanish
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