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Title: Agente inversor para acciones de small cap mediante el uso de Reinforcement Learning
Author: Such Ballester, Ignacio
Tutor: Pérez Ibáñez, Rubén
Others: Benito Altamirano, Ismael  
Abstract: In the current context, the stock market has become a complex and ever-changing industry, where decision-making can be crucial for making significant profits or incurring significant losses. The evolution of investment systems has led to the incorporation of innovative machine learning techniques, such as Reinforcement Learning, which allow them to learn and adapt to market changes in real-time. Reinforcement Learning is a branch of machine learning based on the concept of reward and punishment, which aims to maximise the reward obtained through interaction with an environment. This technique has proven its effectiveness in solving complex problems and has been successfully applied in environments such as robotics and video games. In this context, the use of Reinforcement Learning in the stock market presents itself as a promising alternative for the design of optimal and profitable investment strategies in a changing and highly competitive environment. The objective of this Master’s thesis is to establish a new line of research in predicting stock values of ”small cap¸companies through the use of Deep Reinforcement Learning algorithms. The first algorithm is the Proximal Policy Optimization (PPO), where the implementation of Liu et al. [11] will be used. On the other hand, the A2C and DDPG algorithms will be employed, which have been promising according to this paper Liu et al.
Keywords: deep reinforcement learning
stock market
proximal policy optimization
Document type: info:eu-repo/semantics/masterThesis
Issue Date: 10-Feb-2024
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Appears in Collections:Bachelor thesis, research projects, etc.

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