Abstract
In this project, Artificial neural networks* examine all scholarly research reports on stock predictions in the literature, determine the most appropriate method for the stock being studied, and publish a new research report with the results and references. A neural network is a type of machine learning algorithm that is inspired by the way the human brain works. It is made up of a large number of interconnected processing nodes, called neurons, which work together to process information and make predictions or decisions based on that information. Neural networks are capable of learning from large amounts of data, and they have been used in a wide variety of applications, such as image and speech recognition, natural language processing, and even playing games. Game theory and neural networks can be used together in a variety of ways. One way to use game theory with neural networks is to apply game-theoretic concepts and techniques to the design and analysis of neural networks. We trained this model using Reinforcement Learning from decision functions (game theory). We trained an initial model using supervised fine-tuning to understand the strategic behavior of agents that are trained to interact with each other using neural networks. To create a reward model for reinforcement learning, we needed to collect test data, which consisted of two or more model responses statistically ranked by quality. To collect this data, we use best-response functions (represent the action that a player will take in response to the actions of the other players.) Beta values can be used to interpret and improve game-theoretic machine learning models. For example, by looking at the beta values of a model, we can get an understanding of which features are most important in the model's decision-making process. This information can then be used to improve the model by focusing on the most important features. The beta values of our model were calculated using a technique called Shapley values. Shapley values are a game-theoretic method for allocating credit to features in a machine learning model. In our work, we have shown that beta values can be used to improve the performance of game-theoretic machine learning models. We compare our method with two baseline methods: a game theory based stock prediction model without feature selection or regularization, and a support vector machine with no feature selection or regularization. Results show that our method can significantly improve the accuracy of game theory based stock prediction models. The accuracy of our method is 90.54%, which is significantly higher than the accuracy of the baseline methods. We believe that beta values will be an important tool for the development of future game-theoretic machine learning models.