The main objective of the final project is to predict future quantities of interest, such as stock returns, bond returns, bond yields, commodity prices, currencies, and many others. What justifies the use of machine learning methods in this context is the presence of a large set of potential predictors, numbering at least 50. Traditional statistical approaches may not be as suitable for handling such a vast array of variables. You have the freedom to choose what you want to predict. The experiment should encompass both shallow learning methods (covered in class: the Fama-MacBeth panel regression approach, Machine Learning Supervised and Unsupervised, reinforcement machine learning, Ridge Regression, Lasso regression, Bridge Regression, The Elastic Net regression) and Neural Networks. Some details about material can be provided. You can find relevant code and resources readily available in open-source platforms like Python and R. In your project, you should assess predictability using both statistical and economic metrics. A statistical metric of significance is important, as well as an economic metric related to investment performance. Think of it this way: if you were managing a hedge fund based on your proposed trading strategy, you would want to convince the market that your approach has been successful relative to benchmarks. Your project should include the following components: Problem Description: Start by describing the problem you intend to explore. It is essential to support your project with citations from the finance literature, either academic or practitioner (or both). Data Description: Explain the database you are using. Additionally, provide a summary of the database through summary statistics. Experiment Outcomes: Present the results of your experiment using tables and graphs. This will help illustrate your findings effectively. Conclusion: Conclude your project by summarizing your key findings and insights. I value quality over quantity, but I kindly request that you express your ideas concisely.