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Deep Probabilistic Models for Returns Prediction
Welcome to this in-depth tutorial on Deep Probabilistic Models for Asset Returns, where we will explore the application of Bayesian networks and variational inference in uncertain market conditions. In this tutorial, we will leverage the power of probabilistic modeling to analyze and predict asset returns, a crucial task in the field of finance and investment.
Financial markets are inherently uncertain and volatile, making it challenging to predict asset returns accurately. Traditional models often struggle to capture the complex dependencies and uncertainties present in market data. Deep Probabilistic Models offer a more flexible and robust framework for modeling financial data by incorporating uncertainty into the prediction process.
In this tutorial, we will focus on building a Bayesian network to model asset returns and use variational inference to estimate the posterior distribution of the model parameters. We will use real financial data downloaded using the yfinance library to train and evaluate our model. By the end of this tutorial, you will have a solid understanding of how to apply deep probabilistic models to analyze and predict asset returns in uncertain market conditions.
