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We utilize the unsupervised and supervised learning algorithms of the BayesiaLab software package to automatically generate Bayesian networks from daily stock returns over a sixyear period. We will examine 459 stocks from the S&P 500 index, for which observations are available over the entire timeframe. We selected the S&P 500 as the basis for our study, as the companies listed on this index are presumably among the bestknown corporations worldwide, so even a casual observer should be able to critically review the machinelearned findings. In other words, we are trying to machinelearn the obvious, as any mistakes in this process would automatically become selfevident.
In addition to generating humanreadable and interpretable structures, we want to illustrate how we can immediately use machinelearned Bayesian networks as “computable knowledge” for automated inference and prediction. Our objective is to gain both a qualitative and quantitative understanding of the stock market by using Bayesian networks. In the quantitative context, we will also show how BayesiaLab can reliably carry out inference with multiple pieces of uncertain and even conflicting evidence. The inherent ability of Bayesian networks to perform computations under uncertainty makes them highly suitable for a wide range of realworld applications.
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