Feature selection method explanation
The idea is quite simple: If you find features that are correlated with your target variable but uncorrelated with each other, it will give you information with which you can predict your target variable.

The threshold of 0.5 is chosen by a person and should be changed depending on your data. For example, if you find 10 features that are uncorrelated with each other but all have a 0.3 absolute correlation coefficient with the S&amp;P 500 than you found the stock market equivalent of the Holy Grail.

Also, as you already mentioned, the correlation coefficient only captures linear correlations. This could cause you to exclude strongly related features just because they have a more complex relationship to your target variable.

Therefore, I would strongly advise against this method. Instead, I recommend plotting the data in pairplots.