Implementing Logistic Regression for Stock Trading

Most stock trading algorithms that incorporate machine learning are based upon some form of linear regression. There are benefits and drawbacks to this method. The benefit of this is that the predicted prices of linear regression can be integrated into more complex values, that need the actual price values to function. The drawback is that for the basic "buy low, sell high" strategy, it is not directly related to predicting the direction of the price. What would happen if we used logistic regression, or more specifically binary classification, to predict if the price will increase or decrease? Theoretically, it would hone in on direction itself, and become more accurate than the signals generated by linear regression.