Neural Networks

Trees and networks

It’s been over a month since our last post and for that we must apologize. We endeavor to be more prolific, but sometimes work and life get in the way. On the work front, let’s just say we won’t have to spend as much time selling encyclopedias door-to-door, which should free up more time to dedicate to writing value-added blog posts. On the life front, we had the chance to hike several canyons in southern Utah, USA.

Not so soft softmax

Our last post examined the correspondence between a logistic regression and a simple neural network using a sigmoid activation function. The downside with such models is that they only produce binary outcomes. While we argued (not very forcefully) that if investing is about assessing the probability of achieving an attractive risk-adjusted return, then it makes sense to model investment decisions as probability functions. Moreover, most practitioners would probably prefer to know whether next month’s return is likely to be positive and how confident they should be in that prediction.

Activate sigmoid!

In our last post, we introduced neural networks and formulated some of the questions we want to explore over this series. We explained the underlying architecture, the basics of the algorithm, and showed how a simple neural network could approximate the results and parameters of a linear regression. In this post, we’ll show how a neural network can also approximate a logistic regression and extend our toy example. What’s the motivation behind showing the link with logistic regression?