How to build a model which can predict the probability of an event based on a set of timeseries data?
Since you have features that would be handled best with a recurrent neural net, AND some features that would be handled best with a feedforward net, what you can actually do is both and feed them into a main Dense layer which has a softmax output to give you the probability distribution. 

This would be rather hard to do by hand, but luckily you are using Keras, which allows for this kind of modeling rather easily!

In the Keras functional API guide https://keras.io/getting-started/functional-api-guide/, there is a model actually very similar to what you are looking for, where the "Main" information is an LSTM layer (which you'd do for the stock prices), and the "Auxiliary" information would be (Name of entity, Type Of entity , Size , Country, Specific Attributes) etc...

The model looks like this:


The example model actually uses 2 loss functions (2 outputs), but you can easily build it to only have the one output. The code is all there so will be easy to replicate.

I basically use this kind of model for almost everything now and get great results, vs just LSTM alone.