Artificial Intelligence - LTSM for Financial Modeling - Part I

I decided that it might be fun and interesting to see how AI might be used to optimize a stock portfolio.

Surely there is a ton of work and effort in the context of financial markets, right?

My search took me to an IEEE published research paper from 3 fellows who worked together at the Department of Data Science in Praxis Business School, based in Kolkata, India. 

Below is the link to this paper, which is a PDF.

Stock Portfolio Optimization Using a Deep Learning LSTM Model

In reading the Abstract of this paper, I can see that the Long Short-Term Memory Model is used. I was interested in the source code for this project, but couldn't figure out where it was located, so I decided instead to read up on LSTM.

Reading up on LSTM, I started to realize that LSTM was a preferred model for most Financial AI. 

Learning about LSTM requires a foundation and subsequent building blocks of knowledge, such as the topic of Recurrent Neural Networks (for starters). You have to start wading into the pool, with a level of comfort and confidence as the sub-topics materialize.

A link - the first link I read - on LSTM, is found here.

Understanding-LSTMs

I like this blog article, because it addresses the "Core Idea on LSTMs", and a "Step by Step LTSM Walkthrough". I couldn't help but notice that these models look, essentially, like State Transition Diagrams to me. State Transition is a key part of Artificial Intelligence I am realizing. And the diagrams start to look very electronic. Check out an electrical circuit diagram full of Transistors and Logic Gates, and you will see resemblance.

While this article was very helpful from a big-picture conceptual perspective, I got very confused by the fact that the diagrams showed both a "tanh" function and a "sigmoid" function. The symbol for sigmoid, I was familiar with. But the tanh left me scrambling to figure out what that was all about (it helps to be a math geek when you are delving into Artificial Intelligence). Here is a snippet of the diagram that sent me down this road of investigation:

Here is an article I found that allowed me to understand what "tanh" is: sigmoid-vs-tanh-functions

From this blog article, I went and read a second paper on LTSM Models written by Akhter Rather, found at the following url:

LSTM-based Deep Learning Model for Stock Prediction and Predictive Optimization Model

So from here, I decided I wanted to see this LSTM Model in action. I couldn't find the source code from the Kolkata publication, but I felt there were probably more of these models around, and I decided to look for one. My next blog post will cover that.