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Stock Market Prediction


Introduction

Stocks maketh the future.

Any person who wants to retire rich needs to consider investing in the stock market. After all, there’s only so much you can earn from a mere job, and in order to live a life that you can be proud of you need to explore a future in stocks seriously.

However, one of the negative characteristics of stock markets remains the fact that they can be notoriously difficult to navigate. Stock values are essentially a function of market volatility, and this depends on a mind-boggling array of factors.

Due to the sheer vastness of the multifaceted nature of stock market prediction, it can be very difficult for the human mind to grasp all the nuances that need to be considered while predicting the values of stocks. And while we humans have been navigating the world of shares and equity for quite a long time, and rather successfully (think Mr. Buffet), in current times, the stock landscape has grown in complexity.

This is a direct result of the data economy; we currently live in an age of information explosion. Every moment we are bombarded with so much information that our brains can barely keep up. And due to this information explosion, it becomes very difficult to effectively gauge the various factors associated with stock market analysis and make valid predictions.

Tech To The Rescue

However, there’s a ray of hope. The very technology that has been responsible for the creation of this data explosion can actually be used to deliver us from the problems it has created. The use of machine learning in stock market prediction is no longer confined to the annals of sci-fi and has actually taken a quantum leap to come and reside in reality.

Only recently, Recurrent Neural Networks were utilized for successfully detecting patterns in the Standard & Poor 500, and with optimistic results. This has opened the way for a new future where stock predictions can be made using AI technology.

But what exactly are Recurrent Neural Networks, and how do they find it so simple to do a task that many humans would balk at? In order to understand this, let us take a deep dive into the depths of the technology.

AI Used | Recurrent Neural Networks

Recurrent Neural Networks are a special kind of neural network that works slightly differently from traditional neural nets. While in traditional neural networks, inputs and outputs flow independent of each other, such is not the case in recurrent neural networks. Here, the outputs of the previous stage are fed as inputs to the present stage.

Due to this property, recurrent neural networks can perform the rather intuitive operation of prediction. Let us understand this with a simple example.

Suppose you are going to predict what the next word in this particular sequence of words is going to be. Take a pause and think about how you would ideally go about doing that.

Common sense dictates that in order to predict the next word in a given sequence, you need to analyze the previous words in the sequence. This essentially translates to the fact that the present state output is determined by the inputs of the previous stage.

And this is exactly the principle behind recurrent neural networks (RNNs). Using this concept, RNNs can be used in prediction tasks such as predictive typing and stock market predictions.

Technologies Utilized

So what technologies were utilized to implement the RNN based prediction system? The following gives you a brief overview.

Tensorflow

Tensorflow is a free and open-source dataflow and differentiable programming library that has been developed by the Google Brain team. The Tensorflow library can be effectively used in creating machine learning applications.

This is the technology behind the creation of the RNN that is behind the stock market prediction system.

Crossfilter.js

Crossfilter is a library created using javascript that can be used for manipulation of row-based data. It has the capability to explore large multivariate data sets in the browser itself. This makes it particularly suitable for use in stock predictions, which involve considerable amounts of data processing.

Flask Server

Flask is primarily a web framework that has been developed using the Python language; classified as a micro-framework due to its non-dependence on particular tools and libraries, the Flask server is a lightweight and efficient method that can enable the running of applications on the client-side of things pretty efficiently.

Angular 5

Based on TypeScript, Angular 5 is an open-source front-end development platform for web applications. It has been created by the Angular Team at Google. The Angular team’s prime objective is to make it easy to create web applications.

Conclusion

Stock market prediction is tough, but with a little bit of help from RNN based predictive technology, the creation of wealth can be simplified to a large degree.

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