Stock Price Prediction using Machine Learning

Published on 03 Jul 2021. Written by Amitesh Kumar

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Stock market prediction is the way of predicting future prices and values of the companies. This application will give investors more confidence to invest in a particular company. By using this application, the investors can keep track of the profits and losses in the stocks. The application is developed through a machine learning model and is used to predict stock prices.

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What is machine learning?

It is an application of Artificial Intelligence, which was initially used only by high profile organizations to let the machines learn. Nowadays, it has become a common aspect as it is used in every field to understand the patterns and predict possible outcomes. The stock price predictor is based on the machine learning model as the results are based on the pattern study by the application. It will be beneficial to small-scale investors.

Concepts behind this project

The project is based on two main algorithms which are Particle swarm optimization (PSO) and least square support vector machine (LS-SMV). The PSO algorithm is based on the stochastic optimization technique, and it is similar to the Genetics Algorithm. The LS-SMV is a type of method of analyzing data and recognizing patterns that are related to machine learning methods.

Project Implementation

As a good alternative to the Artificial Neural Networks (ANNs), the SVMs is proved to be better for predicting results. It works on the machine learning method of recognizing patterns and data. It will help to study the variable pattern of data of the stock prices. This algorithm works on optimizing the problem into simpler equations. Then there is a need for search algorithm application to search data for the LS-SVM.

The PSO algorithm is the answer, as it searches the data and optimizes it for the SVM. The PSO is popular in various fields for its easy implementation and ease of access. Optimization by PSO is gained by searching for relevant data about the numerous stock prices of the companies. After having these, both implemented the stock price predictor will be good to work.

The model of the processing shows the outputs from the six inputs vectors. The processing starts with the Data acquisition, which is based on Stocks' historical data. The next step will do the process of Feature extraction, which is done by Technical Indicators. After this, the process will move to Optimize and training through both the algorithms. Then the step is testing the LS-SVM-PSO model with new data and then at the end, computing the MSE.

Highlights of the project

One will get to learn both the algorithm LS-SVM-PSO and also will be able to learn the methods of machine learning. The algorithms are a major part of the project, and both can be implemented in any field to study data.

Conclusion

The PSO is projected to work in the optimization of LS-SVM in the prediction of stocks. After studying millions of data and its patterns through numerous variations of stocks, the application will give approximate results. Investments are the future, and stock predictors will play an important role in it.

Kit required to develop Stock Price Prediction using Machine Learning:

• NA

Technologies you will learn by working on Stock Price Prediction using Machine Learning:

• Machine Learning

Any Questions?