Data Mining Techniques for Stock Market Analysis and Prediction

“Data is a precious thing and will last longer than the systems themselves.” – Tim Berners-Lee, inventor of the World Wide Web.

Analysis-based stock market prediction is essentially important since it can deal with the huge amount of money and stock buyers need a systematic and more accurate prediction. Because of high-risk investments, companies and individuals use different types of algorithms to predict the stock market. With the rising number of factors on global economy and the revolution of information technology, the number of financial data gets generated and accumulated forever at an extraordinary speed. Consequently, there is a compelling need for IT-based methods for monitoring massive amount of financial data to support companies and individuals before they take any strategic decision and investment planning.

The economy of every country is linked with the performance of stock markets. And also, apart from companies, common people are also interested to buy shares as a small investment. Consequently, analyzing stock market is not only related to macroeconomic parameters but also associated with everyday life. Therefore, there must be a systematic approach to reduce the uncertainty in stock market. Nowadays data mining has become one of the important technologies among various sectors and non-business organizations. Data mining techniques rely on data collection and warehousing with the support of computer processing. With these techniques, we can get the benefits of reduced cost, increased revenue, and stock market awareness. Applying data mining techniques for stock market analysis and prediction reveals some useful tips and predicts trends in the future and behaviours in stock markets. There are various data mining techniques in the world. Let’s study five key data mining techniques for analysis and prediction.

Neural Network in Stock Market

The neural network, one of the efficient data mining techniques, is used by companies for almost one decade. This technique is successfully applied in various learning applications for understanding a subject as per the way how our brain functions work. As it is a computational method, it is highly effective during this digital era. At the same time, it combines the features of brain functions and the structure of digital content. The key benefit of this method is that it can simultaneously forecast selling and buying signs with the prediction of future trends and decision-making tips for stock investors. There are various neural network-based architectures such as convolutional neural network, recurrent neural networks, multilayer perception and long short-term memory for predicting the stock price of companies.

Advantages of Neural Networks

  • Neural networks can work beyond the input and can produce the output.
  • The loss of data does not affect its functions since the data is stored in networks.
  • These networks can keep learning by observing examples.
  • We can get multi-skilled work from these networks.
  • Every piece of information is useful in the network.
  • These networks store information on the entire network.
  • Cost and time benefit. You can not only save money and time but also have your work done faster and error-free with quality and accuracy in results.

Decision Tree in Stock Market

Decision tree method is the best method for making decisions as it can deal with a lot of complex information and it is as simple as a ‘flowchart’ to assist you in making conclusions. It is used for building stratification and regression models to be used in data mining and trading. With the effective structure of decision tree, we get empowered to take systematic decisions with an accurate, balanced picture of the risks and rewards. When it comes to decision tree technique, the root of a decision tree is based on simple questions and related answers. Also, each question leads you to study another set of questions. Hence, this method is a simple technique for solving complex problems.

Advantages of Decision Tree

  • It works without the need of normalization of data.
  • It works without the need of scaling of data.
  • It is a simple method which can be easily explained to any technical team
  • It needs less effort for data preparation compared to other methods.
  • It makes us curious to take any decision as it is based on questions and answers.
  • It requires less training period.


Factor Analysis in Stock Market

This type of analysis is mainly useful in contexts in which a few common causes of variation determine a huge number of variables. Recently research on factor analysis concludes that a large number of information can be successfully summarized by a small number of approximate factors. It is a method which depends on a statistical approach and explains fluctuations among variables. Generally, there are four types of factor analysis such as exploratory factor analysis, confirmatory factor analysis, multiple factor analysis and generalized procrustes analysis.

Advantages of Factor Analysis

  • We can use both subjective and objective attributes.
  • We can use this algorithm to recognize the hidden dimensions which sometimes may not be available from direct analysis.
  • We can get accurate results using this algorithm.
  • It can be used to identify hidden constructs which may or may not be necessary from direct analysis.
  • It is not complicated to do, but it is low-cost and accurate.

Clustering in Stock Market

Clustering, an effective method for data analysis, solves problems of classification. The key objective this method is to classify data into groups. When it comes to clustering technique, algorithms that search for groups of records play a major role. Clustering helps single out important features that distinguish various groups.

Advantages of Clustering

  • It is very simple to implement.
  • We can use this method for large datasets.
  • It guarantees convergence.
  • It helps to generalize different clusters in one form.


Association Rules in Stock Market

Ability to predict association between buyers is very critical for market dealers or investors to boost their profits. Decision-making as such as whether to sell, buy or hold shared for investor in stock market is also a challenging task. This type of data mining algorithm determines the associations among factors in the database. For instance, this technique explores how buyer X is related to buyer Y and for decision makers before investment. The association rule discovers hidden patterns in the largest dataset of various domains. 

Advantages of Association Rule

  • It is appropriate for low cardinality sparse transaction database.
  • With lease memory consumption, we can use this algorithm.
  • It is very easy to implement.
  • We can eliminate repeated database.
  • Finding association is a more important task for marketers.

The more data we collect from customers the more analysis and prediction we can get. And the more accurate prediction we have the more revenue we can generate. Hence when it comes to stock market prediction, data collected from customers plays a major role. In view of the above techniques, we have concluded that by using data mining techniques, it is possible to obtain data with accuracy and reliability, which provide share buyers result-oriented solutions on investing their valuable money. Data mining, the method of uncovering consequential trends and patterns, sifts through large amounts of financial data stored. With the support of technologies of pattern recognition, and methods of statistical and mathematical techniques, the process of data mining turns raw data into useful information. Hence, either companies or individuals can learn more about public listed companies so that they can develop effective strategies before any investment.


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