Keyword Analysis & Research: data mining models applied to stock markets

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What is the application of data mining techniques in stock market?

This paper provides an overview of application of data mining techniques such as decision tree, neural network, association rules, factor analysis and etc in stock markets. Also, this paper reveals progressive applications in addition to existing gap and less considered area and determines the future works for researchers.

What is a data mining model?

The mining model is more than the algorithm or metadata handler. It is a set of data, patterns, statistics that can be serviceable on new data that is being sourced to generate the predictions and get some inference about the relationships. The following are some of the techniques that are used in data mining. 1. Descriptive Data Mining Technique

What is predictive data mining?

Predictive Data Mining Technique The main objective of the predictive mining technique is to identify futuristic results instead of the current tendency. Many functions are used for the prediction of the target value. The techniques that fall under this category are classification, regression and time-series analysis.

What technologies are used to predict the stock market?

The paper first looks at the different technologies applied in stock market prediction. It examines how sentimental analysis, decision trees, moving average algorithm, and data mining is applied in various stock prediction scenarios.

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