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A review of data mining methods in financial markets
Source: aimspress.com
Article summary:
1. The rapid growth of data in the financial market poses challenges to traditional analysis techniques, leading to an increased focus on data mining methods.
2. Most of the data in the financial market is time series data, which has inherent complexities that make it a challenge for traditional statistical models.
3. Review articles on the application of data mining methods to financial market issues are limited, with most focusing on specific algorithms or applications such as stock market forecasting. This review provides a broader overview of major data mining algorithms and their application in the financial market.
Article analysis:
The article provides a comprehensive review of data mining methods in financial markets, highlighting the importance of these methods in discovering the value of data. The authors acknowledge the challenges posed by the rapid growth of data and the limitations of traditional analysis techniques. They also emphasize the significance of financial markets in modern society and the impact of financial activities on economic development.
However, there are some potential biases in this article. Firstly, it focuses mainly on machine learning methods and does not give enough attention to statistical models. Secondly, it only covers research published from 2009 to 2015, which may not reflect recent developments in this field. Thirdly, it does not explore counterarguments or potential risks associated with using data mining methods in financial markets.
The article also lacks evidence for some claims made, such as the statement that traditional statistical methods are becoming more and more inadequate for dealing with complex financial data. Additionally, while the authors mention challenges and promising issues in this research area, they do not provide a detailed analysis or discussion of these topics.
Overall, while this article provides a useful overview of data mining methods in financial markets, readers should be aware of its potential biases and limitations. Further research is needed to fully understand the benefits and risks associated with using these methods in finance.
Topics for further research:
Criticisms of data mining methods in financial markets
Comparison of machine learning and statistical models in finance
Recent developments in data mining methods for financial analysis
Risks and limitations of using data mining in financial decision-making
Challenges of dealing with complex financial data
Impact of data mining on financial market efficiency and stability