Data mining in finance: advances in relational and hybrid methods

By Boris Kovalerchuk, E. Vityaev. 2000

218 citations recorded by the source corpus.

How researchers see this paper

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Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “There were published books on stock market forecasting [2], trading system development [3], practical examples of forex and market forecasting applications [4] using ML models like Artificial Neural Networks (ANNs), Evolutionary Computations (ECs), Genetic Programming (GP) and Agent-based models [5]”
Deep Learning for Financial Applications : A Survey (2020, background): “The publications in [8, 9, 10, 11] constitute some of the books that cover the implementations of soft computing models in finance.”
AI in Finance: Challenges, Techniques, and Opportunities (2021, methodology): “Another is to analyze the heterogeneous inputs by, respectively, advantaged learning techniques and feed one result to another model for the overall learning tasks [92].”
Application of data mining techniques in stock markets: A survey (2010, background): “Department of the Treasury noticed that examples and arguments available in (Kovalerchuk and Vityaev, 2000) for the application of relational data mining to financial problems produce expectations of great advancements in this field in the near future for financial applications (Boris and Evgenii, 2”
Intelligent stock trading system by turning point confirming and probabilistic reasoning (2008, methodology): “First, we utilize high-level representation (turning point and technical indicators).”