Discovering golden nuggets: data mining in financial application

By Dongsong Zhang, Lina Zhou. 2004

267 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, neutral): “The authors of [7] also compared ML methods in different financial applications including stock market prediction studies.”
Deep Learning for Financial Applications : A Survey (2020, methodology): “[3] reviewed the data mining techniques including Genetic Algorithm (GA), rule-based systems, Neural Networks (NNs) preferred in different financial application areas.”
Support vector regression with chaos-based firefly algorithm for stock market price forecasting (2013, background): “…of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia c Islamic Azad University, Tafresh Branch, Young Researchers Club, Tafresh, Iran d School of Information Systems, Curtin University, Perth, WA, Australia a r t i c l e i n f o”
Identification of fraudulent financial statements using linguistic credibility analysis (2011, background): “Findings by Zhou et al. [60] came in a laboratory setting where incentives to deceive were artificial and consequences of failure were minor or non-existent.”
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, unknown): “It is suggested that data objects in each cluster should have high intra-cluster similarity within the same cluster but should have low inter-cluster similarity to those in other clusters [17].”
Big data techniques in auditing research and practice: Current trends and future opportunities (2018, unknown): “3 Zhang et al. (2015)); our work focuses more on valuable opportunities to use contemporary big data techniques in auditing.”
Intelligent financial fraud detection: A comprehensive review (2016, unknown): “For the first iteration the weights at each edge are normally determined randomly, and after the results have been calculated each weighting is adjusted slightly for the next sequence [57].”
Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, background): “Zhou and Tian (2007) suggest combining the theory of rough sets and SVM.”
Advanced Data Mining Techniques (2008, background): “Zhao (2007). A multi-objective genetic programming approach to developing Pareto optimal decision trees, Decision Support Systems 43:3, 809–826.”
A multiple-kernel support vector regression approach for stock market price forecasting (2011, unknown): “…(GARCH) (Bollerslev, 1986), artificial neural networks (ANN) (Hansen & Nelson, 1997; Kim & Han, 2008; Kwon & Moon, 2007; Qi & Zhang, 2008; Zhang & Zhou, 2004), fuzzy neural networks (FNN) (Chang & Liu, 2008; Oh, Pedrycz, & Park, 2006; Zarandi, Rezaee, Turksen, & Neshat, 2009), and…”
A Hybrid Neurogenetic Approach for Stock Forecasting (2007, background): “Digital Object Identifier 10.1109/TNN.2007.891629 Although ANNs are dominant so far in stock prediction as surveyed in [7], it is important how to find the most valuable input features and how to use them in applying them to stock prediction.”