The use of data mining and neural networks for forecasting stock market returns

By David Enke, Suraphan Thawornwong. 2005

483 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

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Citations in the corpus

Statistical Arbitrage in Cryptocurrency Markets (2019, background): “This approach is promising, as classiﬁcation problems have found to work better than regression problems in the context of ﬁnancial market predictions (Enke and Thawornwong 2005; Leung et al. 2000).”
Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis (2010, methodology): “…the stock market and the increasing need for more efficient forecasting models, the AI school, operating without the limitation of such an assumption, outperforms the conventional statistical methods experimentally (Enke & Thawornwong, 2005; Hansen & Nelson, 2002; Ture & Kurt, 2006; Zhang, 2003).”
A Multiple-Criteria Approach for Forecasting Stock Price Direction : Nonlinear Probability Models with Application in S & P 500 Index (2016, methodology): “) have been utilized in forecasting of financial problems [9-13].”
On stock return prediction with LSTM networks (2017, methodology): “8 Industrial Average Index data between 1963 and 1988.6 Enke (2005) uses a feedforward neural network model together with a data mining7 framework in order to investigate predicting power of the input variables.”
A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick (2011, methodology): “(2006) using a bootstrap methodology find candlestick trading strategies valueless for Dow Jones Industrial Average stocks over the 1992–2002 period or Horton (2009) by investigating eight signals for 349 stocks, finding no value in the use of candlesticks. But on the other hand Lee and Jo (1999) de”
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010, methodology): “, (2005) [53] S&P 500 index data GA-based SVM 18 Technical indexes GA Enke and Thawornwong (2005) [10] S&P 500 stock index Linear regression model, BPNN, GRNN, PNN 31 Financial and economic variables Information gain”
Intelligent technical analysis based equivolume charting for stock trading using neural networks (2008, background): “Many researhers have utilized various types of neural networks for technical analysis and stock market prediction (Chenoweth, Obradovic, & Stephenlee, 1996; Enke & Thawornwong, 2005; Kim & Han, 2000; Kuo, Li, Cheng, & Ho, 2004; Leigh, Hightower, & Modani, 2005; Saad, Prokhorov, & Wunsch, 1998).”
Automated trading with performance weighted random forests and seasonality (2014, background): “Since then, a plethora of research has attempted to find predictive rules for US (Refenes, Zapranis, & Francis, 1995; Enke & Thawornwong, 2005), Japanese (Kamijo & Tanigawa, 1990) and many other stock prices as well as a multitude of other financial assets (Chen, Leung, & Daouk, 2003; Cheng,…”
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2017, methodology): “We construct a classiﬁcation instead of a regression problem, as the literature suggests that the former performs better than the latter in predicting ﬁnancial market data (Leung et al., 2000; Enke and Thawornwong, 2005).”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, background): “[110] Stock market returns forecasting GRNN with B & H strategy, PNN level estimation NN, LR and T-bill ANN performs better”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, background): “Other useful variables: According to diﬀerent papers, other variables including Treasury bill rates, The term and default spreads have shown to be useful in stock market prediction (Zhong & Enke, 2017; Niaki & Hoseinzade, 2013; Enke & Thawornwong, 2005).”