Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index

By An-Sing Chen, Mark T. Leung, Hazem Daouk. 2003

524 citations recorded by the source corpus.

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

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Citations in the corpus

Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, methodology): “Chen et al. [5] developed a method that uses artiﬁcial neural network model to predict the Taiwan Stock Index.”
Surveying stock market forecasting techniques - Part II: Soft computing methods (2009, methodology): “Akaike's minimum final prediction error (FPE) is used as a performance measure by Chen et al. (2003).”
Evolving Least Squares Support Vector Machines for Stock Market Trend Mining (2009, methodology): “In all neural network applications, multi-layer feedforward neural network (MLFNN) is used most frequently for stock market price prediction [1, 6-12].”
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2011, methodology): “In the light of previous studies, it is hypothesized that various technical indicators may be used as input variables in the construction of prediction models to forecast the direction of movement of the stock price index (Chen et al., 2003).”
A TSK type fuzzy rule based system for stock price prediction (2008, methodology): “Chen, Leung, and Daouk (2003) used a sliding window to predict the next day’s price of the index.”
Correlating financial time series with micro-blogging activity (2012, methodology): “We simulate daily trading [5, 22, 25] of stocks and try to predict the final price on each day of the simulation.”
Simultaneous optimization of artificial neural networks for financial forecasting (2012, result): “These experimental results are in line with prior studies [6, 43, 48].”
Automated trading with performance weighted random forests and seasonality (2014, background): “, 1995; Enke and Thawornwong, 2005), Japanese (Kamijo and Tanigawa, 1990) and many other stock prices as well as a multitude of other financial assets (Chen et al., 2003; Cheng et al., 1996).”
A Hybrid Machine Learning System for Stock Market Forecasting (2008, background): “Studies have also shown that predicting direction as compared to value can generate higher profits [1].”
Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach (2010, methodology): “(17) has been optimized via various techniques (e.g. statistical regression, stochastic regression, neural networks, fuzzy systems, support vector machines and evolutionary computations) for stock price modeling and prediction (Ang & Quek, 2006; Saad, Prokhorov, & Wunsch, 1998) , forecasting of fina”
Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2009, background): “One significant advantage of the ANN models over other classes of nonlinear models is that ANNs are universal approximators that can approximate a large class of functions with a high degree of accuracy [11].”
Stock market value prediction using neural networks (2010, methodology): “So in this paper the value of the stock market is normalized into the range of [-1, 1] using the (1) and then the neural networks are trained and tested using the back propagation algorithm.”