Enhanced stock price variation prediction via DOE and BPNN-based optimization

Abstract Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always determines the parameter settings of the NNs rationally through a trial-and-error methodology. This paper integrates design of experiment (DOE), Taguchi method, and back-propagation NN (BPNN) to construct a robust engine to further optimize the prediction accuracy under a robust DOE-based predictor. Adopting data from Taiwan Stock Exchange (TWSE), the technical analytical indexes and β value of the listed stocks of TWSE were computed. The research results indicated that the proposed approach can effectively improve the forecasting rate of stock price variations.

[1]  B. S. Lim,et al.  Optimal design of neural networks using the Taguchi method , 1995, Neurocomputing.

[2]  Shun-ichi Amari,et al.  Network information criterion-determining the number of hidden units for an artificial neural network model , 1994, IEEE Trans. Neural Networks.

[3]  Holger R. Maier,et al.  Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study , 1998 .

[4]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[5]  Terence Tai-Leung Chong,et al.  Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30 , 2008 .

[6]  Wen-Chin Chen,et al.  A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN , 2010, Expert Syst. Appl..

[7]  Hong Wang,et al.  Predicting stock index increments by neural networks: The role of trading volume under different horizons , 2008, Expert Syst. Appl..

[8]  Elaine Loh,et al.  An alternative test for weak form efficiency based on technical analysis , 2007 .

[9]  Marizete Silva Santos,et al.  Using factorial design to optimise neural networks , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[10]  David B. Fogel.  An information criterion for optimal neural network selection , 1991, IEEE Trans. Neural Networks.

[11]  Shu-Hsien Liao,et al.  Mining stock category association and cluster on Taiwan stock market , 2008, Expert Syst. Appl..

[12]  Chenn-Jung Huang,et al.  Application of wrapper approach and composite classifier to the stock trend prediction , 2008, Expert Syst. Appl..

[13]  Melvin J. Hinich,et al.  Nonlinear event detection in the Chilean stock market , 2007 .

[14]  Baikunth Nath,et al.  A fusion model of HMM, ANN and GA for stock market forecasting , 2007, Expert Syst. Appl..

[15]  Wen-Chin Chen,et al.  Process parameter optimization for MIMO plastic injection molding via soft computing , 2009, Expert Syst. Appl..

[16]  David Enke,et al.  The use of data mining and neural networks for forecasting stock market returns , 2005, Expert Syst. Appl..

[17]  Cheng-Lung Huang,et al.  A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting , 2009, Expert Syst. Appl..

[18]  Hassan Tehranian,et al.  Aggregate Earnings, Firm-Level Earnings, and Expected Stock Returns , 2008, Journal of Financial and Quantitative Analysis.

[19]  Theodore B. Trafalis,et al.  Kernel principal component analysis and support vector machines for stock price prediction , 2007 .

[20]  Colin MacBeth,et al.  Effects of Learning Parameters on Learning Procedure and Performance of a BPNN , 1997, Neural Networks.

[21]  Bernard Widrow,et al.  Neural networks: applications in industry, business and science , 1994, CACM.

[22]  Po-Chang Ko,et al.  Resource allocation neural network in portfolio selection , 2008, Expert Syst. Appl..

[23]  Ingoo Han,et al.  Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index , 2000 .

[24]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[25]  Wen-Chin Chen,et al.  A neural-network approach for an automatic LED inspection system , 2007, Expert Syst. Appl..

[26]  S. Harrington,et al.  All Events Induce Variance: Analyzing Abnormal Returns When Effects Vary across Firms , 2002, Journal of Financial and Quantitative Analysis.

[27]  Pei-Chann Chang,et al.  A neural network with a case based dynamic window for stock trading prediction , 2009, Expert Syst. Appl..

[28]  Rosa Rodríguez,et al.  On the Economic Link Between Asset Prices and Real Activity , 2007 .

[29]  David Enke,et al.  Intelligent technical analysis based equivolume charting for stock trading using neural networks , 2008, Expert Syst. Appl..

[30]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[31]  Kun-Huang Huarng,et al.  A bivariate fuzzy time series model to forecast the TAIEX , 2008, Expert Syst. Appl..

[32]  Leonard M. Lye,et al.  Combined use of dimensional analysis and modern experimental design methodologies in hydrodynamics experiments , 2009 .

[33]  Kyungjik Lee,et al.  Expert system for predicting stock market timing using a candlestick chart , 1999 .

[34]  Ingoo Han,et al.  An evolutionary approach to the combination of multiple classifiers to predict a stock price index , 2006, Expert Syst. Appl..

[35]  Tae Hyup Roh.  Forecasting the volatility of stock price index , 2007, Expert Syst. Appl..

[36]  B. Chen,et al.  An ANN-based element extraction method for automatic mesh generation , 2005, Expert Syst. Appl..

[37]  F. P. Schadler,et al.  Are the AAII stock screens a useful tool for investors , 2008 .

[38]  David Enke,et al.  A Hybrid Derivative Trading System Based on Volatility and Return Forecasting , 2008 .

[39]  David J. Wright,et al.  Analysis of the Interest Rate Sensitivity of Common Stocks , 2007 .