New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic

Abstract In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one.

[1]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[2]  C. Prem Sankar,et al.  Trust Based Stock Recommendation System – A Social Network Analysis Approach☆ , 2015 .

[3]  Balu Santhanam,et al.  A Hybrid ICA-SVM Approach to Continuous Phase Modulation Recognition , 2009, IEEE Signal Processing Letters.

[4]  Ping-Feng Pai,et al.  Software reliability forecasting by support vector machines with simulated annealing algorithms , 2006, J. Syst. Softw..

[5]  Ming-Chi Lee,et al.  Using support vector machine with a hybrid feature selection method to the stock trend prediction , 2009, Expert Syst. Appl..

[6]  Feifeng Zheng,et al.  Forecasting urban traffic flow by SVR with continuous ACO , 2011 .

[7]  Dan Zhang,et al.  Reversal Pattern Discovery in Financial Time Series Based on Fuzzy Candlestick Lines , 2011 .

[8]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[9]  Sergio Ortobelli Lozza,et al.  Fusion of multiple diverse predictors in stock market , 2017, Inf. Fusion.

[10]  Wei-Chiang Hong,et al.  SVR with hybrid chaotic genetic algorithms for tourism demand forecasting , 2011, Appl. Soft Comput..

[11]  Fatima Ardjani,et al.  Optimization of SVM Multiclass by Particle Swarm (PSO-SVM) , 2010 .

[12]  S. Uma Maheswari,et al.  Classification and Prediction of Stock Market Index Based on Fuzzy Metagraph , 2015 .

[13]  Babita Majhi,et al.  On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices , 2014, J. King Saud Univ. Comput. Inf. Sci..

[14]  Milad Jasemi,et al.  A CONCEPTUAL MODEL FOR PORTFOLIO MANAGEMENT SENSITIVE TO MASS PSYCHOLOGY OF MARKET , 2010 .

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

[16]  Jalil Heidary Dahooie,et al.  Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick , 2015, Expert Syst. Appl..

[17]  Shouyang Wang,et al.  A Comprehensive Look at the Predictive Information in Japanese Candlestick , 2012, ICCS.

[18]  Khaled Almejalli,et al.  GA-based learning for rule identification in fuzzy neural networks , 2015, Appl. Soft Comput..

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

[20]  Zongben Xu,et al.  Three improved neural network models for air quality forecasting , 2003 .

[21]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[22]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[23]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[24]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[25]  A. Pazos,et al.  Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification , 2013, TheScientificWorldJournal.

[26]  Mauro Roisenberg,et al.  Optimization of neural networks through grammatical evolution and a genetic algorithm , 2016, Expert Syst. Appl..

[27]  A. Murat Ozbayoglu,et al.  TN-RSI: Trend-normalized RSI Indicator for Stock Trading Systems with Evolutionary Computation , 2014, Complex Adaptive Systems.

[28]  Thomas Bartz-Beielstein,et al.  SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization , 2010, ArXiv.

[29]  Cheng-Jian Lin,et al.  3D reconstruction and face recognition using kernel-based ICA and neural networks , 2011, Expert Syst. Appl..

[30]  M. H. Abooie,et al.  A Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing: The Candlestick Technical Analysis , 2016 .

[31]  Milad Jasemi,et al.  A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick , 2011, Expert Syst. Appl..

[32]  Wei-Chiang Hong,et al.  An Improved Neural Network Model in Forecasting Arrivals , 2005 .

[33]  Kimon P. Valavanis,et al.  Surveying stock market forecasting techniques - Part II: Soft computing methods , 2009, Expert Syst. Appl..