A dynamic threshold decision system for stock trading signal detection

Trading signal detection has become a very popular research topic in financial investment area. This paper develops a model using the Piecewise Linear Representations (PLR) and Artificial Neural Networks (ANNs) to analyze the nonlinear relationships between the stock closed price and various technical indexes, and uncovering the knowledge of trading signals hidden in historical data. Piecewise Linear Representation tools are applied to find the best stock turning points (trading signals) based on the historical data. These turning points represent short-term trading signals for selling or buying stocks from the market. This study further applies an Artificial Neural Network model to learn the connection weights from these historical turning points, and afterwards an exponential smoothing based dynamic threshold model is used to forecast the future trading signals. The stock trading signal is predicted using the neural network on a daily basis. The dynamic threshold bounds generated provide a guide for triggering a buy or sell decision when the ANN-predicted trading signal goes above or under the threshold bounds. Through a series of experiments, this research shows superior results than our previous research (Chang et al., 2009 [1]) and other benchmark researches.

[1]  Pei-Chann Chang,et al.  Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry , 2009, Knowl. Based Syst..

[2]  N. Mironova,et al.  Near infrared absorption spectra in Co3O4 , 1994 .

[3]  A. Koehler,et al.  Exponential Smoothing Model Selection for Forecasting , 2006 .

[4]  Frank Westerhoff,et al.  TECHNICAL ANALYSIS BASED ON PRICE-VOLUME SIGNALS AND THE POWER OF TRADING BREAKS , 2006 .

[5]  Alberto Ferreira de Souza,et al.  Prediction-based portfolio optimization model using neural networks , 2009, Neurocomputing.

[6]  S. Achelis.  Technical analysis a to z , 1994 .

[7]  Huanhuan Chen,et al.  Evolving Least Squares Support Vector Machines for Stock Market Trend Mining , 2009, IEEE Trans. Evol. Comput..

[8]  E. S. Gardner.  EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[9]  Michele Marchesi,et al.  A hybrid genetic-neural architecture for stock indexes forecasting , 2005, Inf. Sci..

[10]  Azuma Ohuchi,et al.  Market micro-structure analysis by multiagent simulation in X-Economy -- comparison among technical indices , 2005, Inf. Sci..

[11]  Ching-Hsue Cheng,et al.  A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting , 2010, Inf. Sci..

[12]  Yi-Fan Wang,et al.  On-Demand Forecasting of Stock Prices Using a Real-Time Predictor , 2003, IEEE Trans. Knowl. Data Eng..

[13]  Pei-Chann Chang,et al.  Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  Jar-Long Wang,et al.  Trading rule discovery in the US stock market: An empirical study , 2009, Expert Syst. Appl..

[15]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[16]  Byung Ro Moon,et al.  A Hybrid Neurogenetic Approach for Stock Forecasting , 2007, IEEE Transactions on Neural Networks.

[17]  Y. Ikuno,et al.  Application of an Improved Genetic Algorithm to the Learning of Neural Networks , 1994 .

[18]  Ramazan Gençay,et al.  Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules , 1999 .

[19]  T. Martin McGinnity,et al.  Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms , 2006, IEEE Transactions on Fuzzy Systems.

[20]  Xiaohu Yang,et al.  A novel piecewise linear segmentation for time series , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[21]  Ping-Feng Pai,et al.  System reliability forecasting by support vector machines with genetic algorithms , 2006, Math. Comput. Model..

[22]  Yuri Kalnishkan,et al.  Weighted Kernel Regression for Predicting Changing Dependencies , 2007, ECML.

[23]  Héctor Pomares,et al.  Hybridization of intelligent techniques and ARIMA models for time series prediction , 2008, Fuzzy Sets Syst..

[24]  Kunio Kashino,et al.  A Quick Search Method for Audio Signals Based on a Piecewise Linear Representation of Feature Trajectories , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

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

[26]  W. Z. Abidin,et al.  The evolution of services science , 2008, 2008 International Conference on Service Systems and Service Management.

[27]  Russell L. Purvis,et al.  Stock market trading rule discovery using technical charting heuristics , 2002, Expert Syst. Appl..

[28]  Haibo He,et al.  Self-organizing learning array and its application to economic and financial problems , 2007, Inf. Sci..

[29]  Juan Julián Merelo Guervós,et al.  Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters , 2007, Inf. Sci..

[30]  Michael A. H. Dempster,et al.  Computational learning techniques for intraday FX trading using popular technical indicators , 2001, IEEE Trans. Neural Networks.

[31]  Russell L. Purvis,et al.  Trading With a Stock Chart Heuristic , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[32]  Kay Chen Tan,et al.  Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks , 2008, IEEE Transactions on Neural Networks.

[33]  Pei-Chann Chang,et al.  A TSK type fuzzy rule based system for stock price prediction , 2008, Expert Syst. Appl..

[34]  Andrew W. Lo,et al.  Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation , 2000 .

[35]  Volatility: A Hidden Markov Process in Financial Time Series , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  Ah Chung Tsoi,et al.  Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference , 2001, Machine Learning.

[37]  Zehong Yang,et al.  Intelligent stock trading system by turning point confirming and probabilistic reasoning , 2008, Expert Syst. Appl..

[38]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[39]  Y. Ong,et al.  An empirical study of Genetic Programming generated trading rules in computerized stock trading service system , 2008, 2008 International Conference on Service Systems and Service Management.

[40]  C. Granger,et al.  Efficient Market Hypothesis and Forecasting , 2002 .

[41]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[42]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[43]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

[44]  D. W. Trigg,et al.  Exponential Smoothing with an Adaptive Response Rate , 1967 .

[45]  Pei-Chann Chang,et al.  A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[46]  William Leigh,et al.  Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike , 2005, Expert Syst. Appl..

[47]  Marcelo Portes Albuquerque,et al.  Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods , 2009, Expert Syst. Appl..

[48]  Russell L. Purvis,et al.  Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support , 2002, Decis. Support Syst..