Intelligent technical analysis based equivolume charting for stock trading using neural networks

It has been long recognized that trading volume provides valuable information for understanding stock price movement. As such, equivolume charting was developed to consider how stocks appear to move in a volume frame of reference as opposed to a time frame of reference. Two technical indicators, namely the volume adjusted moving average (VAMA) and the ease of movement (EMV) indicator, are developed from equivolume charting. This paper explores the profitability of stock trading by using a neural network model developed to assist the trading decisions of the VAMA and EMV. The generalized regression neural network (GRNN) is chosen and utilized on past S&P 500 index data. For the VAMA, the GRNN is used to predict the future stock prices, as well as the future width size of the equivolume boxes typically utilized on an equivolume chart, for calculating the future value of the VAMA. For the EMV, the GRNN is also used to predict the future value of the EMV. The idea is to further exploit the equivolume potential by using a forecasting system to predict the future equivolume measurements, allowing investors to enter or exit trades earlier. The results show that the stock trading using the neural network with the VAMA and EMV outperforms the results of stock trading generated from the VAMA and EMV without neural network assistance, the simple moving averages (MA) in isolation, and the buy-and-hold trading strategy.

[1]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[2]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

[3]  M. Dacorogna,et al.  A geographical model for the daily and weekly seasonal volatility in the foreign exchange market , 1993 .

[4]  Jiang Wang,et al.  Trading Volume and Serial Correlation in Stock Returns , 1992 .

[5]  Zoran Obradovic,et al.  Embedding Technical Analysis Into Neural Network Based Trading Systems , 1996, Appl. Artif. Intell..

[6]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[7]  A. Timmermann,et al.  Predictability of Stock Returns: Robustness and Economic Significance , 1995 .

[8]  H. White,et al.  Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap , 1999 .

[9]  Eberhard Schöneburg,et al.  Stock price prediction using neural networks : A project report , 2003 .

[10]  Milton S. Boyd,et al.  Forecasting futures trading volume using neural networks , 1995 .

[11]  C. Tan,et al.  NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI , 1999 .

[12]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[13]  Richard W. Arms.  Volume cycles in the stock market : market timing through equivolume charting , 1983 .

[14]  Bram van Heuveln,et al.  The normalized radial basis function neural network , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[15]  Beryl Adamson.  Understanding Fibonacci Numbers. , 1978 .

[16]  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..

[17]  Olivier V. Pictet,et al.  Changing time scale for short‐term forecasting in financial markets , 1996 .

[18]  Charles M. C. Lee,et al.  Price Momentum and Trading Volume , 1998 .

[19]  Ricardo Pereira Câmara Leal,et al.  Tests of technical trading strategies in the emerging equity markets of Latin America and Asia , 1999 .

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

[21]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

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

[23]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[24]  Salih N Neftci,et al.  Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis." , 1991 .

[25]  Franklin Allen,et al.  Using genetic algorithms to find technical trading rules , 1999 .

[26]  B. LeBaron,et al.  Simple Technical Trading Rules and the Stochastic Properties of Stock Returns , 1992 .

[27]  Maureen O'Hara,et al.  Market Statistics and Technical Analysis: The Role of Volume , 1994 .

[28]  J. Poterba,et al.  What moves stock prices? , 1988 .

[29]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[30]  Christopher J. Neely,et al.  Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach , 1996, Journal of Financial and Quantitative Analysis.

[31]  Sheng-Tun Li,et al.  Knowledge discovery with SOM networks in financial investment strategy , 2004, Fourth International Conference on Hybrid Intelligent Systems (HIS'04).

[32]  Steven B. Achelis.  Technical Analysis from A to Z : Covers Every Trading Tool... from the Absolute Breadth Index to the Zig Zag , 2000 .

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

[34]  M. Levitt.  Market Time DataTM Improving Technical Analysis and Technical Trading , 1998 .

[35]  Gordon Leitch,et al.  Economic Forecast Evaluation: Profits versus the Conventional Error Measures , 1991 .

[36]  B. Malkiel.  A Random Walk Down Wall Street , 1973 .

[37]  Kalok Chan,et al.  Market Efficiency and the Returns to Technical Analysis , 1998 .