An optimized approach to predict the stock market behavior and investment decision making using benchmark algorithms for Naïve investors

The stock market is chaotic nature in general. The market and the trend will be well known only for existing investor and also for market players. Due to chaotic nature it is more difficult for making the investment decision by the new investors'. The literature survey as well the recent research history were failed to emphasize the strong decision making power for the new investor. The Time Series analysis is not only an indicator for the stock index but also for the market behavior. This paper focuses on introducing a new mechanism using the Dynamic Neural Network. The NSE indices Nifty-Midcap50 and Reliance are chosen for analysis. The Nonlinear Autoregressive eXogenous (NARX) Network architecture in association with two bench mark algorithms Levenberg Marquardt (LM) and Scaled Conjugate Gradient (SCG) are used for identifying the market behavior. The outcome of the analysis will provide accurate results for the investment decision making to the naive investors'.

[1]  Jian Zhang,et al.  Daily stock market forecast from textual web data , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[2]  Ömer Kaan Baykan,et al.  Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..

[3]  A. Neil Burgess,et al.  Neural networks in financial engineering: a study in methodology , 1997, IEEE Trans. Neural Networks.

[4]  Andrew Flitman,et al.  Forecasting Stock Market Performance Using Hybrid Intelligent System , 2001, International Conference on Computational Science.

[5]  Emin Avcı,et al.  Forecasting Daily and Sessional Returns of the ISE - 100 Index with Neural Network Models , 2007 .

[6]  Xin Li,et al.  Application of Neural Networks in Financial Data Mining , 2007, International Conference on Computational Intelligence.

[7]  Rajesh V. Argiddi,et al.  Stock Market Prediction Model by Combining Numeric and News Textual Mining , 2012 .

[8]  Marion O. Adebiyi,et al.  Stock Price Prediction using Neural Network with Hybridized Market Indicators , 2012 .

[9]  Mahdi Pakdaman Naeini,et al.  Stock market value prediction using neural networks , 2010, 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM).

[10]  Hsinchun Chen,et al.  Textual analysis of stock market prediction using breaking financial news: The AZFin text system , 2009, TOIS.

[11]  Ramon Lawrence,et al.  Using Neural Networks to Forecast Stock Market Prices , 2000 .

[12]  Jatinder N. D. Gupta,et al.  Neural networks in business: techniques and applications for the operations researcher , 2000, Comput. Oper. Res..

[13]  Marc-André Mittermayer,et al.  Forecasting Intraday stock price trends with text mining techniques , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[14]  Hava T. Siegelmann,et al.  Computational capabilities of recurrent NARX neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[15]  Ryan Sangjun Lee.  Optimal parameter estimation for long-term prediction in the presence of model mismatch applied to a two-link flexible joint robot , 2011 .

[16]  Xindong Wu,et al.  10 Challenging Problems in Data Mining Research , 2006, Int. J. Inf. Technol. Decis. Mak..

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

[18]  Amir F. Atiya,et al.  Introduction to the special issue on neural networks in financial engineering , 2001, IEEE Trans. Neural Networks.

[19]  K. S. Adewole,et al.  Stock Trend Prediction Using Regression Analysis - A Data Mining Approach , 2011 .

[20]  P Alli,et al.  An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50 , 2013 .

[21]  V. P. Mohandas,et al.  A Methodology for Aiding Investment Decision between Assets in Stock Markets Using Artificial Neural Network , 2010 .

[22]  Manolis I. A. Lourakis.  A Brief Description of the Levenberg-Marquardt Algorithm Implemented by levmar , 2005 .

[23]  Tak-Lam Wong,et al.  Design and implementation of NN5 for Hong Kong stock price forecasting , 2007, Eng. Appl. Artif. Intell..

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

[25]  Shebel Asad.  OBSERVING OF pH FOR TITRATION PROCESS WITH HYBRID NEURAL NETWORK STRUCTURE , 2012 .

[26]  Nazri Mohd Nawi,et al.  Enhancing Back Propagation Neural Network Algorithm with Adaptive Gain on Classification Problems , 2011 .

[27]  Yannis Theodoridis,et al.  Intelligent Stock Market Assistant using Temporal Data Mining , 2005 .

[28]  Eugen Diaconescu,et al.  The use of NARX neural networks to predict chaotic time series , 2008 .

[29]  Abbas Ali Abounoori,et al.  Comparative study of static and dynamic neural network models for nonlinear time series forecasting , 2012 .

[30]  Nor Ashidi Mat Isa,et al.  A Study on Neural Network Training Algorithm for Multiface Detection in Static Images , 2010 .

[31]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.