Neuro Fuzzy Modeling of The Impact of Real Economic Indicators on Stock Market Behaviour: Some Reflections from National Stock Exchange of India
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Abstract
The modeling of stock market behaviour is one of the key areas of present financial research as stock market is the main determinant of economic development of a country. Worldwide a number of researches have been conducted on the modeling of relationship between macroeconomic indicators and stock market behaviour. But in the context of India not many researches can be traced in the literature. These are primarily based on statistical and econometric analysis, which are not much reliable because of lack of accuracy and non-linearity in the system. In the present study the researchers have made an attempt to develop the neuro fuzzy model, which is not been previously done by researchers in India. The model is based on the data for the period from April 1999 to March 2010. The study considered daily index of S&P CNX Nifty as an indicator of stock market behaviour and the real macroeconomic indicators (Gross Domestic Product, Index of Industrial Production and Inflation) as determining variables. The study finds that neuro fuzzy model developed for stock market behaviour is quite suited for this application.
References
Adam, Anokye M and Tweneboah, George (2008), Do macroeconomic variables play any role in the stock market movement in Ghana?, MPRA paper no. 9368.
Abhyankar A, Copeland LS and Wong W (1997), Uncovering nonlinear structure in real-time stock-market indexes: the S&P 500, the DAX, the Nikkei 225, and the FTSE-100, Journal of Business & Economic Statistics, Vol. 15, pp. 1–14.
Aguiar RA, Sales RM and Sousa LA (2006), A behavioural fuzzy model for analysis of overreaction and underreaction in the Brazilian stock market, Joint Conference on Information Sciences, Taiwan, China, 2006.
Ahmed MF (1999), Stock market, macroeconomic variables and causality: the Bangladesh case, savings and development, p. 2.
Ali Imran, Rehman UK, Yilmaz AK, Khan Aslam and Afzal Hasan (2010), Causal relationship between macroeconomic indicators and stock exchange prices in Pakistan, African Journal of Business Management, Vol. 4 (3), pp. 312–319.
Chakravarty S (2005), Stock market and macroeconomic behaviour in India, Discussion paper no. 106/2006 Institute of Economic Growth, Delhi.
Chaturvedi DK (2005), Dynamic model of HIV/Aids population of Agra region, Int. J. of Environmental Research and Public Health, Vol. 2.3, pp. 420–429.
Chaturvedi DK (2009), Soft computing techniques and its applications in electrical engineering, Springer.
Chaturvedi DK (2010), Modeling and simulation of systems using Matlab/ Simulink, CRC Press.
Choudhury SS, Mollik AT and Akhter MS (2006), Does predicted macroeconomic volatility influence stock market volatility? Evidence from the Bangladesh capital market, Working Paper; Department of Finance and Banking, University of Rajshahi, Bangladesh.
Corradi V and Distaso W (2009), Macroeconomic determinants of stock market volatility and volatility risk-premia, Working paper, London School of Economics, London.
Diebold FX and Yilmaz K (2008), Macroeconomic volatility and stock market volatility, worldwide, Working paper, National Bureau of Economic Research, Cambridge.
Engle RF and Rangel JG (2008), The Spline-GARCH model for low frequency volatility and its global macroeconomic causes, Review of Financial Studies, Vol. 21, pp. 1187–1222.
Famma EF (1981), Stock prices, inflation, real activity and money, Am. Economic Review, vol. 71, pp. 545–565.
Hagen MT, Demuth HB and Beale M (1996), Neural network design, MA: PWS Publishing Company, Boston.
Humpe A and Macmillan P (2007), Can macroeconomic variables explain long term stock market movements? A comparison of the US and Japan, CDMA working paper no. 07/20.
Hung JC (2009), A fuzzy asymmetric GARCH model applied to stock markets, Information Sciences, Vol. 179, pp. 3930–3943.
Hussan R (2009), Combination of HMM and Fuzzy model for stock market forecasting, Neurocomputing, Vol. 72 (16–18), pp. 3439–3446.
Ibrahim M (1999), Macroeconomic indicators and stock prices in Malaysia: an empirical analysis, Asian Economics Journal, Vol.13(2), pp.219–231.
Lebibcioglu K and Aksoy H (2004), Modeling ISE100 index through fuzzy logic, Eleventh Annual FMS Conference Proceedings, Turkey.
Maysami RC, Lee CH and Mohamad AH (2004), Relationship between macroeconomic variables and stock market indices: cointegration evidence from stock exchange of Singapore’s All-S sector indices, Journal Pengurusan, Vol.24, pp. 47–77.
Meher (2005), Stock market consequences of macroeconomic fundamentals, Institute of Business and Technology, Karachi, Pakistan.
Othman, Shuhadah, Schneider, and Etienne (2010), Decision making using fuzzy logic for stock trading, ITSIM 10, Kuala Lumpur, Malaysia.
Saxena SP and Bhadauriya S (2012), Principal component analysis of macroeconomic determinants of stock market dynamics: some reflections from National Stock Exchange of India Ltd., Transformation & Survival of Business Organization: Challenges and Opportunities, Macmillan Publishers India Ltd., New Delhi, pp. 97–105.
Sivananadam SN, Sumathi S and Deepa SN (2007), Introduction to Fuzzy Logic using MATLAB, Springer.
