Support Vector Machines Approach to Predict the S&P CNX NIFTY Index Returns

The present study, investigates the predictability of S&P CNX NIFTY Index returns using Support vector machines (SVM). The performance of the SVM model in forecasting Nifty index returns is rigorously evaluated in terms of widely used statistical metrics like mean absolute error, root mean square error, normalized mean square error, correctness of sign and direction change (Pesaran and Timmermann (1992, DA test), and equal forecast accuracy using Diebold and Mariano (1995, DM test) by comparing its performance with those of neural network, random forest regression and a linear ARIMA model. The four competiting models are also examined in terms of various trading performance and economic criteria like annualized return, Sharpe ratio, maximum drawdown, annualized volatility, average gain/loss ratio etc via a trading experiment. The findings of the study reveal that SVM model achieves greater forecasting accuracy and improves prediction quality compared to other models experimented in the study. The SVM model can be used as an alternative forecasting tool for Nifty Index returns and it will lead to better returns based on the traditional forecasting accuracy measures, such as root mean squared errors, and financial criteria, such as risk-adjusted measures of return.

[1]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

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

[3]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

[4]  Dirk Van den Poel,et al.  Predicting customer retention and profitability by using random forests and regression forests techniques , 2005, Expert Syst. Appl..

[5]  Manish Kumar,et al.  Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest , 2006 .

[6]  M. Leung,et al.  Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models , 1999 .

[7]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

[8]  Paul A. Fishwick,et al.  Time series forecasting using neural networks vs. Box- Jenkins methodology , 1991, Simul..

[9]  Derek W. Bunn,et al.  Financial time series modelling with discounted least squares backpropagation , 1997, Neurocomputing.

[10]  Gordon W. Crawford,et al.  Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House Prices , 2003 .

[11]  William Remus,et al.  Neural Network Models for Time Series Forecasts , 1996 .

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  R. Donaldson,et al.  A New Dividend Forecasting Procedure That Rejects Bubbles in Asset Prices: The Case of 1929's Stock Crash , 1996 .

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

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

[16]  Adrian Pagan,et al.  Alternative Models for Conditional Stock Volatility , 1989 .

[17]  H. Brian Hwarng,et al.  A simple neural network for ARMA(p,q) time series , 2001 .

[18]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[19]  Achilleas Zapranis,et al.  Stock performance modeling using neural networks: A comparative study with regression models , 1994, Neural Networks.

[20]  S. Viswanathan,et al.  No Arbitrage and Arbitrage Pricing: A New Approach , 1993 .

[21]  Seref Sagiroglu,et al.  Synergistic Neural Models of a Robot Sensor for Part Orientation Detection , 1996 .

[22]  Kazuo Asakawa,et al.  Stock market prediction system with modular neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[23]  Ilkka Virtanen,et al.  Forecasting stock market prices in a thin security market , 1987 .

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

[25]  Joseph S. Zirilli.  Financial Prediction Using Neural Networks , 1996 .

[26]  M. Hashem Pesaran,et al.  A Simple Nonparametric Test of Predictive Performance , 1992 .

[27]  Paul A. Fishwick,et al.  Feedforward Neural Nets as Models for Time Series Forecasting , 1993, INFORMS J. Comput..