Abstract: In this paper we propose a new multilayer classifier architecture. The proposed hybrid architecture has two cascaded modules: feature extraction module and classification module. In the feature extraction module we use the multilayered perceptron (MLP) neural networks, although other tools such as radial basis function (RBF) networks can be used. In the classification module we use support vector machines (SVMs)—here also other tool such as MLP or RBF can be used. The feature extraction module has several sub-modules each of which is expected to extract features capturing the discriminating characteristics of different areas of the input space. The classification module classifies the data based on the extracted features. The resultant architecture with MLP in feature extraction module and SVM in classification module is called NEUROSVM. The NEUROSVM is tested on twelve benchmark data sets and the performance of the NEUROSVMis found to be better than both MLP and SVM. We also compare the performance of proposed architecture with that of two ensemble methods: majority voting and averaging. Here also the NEUROSVM is found to perform better than these two ensemble methods. Further we explore the use of MLP and RBF in the classification module of the proposed architecture. The most attractive feature of NEUROSVM is that it practically eliminates the severe dependency of SVM on the choice of kernel. This has been verified with respect to both linear and non-linear kernels. We have also demonstrated that for the feature extraction module, the full training of MLPs is not needed.

Abstract: Support vector machine (SVM) is one of the most popular and promising classification algorithms. After a classification rule is constructed via the SVM, it is essential to evaluate its prediction accuracy. In this paper, we develop procedures for obtaining both point and interval estimators for the prediction error. Under mild regularity conditions, we derive the consistency and asymptotic normality of the prediction error estimators for SVM with finite-dimensional kernels. A perturbationresampling procedure is proposed to obtain interval estimates for the prediction error in practice. With numerical studies on simulated data and a benchmark repository, we recommend the use of interval estimates centered at the cross-validated point estimates for the prediction error. Further applications of the proposed procedure in model evaluation and feature selection are illustrated with two examples.

Abstract: There exists vast research articles which predict the stock market as well pricing of stock index financial instruments but most of the proposed models focus on the accurate forecasting of the levels (i.e. value) of the underlying stock index. There is a lack of studies examining the predictability of the direction / sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, this study is an attempt to predict the direction of S&P CNX NIFTY Market Index of the National Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Random forest and Support Vector Machines (SVM) are very specific type of machine learning method, and are promising tools for the prediction of financial time series. The tested classification models, which predict direction, include linear discriminant analysis, logit, artificial neural network, random forest and SVM. Empirical experimentation suggests that the SVM outperforms the other classification methods in terms of predicting the direction of the stock market movement and random forest method outperforms neural network, discriminant analysis and logit model used in this study.