In this paper, we propose Wavelet packet transform based prediction of trends in nonlinear financial time series data. Bombay stock Exchange (INDIA) was selected as a tool to show the Wavelet packet transform based prediction of trends in financial time series. The experimental results demonstrate that the proposed method substantially outperform existing approaches.

Access to finance is widely recognized as one of the main impediments for growth and development of the small and medium enterprises. This article presents certain aspects of development of stock exchanges oriented towards small and mediumsize enterprises. It aims to contribute towards the growing debate on funding of small and medium enterprises through dedicated stock exchanges and serve as a...

Two alternative trading mechanisms for securities markets are compared using laboratory experimentation and computer simulation. One mechanism is the floor-based specialist auction in place in most U.S. stock exchanges today, and the other is an electronic alternative employing automatic order matching. We conclude that transition from the established floor-based exchanges to potentially superi...

All those thinking about buying stocks on one of the national Stock Exchanges should consider consulting w ith one of the experts, Notre Dame’s assistant profes­ sor of finance, Robert Battalio. Battalio and two col­ leagues at Indiana University recently surprised in ­ vestors nationwide w ith the ir study exam ining p ro ­ gram s in Boston and on the C in c in n a ti reg io n a l exchange. St...

This paper presents a scheme using Differential Evolution based Functional Link Artificial Neural Network (FLANN) to predict the Indian Stock Market Indices. The Model uses Back-Propagation (BP) algorithm and Differential Evolution (DE) algorithm respectively for predicting the Stock Price Indices for one day, one week, two weeks and one month in advance. The Indian stock prices i.e. BSE (Bomba...

This paper analyses the top-level structure of the Bombay Stock Exchange using a complex networks framework. The market is visualized as a network, consisting of stock prices as its nodes, and their dynamic interactions being reflected in the edges. The edges used here, signify cross-correlations between any two stocks and take into account the time-lagged interdependence on each other. By proc...