The technological mutations of recent years have profoundly disrupted the design and structure of stock markets with the development and multiplication of electronic platforms: electronic communication networks (ECN) and alternative transaction systems (ATS). These technological innovations have brought about serious changes in the access to and processing of financial information, in brokerage...

In this paper a data mining approach for classification of stocks into clusters is presented. After classification, the stocks could be selected from these groups for building a portfolio. It meets the criterion of minimizing the risk by diversification of a portfolio. The clustering approach categorizes stocks on certain investment criteria. We have used stock returns at different times along ...

Competition among stock exchanges has increased dramatically over the last decade. To attract trading volume, most exchanges introduced makertaker fees, an incentive scheme that rewards liquidity suppliers and charges liquidity demanders. Using a change in fees on the Toronto Stock Exchange, we analyze how the breakdown of trading fees between liquidity demanders and suppliers affects market ou...

The paper focuses on the task of predicting future values of stock market index. Two indices namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets are selected for experimental evaluation. Experiments are based on 10 years of historical data of these two indices. The predictions are made for 1–10, 15 and 30 days in advance. The paper proposes two stage fusion app...

Nowadays, stock market is the one of the major sources of raising resources for India and is act as a key driver for economic growth of a country. The stock market forecasting is a very difficult and highly complicated task because it is affected by many factors such as economic conditions, political events and investor’s sentiment etc. The stock market series are generally dynamic, nonparametr...

This study proposed a novel Nonlinear Auto Regressive eXogenous Neural Network (NARXNN) with Tracking Signal (TS) approach and seeks to investigate the various training functions to forecast the closing index of the stock market. A novel approach strives to adjust the number of hidden neurons of a NARXNN model with different training functions. It uses the Tracking Signal (TS) and rejects all m...