CONFIGURING ARTIFICIAL NEURAL NETWORKS FOR STOCK MARKET PREDICTIONS

Artificial Neural Networks – an Application to Stock Market Volatility

The present study aims at applying different methods i.e GARCH, EGARCH, GJRGARCH, IGARCH & ANN models for calculating the volatilities of Indian stock markets. Fourteen years of data of BSE Sensex & NSE Nifty are used to calculate the volatilities. The performance of data exhibits that, there is no difference in the volatilities of Sensex, & Nifty estimated under the GARCH, EGARCH, GJR GARCH, I...

Artificial Neural Networks for Forecasting Stock Price ]

Statistical arbitrage strategies have always been popular since the advent of algorithmic trading. In particular, Exchange traded fund (E.T.F.) arbitrage has attracted much attention. Trading houses have tried to replicate ETF arbitrage to other stocks. Thus, the objective is to be able to develop a long term pricing relationship between stocks and profit from their divergence from this relatio...

Towards Enhancing Stock Market Watching Based on Neural Network Predictions

Under the growth of the stock market sector and the widespread of stock market applications, the stock market prediction has become one of the most important and challenging tasks in the stock market. Many data mining techniques are exploited to predict the stock prices in order to help investors in making investing decisions. One of the most common and widely used techniques is Artificial Neur...

Stock Market Investing Using Neural Networks

The fundamental hypothesis of this paper is that certain trends exist in stock market data that, if properly recognized, could be used to invest well on certain stocks. This paper details an attempt to apply neural networks to stock market investing, in order to create an agent that can learn to invest profitably in certain stocks. To accomplish this, a classification scheme is developed which ...

Stock Market Pattern Recognition with Neural Networks