PREDICTION OF STOCK TRADING SYSTEM USING NEWS AND USER FEEDBACK

Abstract

An automated framework for soothsaying the stock market investment is presented on this
paper. It is intended to analyze the share predicated on day, week, month, and yearly substructure.The
framework analyses the shares predicated on the RSS feeds from the news. The RSS tracker is implemented
via the API associated with it. Only the financial market alone is focused through the RSS tracker. Though to
make still utilizer amicable and convenient to the utilizer, the utilizer interest about the particular few tasks
are amassed from the utilizer at the initial stage, and the RSS is obtained only for that particular stocks. The
key conception of this project is to offer a secured and remuneratively lucrative platform to the investors in
order to make a positive gain towards their share on return. The presage and suggestion is mainly predicated
on the impact of a share about its liquidity flow and the news events about the particular share. Since the
presage cannot be preceded only with the news events, the concept of obtaining the utilizer feedback from the
authentic time shareholders is introduced. The feedback about each and every task is obtained and converted
in terms of ration and stored on the backend databases. Hence determinately both datasets (via RSS and
Utilizer feedback) are analyzed via genetic programming and the shares are suggested for a utilizer. The
impact of news on liquidity and automated trading is critically examined. Determinately we explore the
interaction between manual and automated trading.