FORECASTING SHARE PRICES USING SOFT COMPUTING TECHNIQUES

International Journal of Students' Research in Technology & Management

View Publication Info
 
 
Field Value
 
Title FORECASTING SHARE PRICES USING SOFT COMPUTING TECHNIQUES
 
Creator Ganesan, Vignesh
 
Subject datamining
genetic algorithm
arima
ANN
linear regression
stock market
 
Description Background: For a long time, there has been a trend of trading of shares. Brokerage firms and dealers buy/sell stocks for clients and companies. Their work is based on knowing how the share price of the company will react in the market. Market/ share price predictions are useful as the investor/broker can attempt to predict the output in order to maximize his dividends or minimize his losses.
Methodology: R and Python tools are used to sort, segregate and process the data, and techniques/algorithms such as Genetic Algorithm, ARIMA, Artificial Neural Networks, and Linear Regression are used to forecast results of data. Along with the model data, external factors affecting share prices also be taken into account.
Findings: For each of the applied algorithms, their results are compared and the difference in output with the real-time values has been observed and recorded.
Implications: Using data mining techniques, an attempt is made to estimate a prediction model to help forecast share prices.
 
Publisher GIAP Journals
 
Date 2019-09-03
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier https://giapjournals.com/ijsrtm/article/view/ijsrtm.2019.722
10.18510/ijsrtm.2019.722
 
Source International Journal of Students' Research in Technology & Management; Vol. 7 No. 2 (2019); 5-10
2321-2543
 
Language eng
 
Relation https://giapjournals.com/ijsrtm/article/view/ijsrtm.2019.722/1158
 

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