Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/310478
Title: | Soft Computing Based Stock Price Prediction |
Researcher: | SRINIVASAN,N |
Guide(s): | LAKSHMI,C |
Keywords: | Computer Science Computer Science Theory and Methods Engineering and Technology |
University: | Sathyabama Institute of Science and Technology |
Completed Date: | 2019 |
Abstract: | Trading in stock markets is not an easy task and requires newlineexpertise and knowledge that improve and increase the chances of newlinemaking more profits and ensuring that you make profitable decisions at newlineall times. Financial Experts and data analysts make use of various newlinealgorithms and neural networks to predict the value of stock depending newlineupon the available information and then use the outcomes to analyze and newlinemake their trading decision. newlineThe main motive of this research is to predict the future stock newlinevalue of the particular stock with minimum variation from the actual newlinevalue of stock. In this research, a soft computing based mygrave newlinealgorithm is proposed for stock market prediction. It will be helpful for newlineshort term investors in the National stock market. Some important newlinefactors that affect the value of stock are Total stocks traded, Total newlineturnover of the company, Gross Domestic Product (GDP) of the newlinecountry, GDP per capita and political or external factors are some of the newlinemain factors that affect the stock value of that particular day. Opening newlineand closing values of the stock market were predicted with the help of newlinethe above factors. Each factor will be considered as an object with mass, newlinethe mass of every object will be based on the importance. With the help newlineof Mygrave Algorithm the converging point of the entire object is newlinedetermined and it is said to be the optimal output of the algorithm. The newlineinputs are opening, closing, low and high values of a stock for a period newlineof one year (256 days). Stock Exchange data were collected from newlineNational Stock Exchange (NSE), India, 2016 and these data were given newlineviii newlineas an input to the proposed model to evaluate the performance of stock newlinemarket. newlineThis research work outlines the basic elements influencing the newlinefuture value of the stock and also suggests the best algorithm which newlinegives the most efficient and accurate results. |
Pagination: | 205 |
URI: | http://hdl.handle.net/10603/310478 |
Appears in Departments: | COMPUTER SCIENCE DEPARTMENT |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
10 chapter 5.pdf | Attached File | 1.1 MB | Adobe PDF | View/Open |
11 references.pdf | 442.9 kB | Adobe PDF | View/Open | |
12 curriculam vitae.pdf | 27.48 kB | Adobe PDF | View/Open | |
13 evaluation report.pdf | 5 MB | Adobe PDF | View/Open | |
1 title.pdf | 49.92 kB | Adobe PDF | View/Open | |
2 certificate.pdf | 194.04 kB | Adobe PDF | View/Open | |
3 acknowledge.pdf | 57.07 kB | Adobe PDF | View/Open | |
4 abstract.pdf | 54.6 kB | Adobe PDF | View/Open | |
5 table of contents.pdf | 180.74 kB | Adobe PDF | View/Open | |
6 chapter 1.pdf | 769.89 kB | Adobe PDF | View/Open | |
7 chapter 2.pdf | 662.09 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 1.1 MB | Adobe PDF | View/Open | |
8 chapter 3.pdf | 1.14 MB | Adobe PDF | View/Open | |
9 chapter 4.pdf | 309.17 kB | Adobe PDF | View/Open |
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