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STOCK MARKET MANAGEMENT USING TEMPORAL DATA MINING

STOCK MARKET MANAGEMENT USING TEMPORAL DATA MINING. TEAM MEMBERS. STALIN ANTONY RAJ . A. ARAVIND . V. PROJECT GUIDE. Mr . ASHOK KUMAR. ABSTRACT.

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STOCK MARKET MANAGEMENT USING TEMPORAL DATA MINING

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  1. STOCK MARKET MANAGEMENT USING TEMPORAL DATA MINING TEAM MEMBERS STALIN ANTONY RAJ . A ARAVIND . V PROJECT GUIDE Mr . ASHOK KUMAR

  2. ABSTRACT The stock market domain is a dynamic and unpredictable environment. Traditional techniques, such as fundamental and technical analysis can provide investors with some tools for managing their stocks and predicting their prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the financial markets and help in stock-price forecasting. Therefore, we propose in this paper a portfolio management solution with business intelligence characteristics .

  3. Objective • Designing intelligent stock market assistant using temporal data mining. • The main aim of this system is to provide valuable suggestions and results about details of all stocks present in the market to the depositor. • This software offers a wide range of standard modules providing a highly effective stock market assistant solution for all type of investors.

  4. EXISTING SYSTEM • The stocks are selling based on the company influence such like P/E factor, • Volume, Business Sector, Historical Behavior, Rumors, Book (Net Asset) Value, Stock Earnings • Data mining can be used extensively in the financial markets and help in stock-price forecasting • In olden days stock maintenance is not accurate and efficiency. • Stock maintenance doesn’t follow any exact algorithm and all.

  5. PROPOSED System • The capabilities of this tool are based on temporal data mining patterns, extracted from stock market data • In the monitoring part, the user is able to define stock portfolios, to view stock Price values over time of companies with similar characteristics (e.g. same business sector, price range, P/E etc...) • In the prediction part, the tool helps users to decide on their stock trades. A sequence mining algorithm is used in order to identify frequently occurring sequences of stock fluctuations and thus recommend some good trading opportunities, based on the extracted frequent patterns. • Temporal data mining was introduced to improve the accurate details of stock maintenance

  6. SOFTWARE REQUIREMENTS • Front End: ASP.Net & C# • Back End: SQL Server 2005 • Client side Scripting: JavaScript

  7. HARDWARE REQUIREMENTS Processor : Intel Pentium IV RAM : 512 MB Monitor/Panel : Plug and Play Monitor Hard Disk : 40 GB Keyboard : 109 Keys Mouse : PS/2 Compatible Mouse

  8. DATA FLOW DIAGRAM Level 0

  9. Level 1

  10. Level 2

  11. Registration Process for both Company and Investor • For first entry with stock market management every company should register with that. • After completion of registration work only company can release their various types of shares. • Shareholders have to register to view suggestions and related details with stock market management. • The company registration includes all details of the company and investor registration will contains all details about an investor.

  12. Share Release • In this module company will able to release it’s all types of shares with various names. Investor can’t do anything in this share release. • Share release control will come under company. Share release contains details like company name, type of share released by that company, quantity of released shares and like information.

  13. Share Updation • After share released by a company it will be updated by a company regularly. • Share updations modules will contains details like share type, price of the share, share availability similar information of every company. • The entire control of share updations will comes under company; they won’t have permission for share updating of other companies.

  14. View Share • Every company can see their own shares based on change with index. • One company not able to see other company share status. • Each company see the status of various type of shares and status based on index.

  15. Share Prediction • This module will feel right to investor and an investor can view different types of shares like finance, accounts, IT, Steel etc, from various companies. • Every shareholder can see details about their share updations from company. • Investor can compare their share with other company share.

  16. Variation Status • Day to day variation of stock is not only based on stock price and it will also includes the following factors like name of the company, financial position of that company, price per annual earnings etc. • Investor can get idea about their share status by comparison with other company shares.

  17. Graphical Representation • In this module investor by selecting his share he can get the graphical representation of variation of that share with occasion from its releasing time.

  18. Buy Share • Shareholder can buy the number of shares he wants to buy from the particular company. • It will holds details like name of the company, share type with its name ,availability of shares etc.

  19. SCREEN SHOTS

  20. LOGIN FORM:

  21. USER/AGENT REGISTRATION:

  22. COMPANY REGISTRATION:

  23. VIEW ALL SHARES:

  24. PREDICT SHARE:

  25. GRAPHICAL VIEW OF SHARE VARIATION:

  26. COMPANY:RELEASE SHARE:

  27. UPDATE TODAY SHARE:

  28. VIEW SHARE BY INDEX:

  29. REFERENCES 1. Adriaans, P., Zantinge, D.: Data Mining. Addison Wesley Longman, Harlow,England (1996) 1-10 2. Lin, W., Orgun, M., Williams, G.: An Overview of Temporal Data Mining. Proc. Of the Australasian Data Mining Workshop, ADM02, Sydney, Australia (2002) 83-90 3. Das, G., Gunopulos, D., Mannila, H.: Finding Similar Time Series. Proc. 3rd Int.Conf. Knowledge Discovery and Data Mining (KDD ’97), Newport Beach, California, USA (1997) 88-100 4. Agrawal, R., Faloutsos, C., Swami, A.: Efficient Similarity Search in Sequence Databases. Proc. 4th Int. Conf. Foundations of Data Organizations and Algorithms (FODO ’93), Chicago, IL USA (1993) 69-84 5. Chan, K.-P., Fu, A.W.-C.: Efficient Time Series Matching by Wavelets. Proc. 15th Int. Conf. Data Engineering, Sydney, Australia (1999) 126-133 6. Agrawal, R., Srikant, R.: Mining sequential patterns. Proc. 11th Int. Conf. Data Engineering (ICDE ’95), Taipei, Taiwan (1995) 3-14 7. Mannila, H., Toivonen, H., Verkano, I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, Vol. 1:3 (1997) 259-289

  30. 8. Bettini, C., Wang, X.S., Jajodia, S., Lin, J.L.: Discovering Temporal Relationships with Multiple Granularities in Time Sequences. Technical Report, George Mason University (1996) • 9. Pediaditakis, K.: A Temporal Data Mining Approach for the Extraction of Optimal Temporal Constraints of Sequential Patterns on Event Sequences. MPhil Thesis, School of Informatics, Faculty of Humanities, University of Manchester (2005) • 10. Bettini, C., Wang, X.S., Jajodia, S.: Mining temporal relationships with multiple granularities in time sequences. Data Engineering Bulletin (1998) 21:32-38 • 11. Manilla, H., Ronkainen, P.: Similarity of Event Sequences. Proc. 4th Int. Workshop Temporal Representation and Reasoning (TIME ’97), Daytona Beach, FL USA (1997) 136-139 • 12. Yahoo! Finance Web site: http://finance.yahoo.com • 13. Weigend, A., Chen, F., Figlewski, S., Waterhouse, S.R.: Discovering Technical Trades in the T-Bond Futures Market. Proc. 4th Int. Conf. Knowledge Discovery and Data Mining (KDD ’98), New York, NY USA (1998) 354-358 • 14. Worcester Polytechnic Institute, Knowledge Discovery and Data Mining Research

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