I aim to develop a website on Stock market Analysis which will deal with the role of markets in our daily life. My aim is to create a website which will help the people or users to have the opportunity of knowing what is presently happening in the markets both in India and globally. The website would display the current news of stock market, latest trends of various commodities, top grossing, top trending, what are the user’s friends upto, top gainers/losers, most active stocks, only buyers and sellers, show the present value of various indices using various data mining algorithms. The website basically aims to advice people about trading through BSE and NSE. The user can have a complete view of the current news and markets trends and be recommended what and where to invest according to various algorithms. The user can view the historical data and can analyze the graphs of a particular company or can draw the comparison between the stock prices of two different companies. The user can also be recommended on the basis of offline and online experts and using an expert advice which gives the feedback about the particular company.

1. FINAL MAJOR PROJECT
Stock Market Analysis
Mentor: Ms. Sakshi Aggarwal
Submitted By:
Gauri Bansal (Batch B8) 10104685

2. ABSTRACT
 My aim is to develop a website on stock market analysis which will
deal with the role of markets in our daily life and will help the users
to have the opportunity of knowing what is presently happening in
the markets both in India and globally.
 The website would display the current news of stock market, latest
trends of various commodities, show the present value of various
indices and calculate the change values.
 The website basically aims to advice people about trading through
BSE and NSE. The user can have a complete view of the current
news and markets.
 There will be a live sensex index to show the sensex values for
various top companies.

3. PURPOSE
• To help the users in recommending the stocks that would help to to
invest in the market wisely. The users can buy the stocks of any
particular company by viewing their historical data and comparing
the stock market prices with another company.
• The main purpose of this project is to build a website where user
can have a complete view of the current news and markets trends
and be recommended what and where to invest based on various
Data mining algorithms.

4. SCOPE
Currently there are very less websites, especially in India, that provide
complete information on stocks as well as commodities fluctuation
information in an integrated form. They provide basic information on
stock market.
These websites display lot of updates which leads to the same amount
of confusion, above all, the most important updates are submerged
beneath all other ordinary updates.
So, I am trying to provide a more user friendly portal with
recommendation on investments. These recommendations are based
on top grossing, top trending, top gainers, Expert advice , online and
offline experts and what are their friend’s up to etc.

5. Solution Approach In Terms
Of Technology Used
The comprehensive study of the following areas is consists of my
content requirement:
Understanding basics of stock market
Live data of stock market
The concept of recommendation
Expert’s opinion (online and offline experts)
Facilities provided to the user
Charts and graphs generated for the historical data of the companies.
Top gainers and losers using Hybrid algorithm
Prediction of the prices
Calculation of the EMI ,better investment avenue , savings, monthly
reducing rate.
Feedback option by the experts.

6. Implementation details
• Collection of data: The .csv files are downloaded from Yahoo
Finance which provides financial news and information and also
offers news and information about stock quotes, stock exchange
rates, corporate press releases and financial reports, and popular
message boards for discussing a company's prospects and stock
valuation.
Data Importing: The .csv files are then imported to the MySQL
server so that the database is compatible with PHP and further
computations be done.

7. Recommendation system
• Top Grossing: Highest volume of the stock of the companies would
be recommended on the basis of current data. The mean average
volume of the companies is calculated.
• What are your friends Upto: Application of nearest neighbor’s
algorithm on the user's friend's stock buying pattern.
• Top trending: Association rules are applied and corresponding
results are recommended to the user.
• Based on expert advice: Application of nearest neighbor based on
CRSP values where CRSP=number of shares * closing price.
• Stock’s prices: the recommendations would be "Buy " and
"Accumulate". Application of neural network to predict future prices
based on historical data.
• Top gainers/ losers : Application of hybrid algorithm.

8. Calculators

9. This website has a login option where new registers can register
themselves and the existing users can log on using their credentials
and can view the historical data of any company and can invest in the
stocks recommended to them on the latest stocks purchased by their
friends, or by online and offline expert advice . Users could also be
recommended on the basis of the various other factors.

10. NOVELTY
The novelty of this project is that different algorithms have been
implemented. K- nearest neighbor algorithm is used where the
difference in the CRSP=number of shares * closing price of the
current users and different users in noted down. Then Euclidean
distance formula is applied on the values so that the neighbor which is
nearest to the user recommends him to invest in that stock.
Neural network concept is used which gives different weights to stock
purchased by user 1 and user 2. This way the algorithm helps in
predicting the prices of the stocks and recommending it to the user.
This website has an exciting feature of Crawler where Mercator crawler
has been used which crawls the yahoo finance news and displays it on
the main page. Use of hybrid algorithm for finding out the change in the
current prices of the companies.
This type of website has never created where the blend of all the
algorithms is used.

11. LIVE MARKET

12. Design Scenario
1
• Database Upadation
• Csv files downloaded from yahoo finance
• Stock tables updated to the latest values
2
• User Login
• Each user has a login id using which he can access his previous
transactions
• Each user has a friend list as well
3
• User can view stock charts, compare with other companies and also
view historical data
• Recommender where he can be recommended based on various
criteria
4
• Neural Network: feed the network with past values as well as result
of test data set
• A generalized result is obtained by giving weightage to different
parameters

13. Existing Approach
The approach adopted in this paper firstly creates user profile
based on the documents viewed by the user. It is discussed in
detail in literature. Each user profile is represented as a
concept tree. Traditional methods represent user profile as
keyword word vector. However, the keyword vectors can be
extremely sparse and it also suffers from the problem of
semantic ambiguity. Then, the correlation strength between
users is computed using tree-edit distance. Finally, the
spreading activation model is employed to search for users
those have similar interests with target user. The strength of
spreading activation model lies in its ability to analyze the
relationship among users based on correlation strength.

15. User Profile-Based Personalized
Research Paper Recommendation System

16. Predicting the future price and recommending the user where he should
accumulate his previous stocks or purchase the new ones . This relies on the
concept of neural networks which help in predicting forecast based on the
historical data.

17. Apart from the recommender system, the user can also use other
features of the website such as comparison of stocks, viewing the
past data or even analyzing data using graphs and charts. Charts
are formalized into trading rules or are used in neural networks to
predict the future stock prices.
Comparison of Vodafone with TTM
from year 2013 to 2014
Comparison of Vodafone from
year 2013 to 2014

18. Future Recommendation System
I believe that apart from the indicators used here, various other factors
can also be considered for the recommendations.
The short term and long term investment patterns and user will can be
further looked upon.
A wide range of companies can be included so that users from
different domains are also benefitted from my website.
The project can further be extended for countries other than India.
There can be a live Sensex index to show the Sensex values for
various top countries.
A large dataset of the users could be added which could help the user
to choose the stocks of the company by the historical data of the other
users and their preference.

19. CONCLUSION
I have developed a website on Investment Portal which deals with the role
of markets in our daily life. The website would help the people or users to
have the opportunity of knowing what is presently happening in the
markets both in India and globally.
The user would be recommended based on the type of investment of the
user- short term or long term. The user would be recommended on the
option he chooses. Suppose if he chooses top grossing then he would be
recommended on the highest volume of the company . Users can also see
their friends activity and can see how many shares they are have
purchased and could be recommended on the basis of k nearest
algorithm.
The website displays the current news of stock market, latest trends of
various commodities, like converting from one currency to another and
also shows the present value of various indices . It also crawls the news
from yahoo finance through Mercator crawler

20. REFERENCES
[1] Christopher Avery, Judith Chevalier, Richard Zeckhauser, The "CAPS" Prediction System
and Stock Market Returns, HKS Faculty Research Working Paper Seriesare 2009
[2] Marco Gori, Augusto Pucci, "Research Paper Recommender Systems: A Random-Walk
Based Approach", IEEE/WIC/ACM International Conference 2006
[3] Pijitra Jomsri, Siripun Sanguansintukul, Worasit Choochaiwattana, "A Framework for Tag- Based
Research Paper Recommender System: An IR Approach", IEEE 24th International
Conference 2010
[4] Chenguang Pan, Wenxin Li, "Research Paper Recommendation with Topic Analysis",
International Conference On Computer Design And Appliations (ICCDA 2010)
[5] Kazunari Sugiyama, Min-Yen Kan, "Scholarly Paper Recommendation via User's Recent Research
Interests, " CDL'10 Proceedings of the 10th annual joint conference on Digital libraries, 20lO.
[6] Bamshad Mobasher, Honghua Dai, Tao Luo, Yuqing Sun and Jiang Zhu, "Integrating Web Usage
and Content Mining for More Effective Personalization, " Electronic Commerce and Web Technologies
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[7] T. Bogers and A. van den Bosch, "Recommending scientific articles using CiteULike", ACM
Recsys'08
[8] T Y Tang and G. McCalla, "A multidimensional paper recommender - experiments and evaluations",
2009 IEEE Internet Computing
[9] K wanghee Hong, Hocheol Jeon, Changho Jeon, "UserProfile-Based PersonalizedResearch
Paper Recommendation System", IEEE 24th International Conference 2010
[10] Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John T. Riedl, "Evaluating
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2004

21. THANK YOU!