SEKE 2012 Proceedings - Knowledge Systems Institute

the major news events that affect the stock market and also
understand their impact. Most recently, in [2] the authors
analyze the text content of Twitter feeds using two mood
tracking tools, and use a Granger causality analysis and a
Self-Organizing fuzzy Neural Network to predict changes
in the Dow Jones Industrial Average closing values.
Apart from the research papers, there exist two Android
applications which are related to the proposed system. One
of them is Bloomberg Mobile 1 . It is one of the best available
in the market supporting features like latest financial news
and stock market quotes among others. The unique selling
point of the application is that users will be able to create
personalized views of the news filtered by industry, region,
exclusivity and/or popularity. Another application 2 which
is related to our project is about simulation of buying and
selling of stocks. It allows the users to track their progress,
buy and sell at real, up to date stock prices and manage their
portfolio on the go. The unique selling point of the application
is the presentation of the stock market in a simulated
environment. Our application is unique in that it combines
the best features of other applications, such as providing latest
financial news, quotes, and a simulated environment for
trading using virtual money, but also adds new features such
as stock market prediction.
3 System Architecture
A high-level architecture of the proposed system is depicted
in Figure 1. This is a three-tier architecture, consisting
of the Android client, the Internet, providing access to
stock-related news and quotes and enabling the client/server
interaction through the Web Services, and the Server, consisting
of the Process, the Prediction, and the Training Engines
and the system’s database.
3.1 Android Client
The Android client provides several types of functionality
to the end user: It serves as a stock market interface,
providing information about the latest financial news and
quotes (using the Internet module). It also allows the end
user to create a watch list of stocks and provides a simulated
environment for buying/selling stocks using virtual money.
This can be regarded as a stock market online game, and
the end user can review his/her ranking as compared to other
players who are using the application. Most importantly, the
Android client interacts with the server’s prediction engine
to provide predictions for a company’s stock price. This information
can be used by the end user to add/remove stocks
from his/her watch list and portfolio. All this information
1 http://www.bloomberg.com/mobile
2 http://www.chickenbrickstudios/games/mmc
is sent to the Android Client by the Server through the Web
Services.
3.2 Server
The Server consists of three Engines (Process, Training,
Prediction), as well as the database, with the Training and
Prediction modules being the core of the proposed system.
Training Engine. The Training Engine uses the Internet
module to retrieve news and quotes. It then employs data
mining algorithms to train the system so that it can then
predict the stock price movement. It saves those rules in
the database, which can then be used by the Prediction Engine
to provide prediction to the Android Client. The training
consists of a series of actions. Since the data are timesensitive,
the system needs to be trained frequently so that
the predictions depict the latest market trends. Our architecture
enables the administrator to decide how often the training
occurs. The training process is triggered automatically
at the times defined.
The training process can be described as the following
sequence of actions, also depicted in Figure 2: The Training
Engine requests news for a list of companies through Yahoo!
Finance 3 The Training Engine then processes the articles
using sentiment analysis, and assigns a sentiment score
to each company using the sentiment dictionary. A positive
sentiment score means that the stock price is predicted to
go up, whereas a negative sentiment score means that it is
predicted to go down. The sentiment scores are saved in
the database. Next, the stock quotes of those companies are
requested and are also saved in the database. The quotes
of the same companies are requested after n hours (n is a
user-defined parameter). The quotes’ source returns realtime
stock quotes for those companies. The training engine
retrieves from the database the sentiment scores which were
generated for those companies as well as the quotes to check
for accurate predictions made in the stock price movement.
The training engine then uses the sentiment scores of the
accurate predictions, and creates correlations of the scores
to the percentage change in the respective stock quotes using
machine learning techniques (association rules or decision
trees). The results, in the form of rules, are saved in
the database, to be accessed later by the prediction engine
to make predictions on both the trends (stock going up or
down) and the percentage change of the stock.
Prediction Engine. The Prediction Engine is used to send
stock price predictions to the Android Client. This engine
interacts with the database to retrieve and use the training
data. However, its functionality is asynchronous to that of
the Training engine. In fact, the Prediction Engine is called
on-demand, i.e. whenever a user wants a prediction on a
3 http://finance.yahoo.com
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