SEKE 2012 Proceedings - Knowledge Systems Institute

and if ss i (c) =0if was neutral (s i (c) =0). Finally, the
prediction for this company’s stock is done as follows:
{
Pred(c) =
up if ∑ i s i(c) > 0
down if ∑ i s i(c) < 0
The system can use the aforementioned algorithm and
provide the user with predictions on whether specific stocks
will go up or down. However, this data can be also used as
input to predict the percentage change in the stock price.
One approach would be to build a prediction model, using
machine learning techniques such as decision trees or
association rules, that will generate a set of rules linking the
sentiment score of each company with a specific percentage
change (range) in the stock price. To achieve that, a training
process is needed, as described in Figure 2.
The training of the system is performed as follows: for
each company c the system starts by retrieving the latest
news and generating a prediction Pred(c) as described
above. This prediction is stored in the database. The system
periodically retrieves the stock quotes for the same companies
and stores the trend (up/down), as well as the percentage
of stock change. It also classifies the previous prediction
as accurate or not. The process is repeated until we
have a statistically significant sample and sufficient prediction
accuracy, in which case we can train the system to generate
the rules.
In order to generate rules, we can use appropriate machine
learning techniques such as association rules or decision
trees. The input to the algorithm is the company’s
ticker (unique id), its sentiment score, and its prediction and
the output is a set of rules correlating the aforementioned
data with a percentage range that signifies the change of the
stock (note that, in the case of association rules, we should
keep only the rules that comply with this format). The rules
are in turn stored in the database and can subsequently be
used for real-time predictions of percentage changes.
5 System Prototype
Based on the proposed architecture, we built a system
prototype. The Client was built using Android SDK and
ADT plugin for Eclipse. It communicates with the Server
using RESTful WebServices hosted on a cloud. The Prediction
Engine and the Training Engine are written in Perl, and
the Process Engine is written in Java. The database used is
MySQL. For more technical details on the prototype’s design,
the reader may refer to [4].
The user interface was implemented especially to accommodate
the limitations of a mobile application. The
first time the user uses the application, he/she is asked to
provide some personal data in the registration page. Once
the registration is complete, the user has access to the afore-
Figure 4. Company details for Appliance Recycling Centers
of America (ARCI) stock
mentioned functionality, through 5 menu options: Markets,
MyProfile, News, Prediction, and Search.
The News section allows the user to enter the company
name or the ticker of the company whose news the user
wants to search. If the text box is left empty, all financial
news will appear, ordered by time (newest first).
The Search section allows the user to search for stock
quotes. The user may enter the company’s name or ticker
symbol, or the first letters of any of the above. In the latter
case, the user will be given the option to choose among the
companies that match this description. Once a company is
selected, the system shows the value of the company’s stock
price, change in the stock, percentage change in the stock,
as well as the volume of stocks traded. The user is then
given the option to either buy, add to watch list, check the
company-related news, or get the prediction for that company’s
stock. An example is shown in Figure 4.
The user’s activity can be reviewed from the MyProfile
section. This section provides several options.
The MyPortfolio tab allows the user to see details like
their name, rank, amount of money in cash and equities,
total current worth, as well as the stocks the user owns.
Asample screen is shown in Figure 5. The Watchlist tab
shows the stocks the user has put in his/her watch list. These
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