SEKE 2012 Proceedings - Knowledge Systems Institute

Figure 1. System Architecture
would be really beneficial to the traders. This will assist
them in making the decision of buying or selling a companys
stock. This motivated our work, designing a mobile
client which enables the user to create a watch list of stocks,
get latest stock quotes, and request stock predictions. The
application also enables the user to simulate stock trading
based on the predictions using virtual money but real stock
data.
In a nutshell, our prediction system works as follows:
the user sends the companys ticker symbol whose prediction
is needed. The prediction system scans that particular
companys news and generates a sentiment score which tells
whether the sentiment related to that company is good/bad
and also how much percentage fluctuation in price might
occur within three hours. The prediction system is able to
give the percentage fluctuation because it has been trained
to determine the relationship between the sentiment score
and the percentage in stock price change.
The rest of the paper is organized as follows: In Section
2, we provide a review of the related work. We then discuss
in detail the architecture of the mobile application in Section
3. In Section 4 we outline the basic steps of the prediction
algorithm and in Section 5 we present the system prototype.
We conclude with our plans for future work in Section 6.
2 Related Work
One line of research is to use Support Vector Machines
(SVM) for stock market prediction. In [3] a hybrid machine
learning system based on Genetic Algorithm (GA)
and Support Vector Machine (SVM) is proposed. For input,
the authors use the correlation among stock prices of various
companies as well as various pointers from the area of
technical analysis. Their observation was that, the fusion of
Genetic Algorithm/SVM is better than the individual SVM
System. In [9] the author claims that SVM combined with
boosting gives superior results in stock market prediction.
It also suggests that a particular algorithm might be better
suited to a particular type of stock, say technology stocks,
whereas give lower accuracies while predicting other types
of stocks, say energy stocks. This paper also states that
external unknown factors like election results, rumors, and
other factors should be considered for stock market prediction.
In another paper [5] the authors state that non-linear
combination of linear and non-linear methods give better
predictions than using each method separately. This paper
combines linear and non-linear regression models with
SVM and use their model to predict opening and closing
price on the Shanghai Stock Exchange.
Another line of research focuses on the use of Neural
Networks (NNs). In [11] various prediction models are
compared and the authors conclude that NNs predict market
directions more accurately than other existing techniques.
In [8] the authors describe how NNs learn with time and
errors when performing three predictions.They check the
results for low errors and high accuracy, and suggest improving
the preprocessing or enlarging the training size and
trying again in case of low accuracy and errors.
More recently, the research proposes the use of text mining
on financial news in order to predict the stock market
[1, 7, 6] . They state that news contents are one of the essential
aspects that control the market. The authors in [1]
discuss various techniques that are applied to study the consequence
of financial news on prediction of stock market.
They claim that if the classifier input has both news and
stock prices at the same time, it results in more precise results.
The work of [7] looks at the role of financial news
articles on three textual representations; bag of words, noun
phrases, and named entities and their capacity to predict discrete
number stock prices twenty minutes after an article release.
Using an SVM derivative, the writers prove that their
model had a major impact on predicting future stock prices
compared to linear regression. In [6], the authors propose a
text mining-based financial news analysis system to identify
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