=Paper=
{{Paper
|id=Vol-1774/MIDAS2016_paper9
|storemode=property
|title=Good news and bad news: Do online investor sentiments reaction to return news asymmetric?
|pdfUrl=https://ceur-ws.org/Vol-1774/MIDAS2016_paper9.pdf
|volume=Vol-1774
|authors=Alya Al Nasseri,Faek Menla Ali,Allan Tucker
|dblpUrl=https://dblp.org/rec/conf/pkdd/NasseriAT16
}}
==Good news and bad news: Do online investor sentiments reaction to return news asymmetric?==
https://ceur-ws.org/Vol-1774/MIDAS2016_paper9.pdf
Good news and bad news: Do online investor
sentiments reaction to return news asymmetric?
ALYA AL NASSERI1, FAEK MENLA ALI2, and ALLAN TUCKER3
1
Brunel Business School, Brunel University London, UK
2
Department of Economics and Finance, Brunel University London, UK
3
Department of Information Systems, Brunel University London, UK
Abstract
There is growing evidence to suggest that the impact of positive and negative
news are asymmetric- that the impact of bad news has a much greater effect on
investors’ sentiment than positive news does; investors react more harshly when
bad news is disseminated. Using daily data from 30 companies listed on the
DJIA index over the period April 3, 2012 to April 5, 2013, we analyse 289,443
online tweets, from the so-called StockTwits, and construct a measure of online
investor sentiment. The aim of this paper is to explore the asymmetric responses
of online investor sentiments to different news in different market conditions
(i.e. bull versus bear market). Applying data mining techniques for sentiment
detection coupled with a nonlinear econometric model, we find strong evidence
of asymmetry. The result provides evidence that investor sentiments exhibit dif-
ferent sensitivity to news in the bull and bear market. In particular, return ef-
fects on sentiment are positive (negative) in the bull and (bear) market where
this effect is more pronounced in the bull market.
1 Introduction
Over the past few decades, a growing body of literature has focused the debate on
how fast information is incorporated into security prices (Fama, 1965). Most of these
studies are based on the assumptions underlying the two leading theories in financial
economics: Efficient Market Hypothesis (EMH) and Random Walk (RW) theory.
However, the largest criticism of these theories is that both do not incorporate the
behavioural component in their models and that news is treated as neutral infor-
mation. Psychological researchers however argue that the effect of different types of
news (good or bad) certainly affect individual sentiments of that news item. A large
body of recent finance literature however, recognize the various affects of news on
investor sentiments and have provided empirical evidence that while news undoubted-
ly influences security prices in the stock market, its impact on public mood and emo-
tions (sentiments) may play an equally important role (i.e. Baker and Wurgler, 2007;
Brown and Cilff 2004; Verma Verma, 2007). Behavioural finance has provided evi-
dence that noise investors’ emotions, preferences and mistaken beliefs can affect the
decisions of other investors in the market and may result in shifting the asset’s value
from its fundamental level. The extreme deviations from fundamentals may be a re-
sult of noise traders overreacting or under-reacting to good and bad news, causing
price levels and risk to deviate far more drastically from expected levels than would
have been actually required by the news. Despite the well-recognized literature on the
impact of news on sentiments, some previous studies treat news information as neu-
tral and rarely differentiate between good and bad news (Bowman, 1983, among oth-
ers). It is unlikely that investors’ responses to positive and negative information are
symmetric. It has been argued that news of all type (i.e., positive vs. negative) should
have different impact on investor sentiments. Therefore, it is unaspiringly that inves-
tors shows different reactions depending on the types of news released in the market.
Empirical evidence in the context of macro economic news, firm-specific news argues
that investors’ responses to positive and negative information are asymmetric and that
negative news has a more significant and harsh affect than does positive news (for a
summary, see Soroka, (2006) and Pritamani and Singal, (2001)). Using news releases
on traditional sources of information such as those in the Wall Street Journal and
Newswire (e.g. Tetlock, 2007) show that news sentiment has an effect on market reac-
tions and that news of negative nature has a very significant influence on some market
indicators such as market liquidity.
The contribution of the present study to the existing literature is threefold. First we
examine the impact of different news (good vs. bad news) on investor sentiments by
using relatively new data from an online stock forum (StockTwits). The high volume
of message posts, the real-time message streams and the efficient diffusion mecha-
nism of information are the three distinct features of the stock micro-blogging forum.
Therefore, our sentiment measure reflects the natural market conversations while
concurrently distinguishing between good and bad news information. Second, Our
empirical methodology accounts for possible asymmetries in the effects of market
news on investor sentiments in different states of the market by employing the nonlin-
ear model to examine the effect of news returns on investor sentiments in two differ-
ent regimes; bull and bear market periods. Third, this paper seeks to draw research in
behavioural finance together with research from data mining and build a more thor-
ough account of the impact and magnitude of effects of asymmetric responses of in-
vestor’s sentiments to different types of news. In particular, this paper combines data
mining techniques with financial econometric modelling to investigate the asymme-
tries evident in online investors’ opinion to different types of market news.
The paper is organised as follows. Section 2 reviews the related literature on online
investing forums and different classification algorithms. Section 3 presents the data,
the classification method employed, and our investor sentiment measure. Section 4
describes the simple Markov- switching model of returns to estimate the bull and bear
market regimes. Section 5 presents and discusses the empirical results. Finally, Sec-
tion 6 provides some concluding remarks.
2 Related Work
2.1 Online Investment Forum and Stock Market
A growing body of empirical research has been undertaken to investigate the predic-
tive power of online investing forums in predicting various financial market indica-
tors; all of these papers have focused on message boards, financial news articles and
recently on micro-blogging forums. Internet message board is one of the most popu-
lar investment forums that provides an effective means for investors to communicate,
disseminate and discover information (Delort et al., 2012) Previous research studies
have begun to explore the impact of stock message boards on financial markets and
stock price behaviour (Wysocki, 1998; Tumarkin and Whitelaw, 2001; Antweiler and
Frank, 2004). The two initial papers to analytically investigate Internet posting were
Wysocki (1998) and Tumarkin and Whitelaw (2001) who measured the correlation
between the message volume and the next day trading volume and returns. Their find-
ings reveal that firms with high volume postings characterized as high market valua-
tion with high return and accounting performance, high volatility and trading volume.
One major criticism of the above-mentioned studies is that they rely too heavily on
quantitative data of internet message boards such as message volume and users’ rat-
ings. Unlike previous works, the most complete study of Internet message boards is
by Antweiler and Frank (2004), who focused on qualitative as well as quantitative
data analysis of the internet messages posted on Yahoo Finance and Raging Bull.
They determined the correlation between activity on Internet message boards and
stock volatility and trading volume. They found that positive shocks to message board
posting levels do predict negative stock returns the following day. The literature relat-
ed that the impact of financial news articles and investment stories on stock markets
are vast. Gidofalvi (2001) presents an approach for investigating the relationship be-
tween the financial news articles and short-term price movement. Similar patterns
adopted by Schumaker and Chen (2009), who provided evidence that validate the
importance of news on the performance of stock prices. Recently, with the pragmatic
innovation of stock micro-blogging forums around the world, platforms like Stock-
Twits and TweetTrader have spread widely as an online discussion forum among
investors and traders. A study by Sprenger and Welpe (2014) investigate the relation-
ship of market prices of publicly traded companies with the StockTwits sentiment.
They show that sentiment of StockTwits (i.e. bullishness) is significantly associated
with abnormal stock returns and message volume, while that sentiment has power in
predicting the next day trading volume.
3 Methods & Data
3.1 Classifier Algorithms
Three different classifier algorithms are applied to the sentiment detection process in
this paper, namely: Naïve Bayes (NB), Decision Tree (DT) and Support Vector Ma-
chine (SVM). The following subsection will elaborate in more detail the three models
of machine-learning classifiers
• Naive Bayes Classifier
A Naive Bayes classifier is a simple classifier technique based on Bayes’ Theorem.
It is based on the naive assumption, which states that a given attribute is independent
of the other attributes contained in a given sample, and it considers each of these at-
tributes discretely when classifying a new incoming instance. The Naive Bayes algo-
rithm is based on the joined probabilities of words or a document belonging to a class
in a given text (Witten et al., 1999).
• Decision Tree Classifier
The decision tree method is one of the most frequently used techniques for classifi-
cation problems. It exploits a tree structure consisting of nodes, leaves and branches.
Decision trees used for classification problems are often called classification trees
where each node represents the predicted class of a given feature. It applies the con-
cept of information gain or entropy reduction, which is based on the selection of a
decision node and further splitting the nodes into sub-nodes. The normalised infor-
mation gain is an impurity-based criterion that uses the entropy measure (Rokach and
Maimon, 2005) to evaluate the effectiveness of an attribute for splitting the data. The-
se criteria state that the attribute with the greatest normalised information gain is cho-
sen to make the decision.
• Support Vector Machines (SVMs).
Support vector machines (SVM) are the most widely used techniques for textual
analysis applications; they have proven excellent empirical success with strong theo-
retical foundations (Tong and Koller, 2002). The primary aim of SVM is to find a
maximum hyperplane, which clearly separates the instances and non-instances of a
given class relative to the target variables (Barakat and Bradley, 2007). This common
approach is generally used when the instances of the target variables are described as
linearly separable whereby the target variable should have only two class values. On
the other hand, there are some cases where the target variable may have more than
two class values where, in this case, described as nonlinearly separable. For a non-
linearly separable data, SVM makes use of Kernel methods to transform the data from
an input space or parametric space into a high-dimensional feature space. SVM at-
tempts to maximise the margin space between the separating hyperplane and the data
instances.
3.2 StockTwits Data
One year of StockTwits data about the companies listed on the DJIA Index are col-
lected for the period April 3rd 2012 – 5thApril 2013.The sample period consists of
252 days only because the U.S. stock market is idle on weekends and national holi-
days. In line with Antweiler and Frank (2004), messages are aligned with US market
hours; messages posted after 4:00 pm (market closing) are combined together with
pre-market messages up to 9:30 am (market opening) on the following trading day.
Figure 1 shows the distribution of tweet messages, where panels A, B and C display
such message postings respectively over the sample period of one year, over the days
of the week, and over the hours of the day. A graphical inspection suggests that the
StockTwits postings are reasonably stable over the considered period of study. None-
theless, some increase in the volume of postings is observed during the early summer,
the autumn months (i.e., Halloween), Christmas, and New Year’s Eve (see panel A),
suggesting that people tend to post more actively during these special occasions.
Moreover, consistent with previous studies (e.g., Oh and Sheng, 2001), the volume of
tweets posted during working days is high (i.e., reaching a peak on Thursdays), as
opposed to the low volume of postings observed during the weekend and on public
holidays (see panel B). It is also evident that message postings are concentrated be-
tween 10:00am and 5:00pm (see panel C), which suggests the high activity of day
traders; hence, more sentiment is developed during the market hours.
Panel A: The evolution of daily StockTwits messages (posting activity of 30 companies of the
DJIA index combined)
Panel B: The distribution of StockTwits posts Panel C: The percentage of postings of
throughout the week (average postings of all the 30 companies of the DJIA index
companies in the sample are considered across during the daytime
days of the week)
Fig. 1. The distribution of StockTwits postings
3.3 StockTwits Messages’ Manual Labelling
In order to manage the huge amount of StockTwits messages collected for this
study, a random selection of a representative sample of 2,892 tweets on all 30 stocks
on the Dow Jones Index are hand-labelled as either buy, hold or sell signals based on
a redefined dictionary (Harvard-IV-4 classification dictionary). These hand-labelled
messages constitute the training set, which is then used as an input for what will be
used as training set for different machine learning models. The results of the percent-
age allocation of the manual classifications of tweet messages into the three distinct
classes are shown in Table 1. Table 1 shows that roughly half of these messages were
considered to be “buy” signals (47.06%). The remaining messages for “sell” signals
were (32.54%) which roughly constitute three quarters of “buy” signals whereas the
“hold” signals were (20.40%).
Table 1. Number and percentage distribution oft he manual classification of posting
The results of the study indicate that the stock micro-blogging forum seems to be
more balanced in terms of the distributions of buy vs. sell messages than internet mes-
sage boards where the ratio of buy vs. sell signals appears to be unbalanced, ranging
from 7:1 (Dewally, 2003) to 5:1 (Antweiler and Frank, 2004). The finding that “hold”
messages constitute a relatively small percentage of 20.40%, does not confirm that of
Sprenger et al. (2014), who found that almost half of the messages manually classified
were considered to be “hold” signals. It follows that the higher distribution of buy and
sell messages may provide evidence that there is more relevant financial information
present in such forums.
3.4 Automated Classification
To extract the bullishness measure that serves as a proxy for investor sentiment,
each message has to be classified into one of the three distinct classes {sell, buy or
hold}. Our StockTwits data contain nearly 300,000 text messages - far too many to
classify manually. To manage the message classification task, we employ well-
recognised methods from computational linguistics. Unlike previous studies (i.e.
Antweiler and Frank (2004) and Sprenger et al. (2014)) that use Naïve Bayes Classi-
fiers for classifying messages, our study takes a different approach by comparing the
classification performance of three different machine algorithms: (NB), (DT) and
(SVM).1 Training the selected sample of StockTwits messages in Weka using the
three machine learning algorithms (NB, DT and SVM) reveals that the (Random For-
est) Decision Trees classifier results in a higher accuracy rate compared to the other
two classifier algorithms. Table 2 presents a consolidated summary of all performance
metrics of the three classifiers. It is evident that there is no clear winning classifier in
terms of the performance evaluation method used, yet the Decision Tree classifier is
possibly the best classifier in terms of almost all the metrics. The 10-fold cross-
validation experiments achieved accuracy rates of 66.70%, 62.80% and 65.20%,
where 1,929, 1,815 and 1,887 instances were correctly classified out of 2,892 for
RandF, NB and SMO, respectively. It follows that the RandF decision tree classifier
outperforms the NB and SMO counterparts in predicting investor sentiment class (i.e.,
buy, hold and sell) of StockTwits postings. The weighted averages of the three classes
of RandF classifier are also reported in Table 2, achieving 65.50%, 66.70% and
66.20% for precision, recall and F-measures, respectively. Figure 2 shows the graph-
1
In normal settings, machine learning algorithms are designed for the purpose of maximising the classifica-
tion accuracy and minimising the error rate as far as possible (Kukar and Kononenko, 1998).
ical representation of the comparative performance of the three discussed classifiers
using some of the important measures given in Table 2.
Table 2. Summary Resuls of the classification performance evaluation of NB, RandF and SMO
Fig. 2. Comparative Performance of NB, RandF and SMO classifiers
Further, to make sure that our classification accuracy is good enough, we perform
an out-of-sample testing. In Weka, training on the first ten months of the year and
testing on the remaining two months. Table 3 provides a comparison of the manual
classification of hold-out messages and the automated classification of the Random
Forest algorithm. The results suggest that the Random Forest algorithm preforms
reasonably well, as indicated by the relatively small numbers of misclassifications in
each sentiment class. Finally, Table 4 shows the assigned labels for the entire set of
StockTwits postings. The reported postings are 140,350, 26,157, and 122,517 for
those of ‘buy’, ‘hold’ and ‘sell’, respectively2.
Table 3. Overall classification distribution of Random Forest (supplied test)
Table 4. The Overall distribution of the total StockTwits postings of 30 companies of the DJIA
index
2
Following Antweiler and Frank (2004), the ‘hold’ postings are removed from the analysis as they are
considered noise and convey neutral opinions.
3.5 Investor Sentiment Measure
In this research paper bullishness measure is extracted from StockTwits data which
then be used as a proxy for investor sentiment. In the stock market, bullishness can
be defined as optimism that a particular investment is potentially profitable. The clas-
sification algorithm classified all the tweet messages into three distinct classes 𝑀 !
where 𝑐 ∈ {𝐵𝑢𝑦, 𝐻𝑜𝑙𝑑 , 𝑆𝑒𝑙𝑙 }. The bullishness of messages is an important tweet fea-
ture that determines the proportion of buy and sell signals on a particular day t. It is
!"#
used to aggregate the three different message classes 𝑀! 𝑎𝑛𝑑 𝑀!!"## 𝑎𝑛𝑑 𝑀!!"#$ in a
given time interval. This research study has carried forward the work of Antweiler
and Frank, (2004b) by defining bullishness 𝐵! using three different measures as fol-
lows:
!"# !"#
!! !!!!"## !!!! !!!! !!!!
𝐵! = !"# (1), 𝐵!∗ = 𝑙𝑛 = ln 𝐵! ln (1 + 𝑀! ) (1)
!! !!!!"## !!!!!"## !!!! !!!!
!"#
𝐵!∗∗ = 𝑀! − 𝑀!!"## = 𝐵! 𝑀! (3)
!"#
where 𝑀! indicates the overall number of messages and 𝑀! 𝑎𝑛𝑑 𝑀!!"## indicate
the total number of traders’ messages conveying buy and sell signals on day t respec-
tively. The first bullishness measure is an essential component for obtaining results of
the two other measures while these last two measures are more comprehensive
measures as both take into account the number of messages 𝑀! as well as the ratio of
bullish to bearish messages. The measure 𝐵!∗∗ appears to outperform both alternatives;
hence, this measure is used to measure bullishness, which is used as a proxy for inves-
tor sentiment in this research study. Because a markedly large number of messages
are tweeted on a daily basis, normalisation is therefore needed for these messages as
this will assist the model’s estimation. More specifically, as 𝐵!∗∗ may contain negative
values and in order to take into account such values, the following formula of normal-
isation is considered:
** ( B ** ! !"# B ** )
it i
Bit = 4
(!"# B ** ! !"# B ** )
i i
**
where Bit is the normalised value of bullishness 𝐵 ∗∗ of company 𝑖 at time 𝑡, and max
Bi** and min Bi** indicate respectively the maximum and minimum value of the bull-
ishness measures of company 𝑖 over the sample period. This measure represents the
number of investors’ messages expressing a particular sentiment (buy or sell), giving
more weight to a larger number of messages in a specific sentiment.
3.6 Stock Return Data
The financial data are obtained from Bloomberg for the actively traded blue chip
stocks of the 30 companies making up the DJIA index for the period between April
3rd 2012 and April 5th 2013. No extraordinary market conditions were reported during
this period, so it represents a good base test for the evaluation. The return series of the
DJIA index stocks are computed by taking the first differences of the logarithm of the
daily closing prices, multiplied by 100. There are several reasons why DJIA index is
being focused to adequately reflect the US stock market. One of theses reason is that
DJIA is a price-weighted average of 30 largest market capitalisations of the industrial
companies in the US equity market traded on the NYSE and the NASDAQ. For more
justified reasons see Al-Nasseri et al. (2016).
4 Market Regimes
Following Chen (2007) and Kurov (2010), we estimate the regime switching proba-
bilities using a simple Markov-switching model of stock returns to identifies the peri-
ods of the two different market regimes namely; bull and bear market as follow;
𝑅! = 𝜇!! + 𝜀! , 𝜀! ∼ 𝑖. 𝑖. 𝑑. 𝑁 0, 𝜎 !!! (5)
where 𝑅! is the daily returns on the DJIA index and 𝑆! is an unobserved dummy
variable that indicates the two different states of market regimes; bull or bear market.
Therefore, 𝜇!! and 𝜎 !!! are the state- dependent mean and variance of returns, respec-
tively. The model in Eq. (5) is used to statistically identify two regimes classifications
based on smoothed probability. These two regimes are; regime 0 with a lower vari-
ance of returns and higher returns so called (bull market) and regime 1 with a higher
variance and lower returns (bear market) as indicated in Figure 3. The mean variances
of the model are estimated jointly with maximum likelihood. Once the model is esti-
mated, regime classification based on smoothed probabilities of bull and bear market
at different point in time are computed to identifies a periods of each regimes states
separately.
Fig. 3. Regime classification based on smoothed probability
We create two indicator variables labeled 𝐼 !"## and 𝐼 !"#$ for the bull and bear market
respectively as shown in Eqs. (6a) and (6b):
1 𝑖𝑓 𝑟!" > 0 1 𝑖𝑓 𝑟!" ≤ 0
𝐼!"!"## = 6𝑎 𝐼!"!"#$ = (6𝑏)
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
5 Empirical Results
In order to empirically investigate the asymmetrical response of investor sentiments
to returns in different states of the market, the bullishness equation is estimated by
including two market regimes (bull and bear market) to show whether the returns help
to explain investor sentiment. Both interaction terms as defined previously in Eqs.
(19a and 19b) are added in the bullishness equations as follows:
∗∗ ∗∗
𝐵!" = 𝛼! + 𝜙𝐵!"!! + 𝛾! 𝐼!"!"## 𝑟!" + 𝛾! 𝐼!"!"#$ 𝑟!" + 𝛽! 𝑁𝑊𝐾! + 𝛽! 𝑀𝐾𝑇! + 𝜀!" (7)
The results presented in Table 5 indicate that the model specification in Eq. (7) suf-
fers from serial autocorrelation in the data series. Therefore, the model is modified to
include lagged bullishness to assist in removing the serial correlations (Dickey and
Fuller, 1979). The panel regression with company fixed effects is used where the
market index and first-day-of-the week dummy were added to the regression to con-
trol for the market-wide effect and the negative return on the first trading day of the
week, respectively. The results reported in Table 5 show that bullishness tends to
respond to stock returns positively in the bull market and negatively in the bear mar-
ket. The significant positive coefficient of 𝛾! = +0.0157 indicates that positive returns
trigger an increase in investor bullishness in the bull market by 1.57 %, while the
negative coefficient of 𝛾! = -0.0122 implies therefore that a negative return triggers a
reduction in investor bullishness by 1.22% in the bear market. A possible explanation
for this is that, when the stock return 𝑟! is positive (negative), investor bullishness
exhibits a pronounced increase (decrease), which implies that in the bull market an
investor becomes more bullish whereas in the bear market investor is likely become
more bearish. These findings are in line with the subjective evidence: When the mar-
ket is on a bull run as it was in the late 1990s, investors appear to become more bull-
ish. This finding is consistent with (De Bondt, 1993) who found that increased bull-
ishness could be expected after a market rise and increased bearishness after a market
fall. This evidence is also in line with the existence of bandwagon effect (Brown and
Cliff, 2004), which states that good returns in a given period drive optimism and they
found that stock returns predict sentiments. The magnitude effects of the impact of
returns on bullishness appear to be greater in the bull market compared to bear mar-
ket. This finding is in line with Verma and Verma (2007) who found a stronger effect
on bullish sentiments during the period of positive return (growth) than the effects on
bearishness during the period of negative return (decline).
Table 5. The asymmetric response of the investor sentiment to the change in stock returns in
the bull and bear markets.
Note (*), (**), and (***) denote significance levels at 10%, 5%, and 1%, respectively. Standard errors are
shown in parentheses
6 Conclusion
In this paper we proposed a novel approach by combining various text-mining tech-
niques and financial econometric modelling to investigate the asymmetric behaviour
of investor on news sentiments in two different state of the market. This paper seeks
to answer three interrelated questions. Can text-mining techniques accurately predict
sentiment on StockTwits? Do investor reactions to good and bad news are asymmet-
ric? Do investor react more harshly when bad news is disseminated? Our findings
provide significant evidence of the effectiveness of different classifiers algorithms in
predicting online investor sentiments in financial market. Despite the well perfor-
mance of all of our three classifiers algorithms (NB, RandF and SMO), the Random
Forest classifier however achieved the best results and proves capable in predicting
sentiments of online financial text. Our findings show that investor behave asymmet-
rically to the good and bad news in the bull and bear market. We find that investor
sentiment show positive (negative) impact to news in the bull and bear market respec-
tively. Furthermore, our result indicates that investors are more sensitive to positive
news and react much more positively in the bull market than otherwise do in the bear
market. Overall our results are consistent with the conjecture of Kurov (2010) that
investor shows asymmetric response to information news in the market and that effect
are very dependent on the market regime since there is a greater positive impact on
bullishness during the period of growth in the bull market than the negative impact on
bullishness during the period of decline in the bear market.
References
1. Al-Nasseri, A., Menla Ali, F. and Tucker, A. (2016) Dispersion of stock returns and inves-
tor sentiment: StockTwits evidence, Unpublished Paper.
2. Antweiler, W. and Frank, M.Z. 2004b. Is all that talk just noise? The information content
of Internet stock message boards. The Journal of Finance, 59(3), pp. 1259-1294.
3. Baker, M. and Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Eco-
nomic Perspectives, 21(2): 129–151.
4. Barakat, N.H. and Bradley, A.P. 2007. Rule extraction from support vector machines: a
sequential covering approach. Knowledge and Data Engineering, IEEE Transactions on,
19(6), pp. 729-741.
5. Bennett, K.P. and Mangasarian, O.L., 1992. Robust linear programming discrimination of
two linearly inseparable sets. Optimization methods and software, 1(1), pp.23-34.
6. Dickey, D.A. and Fuller, W.A. 1979. Distribution of the estimators for autoregressive time
series with a unit root. Journal of the American statistical association, 74(366a), pp. 427-
431.
7. Bowman, R. G. (1983). Understanding and Conducting Event Studies. Journal of Business
Finance and Accounting, 10 (4), pp. 561–84.
8. Brown, G.W. and Cliff, M.T. 2004. Investor sentiment and the near-term stock market.
Journal of Empirical Finance, 11(1), pp. 1-27.
9. Chen, S.S., 2007. Does monetary policy have asymmetric effects on stock returns? Jour-
nal of Money, Credit and Banking 39, 667–688.
10. Cortes, C. and Vapnik, V. 1995. Support vector machine. Machine Learning, 20(3), pp.
273-297.
11. De Bondt, W.P. 1993. Betting on trends: Intuitive forecasts of financial risk and return. In-
ternational Journal of Forecasting, 9(3), pp. 355-371.
12. Delort, J., Arunasalam, B., Leung, H. and Milosavljevic, M. 2012. The impact of manipu-
lation in Internet stock message boards. International Journal of Banking and Finance,
8(4), pp. 1.
13. Dewally, M. 2003. Internet investment advice: Investing with a rock of salt. Financial An-
alysts Journal, 59(4), pp.65-77.
14. Fama, E.F. 1965. The behavior of stock-market prices. The journal of Business, 38(1), pp.
34-105.
15. Gidofalvi, G. and Elkan, C. 2001. Using news articles to predict stock price movements.
Department of Computer Science and Engineering, University of California, San Diego.
16. Kukar, M. and Kononenko, I. 1998. Cost-Sensitive Learning with Neural Networks. ECAI
1998, pp. 445-449.
17. Kurov, A. 2010. Investor sentiment and the stock market’s reaction to monetary policy.
Journal of Banking and Finance, 34(1), pp. 139-149.
18. Oh, C. and Sheng, O. (2011), “Investigating Predictive Power of Stock Micro Blog Senti-
ment in Forecasting Future Stock Price Directional Movement”, in 32ndInternational Con-
ference on Information Systems, AIS, p.17.
19. Pritamani, M. and V. Singal (2001). Return Predictability Following Large Price Changes
and Information Releases. Journal of Banking and Finance, 25, pp. 631–51.
20. Rokach, L. and Maimon, O. 2005. Decision trees. Data Mining and Knowledge Discovery
Handbook. Springer, pp. 165-192.
21. Schumaker, R.P. and Chen, H. 2009. Textual analysis of stock market prediction using
breaking financial news: The AZFin text system. ACM Transactions on Information Sys-
tems (TOIS), 27(2), pp. 12.
22. Soroka, S. N. (2006). Good News and Bad News: Asymmetric Responses to Economic In-
formation. Journal of Politics, 68, pp. 372–85.
23. Sprenger, T.O., Tumasjan, A., Sandner, P.G. and Welpe, I.M. 2014. Tweets and trades:
The information content of stock microblogs. European Financial Management, 20(5), pp.
926-957.
24. Tetlock, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the
Stock Market. The Journal of Finance, 62, pp. 1,139–1,168
25. Tong, S. and Koller, D. 2002. Support vector machine active learning with applications to
text classification. The Journal of Machine Learning Research, 2, pp. 45-66.
26. Tumarkin, R. and Whitelaw, R.F., 2001. News or noise? Internet postings and stock prices.
Financial Analysts Journal, pp. 41-51.
27. Witten, I.H., Frank, E., Trigg, L.E., Hall, M.A., Holmes, G. and Cunningham, S.J., 1999.
Weka: Practical machine learning tools and techniques with Java implementations. Mor-
gan Kaufmann, San Francisco.
28. Wysocki, P. 1998. Cheap talk on the web: The determinants of postings on stock message
boards. University of Michigan Business School Working Paper.
29. Verma, R. and Verma, P. 2007. Noise trading and stock market volatility. Journal of Mul-
tinational Financial Management, 17(3), pp. 231-243.