Efficient market hypothesis is one of the most important theory in the finance field.
The core of this theory is that asset price completely reflects all available information (Eugene Fama 1970). It means that no one can beat the market and it is really hard to earn the abnormal return in the long run. However, some critics say the market isn’t efficient and rational. It’s difficult to explain financial crises in recent years like dot com bubble in 2000, subprime mortgage crisis in 2007, European debt crisis in 2011.
The efficient market hypothesis requires that agents have rational expectations that on average the population is correct which is rejected by the theory of behavioral finance.
2.2 Behavioral finance
Prospect theory is one of the most important theories in behavioral finance (Kahneman and Tversky 1979). There are four important points in this paper:(1) Reference dependence: When evaluating outcomes, the decision maker has a reference level. Outcomes are compared to the reference point and classified as
"gains" if greater than the reference point and "losses" if less than the reference point.
(2) Loss aversion: Losses bite more than equivalent gains. In their paper, they found the median coefficient of loss aversion to be about 2.25 times more than equivalent gains. (3) Non-linear probability weighting: Evidence indicates that decision makers overweight small probabilities and underweight large probabilities.(4) Diminishing sensitivity to gains and losses: As the size of the gains and losses relative to the reference point increase in absolute value, the marginal effect on the decision maker's utility falls.
According to the statements above, people aren’t rational and the degree of gains
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or losses may easily influence one’s mind and investment decisions. It means emotions is a significant factor for investors.
2.3 Information and Social Networks
The traditional financial theory mainly focuses on the efficient market hypothesis (EMH). One of the EMH assumptions tells us people are rational that people make their own investment strategies without any emotional problem. However, there is a completely different view in the behavioral finance. The Behavioral finance is a subject that tells us emotions can influence one’s investment decision and there are lots of factors that may influence the investment decision meaning people are not rational. One of the factors is other people’s opinions.
Not only retail investors but also institutional investors usually need to read tons of information from traditional social networks like TV programs of finance, news papers, financial magazines to realize the current condition of the industrial trend or pick up some stocks to put into their portfolios. For example, some professional people talk about their ideas and suggest some stocks to investors and investors may believe the people who are seen as professional people even though they don’t know whether the people are really professional.
During 2000, Yahoo Finance and Raging Bull provided two of the largest and prominent sets of message boards. Werner Antweiler and Murray Z. Frank (2004) used these message boards to study how Internet stock message boards are related to stock markets. First, authors found that a positive shock to message board posting predicts negative returns on the next day. Second, a traditional hypothesis is that disagreement induces trading. Authors found significant evidence supporting this claim. Malcolm
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Baker and Jeffrey Wurgler (2006) told us that when sentiment is estimated to be high, stocks that are attractive to optimists and speculators and at the same time unattractive to arbitrageurs. Wesley S. Chan (2002) told us stocks with public news in a given month experience momentum. Those that do not have public news show no momentum.
In recent years, use of social networks like twitter and PTT has grown exponentially. A person talks about their ideas and suggest some stocks that can be bought or sold on the internet and other people will agree or disagree with that. The people who agree may really buy or sell the stocks suggested. It means that people’s emotions may be triggered or changed by other people’s opinions.
Huina Mao, Scott Counts and Johan Bollen (2011) survey a range of online data sets (Twitter feeds, news headlines, and volumes of Google search queries) and sentiment tracking methods (Twitter Investor Sentiment, Negative News Sentiment and Tweet & Google Search volumes of financial terms), and compare their value for financial prediction of market indices such as the Dow Jones Industrial Average, trading volumes, and market volatility (VIX), as well as gold prices. . The results show that traditional surveys of Investor Intelligence are lagging indicators of the financial markets. However, weekly Google Insight Search volumes on financial search queries do have predictive value. An indicator of Twitter Investor Sentiment and the frequency of occurrence of financial terms on Twitter in the previous 1-2 days are also found to be very statistically significant predictors of daily market log return. Survey sentiment indicators are however found not to be statistically significant predictors of financial market values, once we control for all other mood indicators as well as the VIX. Z. Da, J. Engelberand, and P. Gao (2010) told us that fear sentiment predicts (i) short-term return reversals, (ii) temporary increases in volatility, and (iii) mutual fund flows out of equity funds and into bond funds.
There are two mood tracking tools called OpinionFinder (OF) that measures
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measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy).Johan Bollen, Huina Mao and Xiao-Jun Zeng (2010) used the two softwares above to test the sentence of twitter and investigated the hypothesis that public mood states are predictive of changes in DJIA closing values. Their results indicate that the accuracy of DJIA predictions can be significantly improved by the specific public mood but not others. They find an accuracy improvement that predicts the daily up and down changes in the closing values of the DJIA. OF is a publicly available software package for sentiment analysis that can be applied to determine sentence is positive or negative.
However, there are only two kinds of sentences (positive and negative) if they use OF.
Consequently, to obtain additional dimensions of public mood, they used another mood analysis tool GPOMS that can measure human mood including 6 different mood dimensions, namely Calm, Alert, Sure, Vital, Kind and Happy. Finally, they do not observe relation between DJIA and OpinionFinder’s assessment of public mood states (positive and negative) but the GPOMS dimension labeled “Calm”. The calmness of the public is predictive of the DJIA which can improve the predictive accuracy of closing values of DJIA.
Tushar Rao and Saket Srivastava (2012) conducted over a period of 14 months between (2010.6.2~2011.7.29) and analyzed 4 million tweets for DJIA, NASDAQ-100 and 13 other big cap technological stocks (Amazon, Apple, AT&T, Dell, EBay, Google, IBM, Intel, Microsoft, Oracle, Samsung Electronics, SAP, Yahoo). These companies are some of the highly traded and discussed technology stocks having very high tweet volumes. They used JSON API from Twittersentiment which is a service provided by Stanford NLP research group to divide all tweets into two parts, positive and negative tweets. The results show that negative and positive dimensions of public mood carry
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high correlation (up to 0.88 for returns) between stock prices and twitter sentiments.
2.4 Summary of Literature Review
In the foreign papers, they use twitter and some software to analyze sentiments.
However, this paper may choose the local social network PTT as the target to study and to know if the result might be different or not. Before studying, we may expect two topics. The first is that the popular bullish articles in PTT may cause the poor performance of the stock index or some stocks mentioned in articles in the future and popular bearish articles in PTT may cause the great performance of the stock index or some stocks mentioned in articles in the future. The second is if there are more bullish articles than bearish articles now, the stock index will be with poor performance in the future. On the contrary, if there are more bearish articles than the bullish articles now, the stock index will be with great performance in the future.
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There are two topics discussing in this paper. The first topic is that there is a type of article called “target” in the stock section of PTT. This paper chooses only this type of articles and read these articles mentioning a specific stock. Then judge the stock of the article for the author is bullish or bearish. If the stock is bullish, the independent variable (X) is one (+1). However, the stock is bearish and the independent variable is minus one (-1). Sum up all the bullish and bearish articles this week to predict the returns of TAIEX (dependent variable: Y) next week to next seven week. For example, it means if there are 5 bullish articles and 3 bearish articles this week, the X is 2.The period of this topic is 2012/10~2016/2.
The method how to judge an article is bullish or bearish is that read an article and the author of the article may mention some statements that can easily know the author is bullish or bearish to a specific stock. For example, the statement is that he/she would stop loss if the price was under a number or stop loss if the stock price was under the 20-day moving average line and we can know that this is a bullish article.
According to the Figure 4, the author obviously told us that the stock 2330 will be bullish in the future. According to the Figure 5, the author didn’t obviously told us the stock is bullish or bearish for us, but we can see that the author said if the price broke through the bottom, he/she would stop loss. Consequently, we can know the stock 2330 is bullish for the author.