Organizations - Google搜尋趨勢與認知金融：從臺灣股票市場學到的新知

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(2010).

3. Organizations

The remainder of the thesis is organized as follows.

In chapter 2, we present the research of behaviour finance that is related to this thesis. In particular, we summarize the psychological factors that our empirical study investigates. Next, we review empirical studies of behaviour finance related to this work.

We also compare their results with ours.

In chapter 3, we explain the research methodology used in this thesis. We first explain the sources of the data and the methods used to process the data. Next, we describe the algorithms used to analyze the data.

In chapter 4, we present the research results and give analysis. In particular, we discuss if our empirical results support the hypotheses proposed in behaviour finance.

We also highlight the new discovery about the Google search behaviours of the investors in the Taiwan Stock Market.

In chapter 5, we summarize our research findings, provide application of the study, and discuss the limitations of the work.

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Background and Related Works

In behaviour finance, various research works have reported that investors’ attention and interview sentiments can affect their decision-making. In Section 1, we discuss about these psychological phenomena associated with investors’ behaviours. It explains the variables we have chosen in our empirical study to investigate these phenomena. In Section 2, we review related empirical studies using Google SVI as investors’ attention, trading volume as the past stock market performance, and various confidence indexes as investors’ interview sentiments to investigate their impacts on different stock markets.

Then, we summarize the chapter in Section 3.

1. Investors’ Psychology and Behaviours

People are not only influenced by the limitation of their mental ability, but also by their sentimental changes. Miller (1977) stated that even if investors have received the same information about a stock, they might hold different opinions on the stock because they have different feelings about the information. Baker and Wurgler (2006) also pointed out that investors’ sentiments have an effect on stock returns.

1.1 Investors’ Attention

Barber and Odean (2007) noted that individual investors would buy stocks that

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attract more attention. They added that a stock could attract more attention through appearing on newspapers/ websites or having abnormal trading volume, or having the highest return in a day. Furthermore, they hypothesized that the phenomenon is due to the fact that it is hard for individual investors to collect information of all stocks, so they only buy the stocks receiving high attention. Barber and Odean (2007) added that when investors sell their stocks, however, this phenomenon doesn’t exist, because investors only pay attention to the stocks they own.

1.2 Investors’ Overconfidence and Over Optimism

Taylor and Brown (1988) pointed out that people tend to be overconfident and overoptimistic in their decision-making. Later, De Bondt and Thaler (1995) demonstrated that the phenomenon exists in the financial field. This happening can be used to explain why when investors are optimistic about the price of a stock, they will lose money in the end. This is because they are under the illusion of other factors, causing them to make wrong judgements about the price of the stock.

1.3 Herding

Barber, Odean, and Zhu (2009) showed that individual investors are highly influenced by one another on their buying and selling behaviours. When they invest in a stock market, they move together to buy or to sell stocks, which is one of the irrational

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behaviours. Moreover, Barber et al. (2008) added that when retail investors buy certain stocks in crowd to raise the stock prices, the price of the stocks would fall later.

Andrade, Chang, and Seasholes (2008) reported that the herding phenomenon exists in the Taiwan stock market.

1.4 Short-term Price Momentum and Long-term Price Reversal

Daniel, Hirshleifer, and Subrahmanyam (1998) proposed a hypothesis that irrational investors could influence short-term stock prices, which can go above or below their fundamental value. They believed that the phenomenon will last for a long time but eventually the stock prices will reverse back to their fundamental value.

1.5 Stock Trading Volume

A number of empirical studies have documented that there is a positive correlation between stock trading volume and the stock’s absolute price changes (see Karpoff,

1987). Lee and Swaminathan (2000) pointed out that the trading volume of a stock could represent the past performance of the stock. They added that if the past performance of a stock is good, the stock would attract more attention and make investors buy the stock more, leading to an increase in trading volume.

2. Related Works

There are other works that also used an empirical approach to study the impact of

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investors’ attention and sentiments on stock markets. First, we focus on works using Google SVI to study different markets, and then compare their results with ours. Next, we discuss about works using trading volume and various market interview sentiment indexes to predict stock performance.

2.1 Google Search Volume Index

Da, Engelberg, and Gao (2009) is the first to conduct research using Google SVI data on the U.S. stock market. They found there is a positive relationship between the SVI and the returns of Russell 3000. They pointed out that the increase of the SVI would increase the turnover of Russell 3000 for two weeks. For the Taiwan stock market, our investigation indicates that the change of SVI can predict the average returns of TAIEX from the first to the fifth week, except the second week. Another study using Google SVI data is on the largest 30 stocks traded in NYSE by Vlastakis and Markellos (2012). They showed that SVI is positively related to the stock trading volume and the stock return volatility of the U.S. stock market. Moreover, they reported that there is a positive link between the information investors received and the risk aversion, based on the expected variance risk premium.

Focused on the German stock market, Bank, Larch, and Peter (2011) also paid attention to risk liquidity and reported that the higher the Google search volume is, the

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higher the trading volume and the more improved stock liquidity are, leading to higher returns of the German stocks in the short run. They believed that attention-grabbing stocks are the subjects of temporary buying interests, causing price pressure and then leading to higher stock prices.

For the Japan stock market, Takeda and Wakao (2014) focused on the relationship between the intensity of SVI, the returns of Nikkei 225, and their trading volume. They reported that the relationship between the SVI and the trading volume is strong but the relation between the SVI and the Nikkei 225 return is not significant, probably because the sampling period included major negative economic shocks, such as the 2008 world financial crisis and the 2011 Great East Japan Earthquake.

For the France stock market, Aouadi, Arouri, and Teulon (2013) conducted a research on the relationship between investors’ attention, based on Google search volume, stock liquidity and volatility, using the CAC 40 index data. The research pointed out that more attention is given to larger sized firms, leading to more liquidity.

Although our research focus on the average returns of TAIEX, not on the stock liquidity or volatility, we decide to apply their research results and used the company size as the weight to compute the weighted sum of SVI to represent investors’ attention.

For the Taiwan stock market, Fan, Liao, and Chen (2014) showed that using the

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top-50 company names as keywords for Google Search, the search volume could predict the average returns of TAIEX. In our research, we found that search volume based on company names of all ranks, not just the top-50, have the power to predict the average returns of TAIEX.

There are many studies that used company names or ticker symbols as keywords in Google search to represent investors’ attention to study different stock markets. Among them, Fan, Liao, and Chen (2014), Vlastakis and Markellos (2012), Bank, Larch, and Peter (2011), and Takeda and Wakao (2014) used company names as the search keywords. They reported that SVI is significantly correlated with the stock returns in their studied markets. By contrast, Da, Engelberg, and Gao (2011) used ticker symbols as the search keywords. They reported their SVI is also significantly correlated to the Russell 3000 index. For the Taiwan stock market, we find that SVIs based on company names of all ranks have the power to predict the average returns of TAIEX.

Additionally, SVIs based on the ticker symbols of companies that are ranked higher than 100 also have a similar predictive power.

2.2 Trading Volume and Market Sentimental Indexes

There are also works focusing on the impact of investors’ sentiments on the financial stock markets. For example, Chuang, Ouyang, and Lo (2010) used much

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theory to support the use of trading volume as an investors’ sentiment index and found that trading volume is a suitable proxy as sentiments. So, trading volume reflects investors’ expectation of the stock prices; hence influences the average returns of the stocks in the Taiwan stock market. Another work is by Chung and Yeh (2009), who did research on the U.S. stock market using many sentiment indexes, such as consumer confidence level¹, the VXO² (the old VIX (Volatility Index)), Baker and Wurgler's orthogonal sentiment index³ to capture consumers’ and investors’ sentiments. They reported that sentiments could be used to predict the stock returns in the U.S. stock market. Our research uses J.P. Morgan confidence indexes as investor’s interview sentiments and also found that two particular investors’ interview sentiments can help predict the average returns of TAIEX.

3. Summary

Based on the established behaviour finance research findings, we have identified several investors’ psychological factors that might have an impact on the average returns of the Taiwan stock market and will incorporate them in our empirical study.

These factors include investor’s interview sentiments, their attention, herding,

1 http://www.sca.isr.umich.edu/

2 http://www.cboe.com/VXO

3 http://people.stern.nyu.edu/jwurgler/

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overconfidence and over optimism. The investors’ attention and herding are reflected on the Google SVI. The investors’ overconfidence and over optimism are reflected on the J.P. Morgan data of Taiwan Stock Price Index. Moreover, we include trading volume in our study to investigate if the past performance of a stock has an impact on its present performance.

We will explain these data and the research methods used in this empirical study in the following chapter.

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Research Methods

To investigate how investors’ attention and interview sentiments influence the average returns of TAIEX, we selected eight independent variables and one dependent variable from three different data sources. Section 1 describes the data from the Taiwan Economic Journal; Section 2 explains the data from the J.P. Morgan Asset Management, and Section 3 discusses the search volume data from Google Trends. In section 4, we explain the econometric method we choose to run the regression. Section 5 summaries the whole chapter.

1. Taiwan Economic Journal Data

Taiwan Economic Journal (TEJ)⁴ is a database that contains historical financial data and information in the major financial markets in Asia. We downloaded weekly average opening price of TAIEX and weekly total trading volume from January 5, 2014 to November 6, 2016 for this research.

2. J. P. Morgan Asset Management Confidence Indexes

We obtained six confidence indexes form J.P. Morgan Asset Management⁵. J.P.

4 http://www.tej.com.tw/twsite/

5hhttps://www.jpmrich.com.tw/wps/portal/!ut/p/b0/04_Sj9CPykssy0xPLMnMz0vMAfGjzOK9AkIDjEJc DQz83XycDIxczIyd3TzcDAycTfULsh0VAXrBmJ0!/?WCM_PORTLET=PC_Z7_JPUP2TE008CAC02

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Morgan Asset Management started investigating Taiwan investors’ sentiments change in 2004. They interview investors from time to time by asking six questions. The interviews are designed to evaluate investors’ confidence in Taiwan economics, politics, and stock market. The results are used to compose six interview sentiment indexes.

Moreover, they set the threshold used to evaluate these indexes as 100. If an index is higher than 100, it means that investors are optimistic about the market. The higher the score is, the more optimistic the investors are. If an index is below 100, it means the investors are pessimistic about the market. The lower the score is, the more pessimistic the investors are. The six confidence indexes are defined as follows:

1. Taiwan Stock Price Index: What is the possibility that TAIEX rises in the future?

2. Taiwan Economic Situation Index: What is the possibility that Taiwan economic situation becomes better in the future?

3. Taiwan Political Index: What is the possibility that the political situation between Taiwan and China becomes more stable in the future?

4. Taiwan Investment Environment Index: What is the possibility that the investment situation in Taiwan becomes better in the future?

DL2KSHG28U1000000_WCM&WCM_GLOBAL_CONTEXT=/wps/wcm/connect/jpmrich/eportal/lb_b 2c_l01/b2c_l01p220/b2c_l01p220_01/at_b2c_l01p220_01-1_00002

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5. Global Economic Index: What is the possibility that the global economic situation becomes better in the future?

6. Possibility of the Value of Your Portfolio Increases within next six months:

What is the possibility that the value of your portfolio increases in the future?

We downloaded the confidence indexes data from 2014 to 2016. The data were mostly quarterly. To work with other data in weekly format, we used frequency conversion method by Litterman (1983) to raise the frequency of these interview sentiment indexes to become weekly using the statistical software Eviews.

The method assumes that an interview sentiment index this week will affect the index next week and their residuals are correlated. These functions are described as follows:

𝑥𝑥𝑖𝑖𝑖𝑖 = 𝑥𝑥𝑖𝑖𝑖𝑖−1+ 𝜀𝜀𝑖𝑖𝑖𝑖, ε𝑖𝑖𝑖𝑖 = ρε𝑖𝑖𝑖𝑖−1+ e𝑖𝑖𝑖𝑖, where ε𝑖𝑖𝑖𝑖~𝑁𝑁(0, V) and 𝑖𝑖 = 3, 4, 5, 6, 7, 8.

We used 𝑥𝑥3𝑖𝑖~𝑥𝑥8𝑖𝑖 to represent the six interview sentiment variables. The variable

𝑥𝑥1𝑖𝑖 was reserved for SVI while the variable 𝑥𝑥2𝑖𝑖 was reserved for trading volume. The initial condition is 𝑥𝑥𝑖𝑖0 = 0. The function is an ARIMA (1,1) model. The last weekly data series was November 6, 2016.

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Google Trends⁶ is a public web facility of Google Inc. (original Google Search) that shows how often a particular search-term (keyword) is entered, relative to the total

search volume across various regions of the world, and in various languages. The SVI

values represent search interest relative to the given region and time. The highest search

number during the downloading period is given SVI value of 100. The weekly SVI is

calculated by dividing the weekly search volumes with the highest search volume

assigned SVI value 100. We can find the details in Google Trends help⁷.

We downloaded two sets of SVI data from Google Trends using two sets of search terms. The first set consisted of the company names of the all companies⁸ listed in TAIEX and the second set contained the ticker symbols of these companies. The following subsections explain the procedures to obtain these data.

3.1 Company Names

A company listed in TAIEX has a full name. However, in the stock market, investors often call these companies by their abbreviated company names. For example,

“台積電” is the abbreviated company name of “台灣積體電路製造股份有限公司”.

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For brevity, we will use company names instead of abbreviated company names in the rest of the thesis. We retrieved the SVI in the Taiwan region using the company name of each company listed in the TAIEX as the search term from January 5, 2014 to November 6, 2016. However, we found some small capital companies do not have any SVI information. Also, some company names are common terms that may be used to conduct Google search by non-investors. For these two kinds of company, we replaced the search results with that obtained using ticker symbols. The total number of companies whose SVI have been replaced under the process was 49.

To sum the search volume data up as a single index, we used a weighted sum approach, where the weight was the company size, represented by their relative percentage of market value on November 18, 2016, downloaded from Taiwan Futures Exchange⁹. This approach is based on the following assumptions:

• Each search volume is independent. Increased attention on one stock will not influence others.

• The higher a company’s market value is, the more attention the company receives and hence the higher the search volume.

• The companies that constitute TAIEX remain unchanged.

After we summed up the weighted SVI of all companies, the time series contained

9 http://www.taifex.com.tw/chinese/9/9_7_1.asp

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146 weeks of data.

3.2 Ticker Symbols

A company listed in TAIEX also has a ticker symbol. For example, “2330” is the ticker symbol of “台積電”. We first used ticker symbols to retrieve their SVI from Google Trends. Next, we used the similar procedures described in the previous section to obtain the weighted SVI of all companies. The time series also had 146 weeks of data.

4. Newey-West Correction of Standard Errors

Newey-West correction of standard errors method¹⁰ is a method to estimate the coefficients of a linear regression model applied to time series data. It is used to correct autocorrelation (also called serial correlation) and heteroskedasticity in the error terms in the regression model. We used the statistical software SAS to run the regression.

Below are the variables names and their meaning.

y：average returns of TAIEX 𝑥𝑥1: Google SVI

𝑥𝑥2: trading volume of TAIEX 𝑥𝑥3: Taiwan Stock Price Index

10 Please refer to the appendix to know the details.

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𝑥𝑥4: Taiwan Economic Situation Index 𝑥𝑥5: Taiwan Political Index

𝑥𝑥6: Taiwan Investment Environment Index 𝑥𝑥7: Global Economic Index

𝑥𝑥8: Possibility of the Value of Your Portfolio Increases within six months We first converted all time series data into log10, and then used the difference between adjacent weeks ( 𝑥𝑥𝑖𝑖𝑖𝑖− 𝑥𝑥𝑖𝑖𝑖𝑖−1) to run the regression. In this way, the interpretation of the regression is easier: 1 percentage change of an independent variable will change a certain percentage of the dependent variable, specified by the coefficient of the independent variable in the regression model. Moreover, we can reduce the scale difference of the variables; hence increase predicting accuracy.

The linear regression model is as follows:

y_𝑖𝑖 = 𝛽𝛽₀+ ∑⁸_𝑖𝑖=1𝛽𝛽_𝑖𝑖𝑥𝑥_{𝑖𝑖𝑖𝑖}+ e_𝑖𝑖, and where t = 1, 2, ... ,146

We used the regression results to estimate what variables are significant in effecting the average returns of TAIEX and how long the effect lasts. Because the residuals had heteroscedasticity and autocorrelation, the method used HAC (Heteroskedasticity and Autocorrelation Consistent) estimators to correct them. We

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used Parzen kernel, an HAC estimator, to run the regression. The relationship between Newey-West correction of standard errors method and Parzen kernel is shown in the appendix.

5. Summary

In the chapter, we have explained the data sources and the methods we used to conduct our research. We downloaded the data from TEJ, Google Trends, and J.P.

Morgan Asset Management to help us test different behaviour hypothesis. After that, we used the frequency conversion method to process J.P. Morgan Asset Management data and the weighted sum approach to process Google Trends SVI data. Finally, we applied Newey-West correction of standard errors method to run the regression on the processed

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