• The Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.1-14
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• 2015

In this paper, we investigate the relationship between Internet search trends and stock market. Under the assumption that investors may use Internet search engine to obtain information for companies of their interests before taking actual investment actions, the relationship between the changes on Internet search volume and the fluctuation of trading volume as well as stock price of a company is analyzed with actual market data. A search trend investment strategy that reflects the changes on Internet search volume is applied to large enterprises' group and to small and medium enterprises' (SMEs) group, and the correlation between profit rate and trading volume is analyzed for each company group. Our search trend investment strategy has outperformed average stock market returns in both KOSPI and KOSDAQ markets during the seven-year study period (2007~2013). It is also shown that search trend investment strategy is more effective to SMEs than to large enterprises. The relationship between changes on Internet search volume and stock trading volume is stronger at SMEs than at large enterprises.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• The Korea Journal of Herbology
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• v.31 no.4
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• pp.93-100
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• 2016

Objectives : The internet is the most common method to investigate information. It is showed that 75.2% of Internet users of 20s had health information search experience. So this study is aim to understanding of interest of public about the herbal medicine using internet search query volume data.Methods : The Naver that is the top internet portal web service of the Republic of Korea has provided an Internet search query volume data from January 2007 to the current through the Naver data lab (http://datalab.naver.com) service. We have collected search query volume data which was provided by the Naver in 606 herbal medicine names and sorted the data by peak and total search volume.Results : The most frequently searched herbal medicines which has less bias and sorted by peak search volume is 'wasong (와송)'. And the most frequently searched herbal medicines which has less bias and sorted by total search volume is 'hasuo (하수오)'.Conclustions : This study is showed that the rank of interest of public about herbal medicines. Among the above herbal medicines, some herbal medicines had supply issue. And there are some other herbal medicines that had very little demand in Korean medicine market, but highly interested public. So it is necessary to monitor for these herbal medicines which is highly interested of the public. Furthermore if the reliability of the data obtained on the basis of these studies, it is possible to be utilizing herbal medicine monitoring service.

• 한국벤처창업학회:학술대회논문집
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• 2018.04a
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• pp.183-185
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• 2018

Online search has recently become a popular business research field not only because the search volume is used to predict demand, but also consumer search history is effective to predict product prices and investment returns. This study analyzes the relationship between the Internet search volume of IPO stocks and their post-IPO stock returns in Korean Exchange. We find that the lower the amount of Internet search for stocks before IPO, the higher the stock returns after IPO both in short and long-term. Similar results are shown for excess returns over benchmark stocks. This finding suggests that IPO stocks with low investors' attention based on the Internet search volume may be undervalued.

• Journal of Korean Foot and Ankle Society
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• v.25 no.3
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• pp.126-132
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• 2021

Purpose: This study examined whether there are seasonal variations in the number of plantar fasciitis cases from the database of the Korean Health Insurance Review & Assessment Service and an internet search of the volume data related to plantar fasciitis and whether there are correlations between variations. Materials and Methods: The number of plantar fasciitis cases per month was acquired from the Korean Health Insurance Review & Assessment Service from January 2016 to December 2019. The monthly internet relative search volumes for the keywords "plantar fasciitis" and "heel pain" were collected during the same period from DataLab, an internet search query trend service provided by the Korean portal website, Naver. Cosinor analysis was performed to confirm the seasonality of the monthly number of cases and relative search volumes, and Pearson and Spearman correlation analysis was conducted to assess the correlation between them. Results: The number of cases with plantar fasciitis and the relative search volume for the keywords "plantar fasciitis" and "heel pain" all showed significant seasonality (p<0.001), with the highest in the summer and the lowest in the winter. The number of cases with plantar fasciitis was correlated significantly with the relative search volumes of the keywords "plantar fasciitis" (r=0.632; p<0.001) and "heel pain" (r=0.791; p<0.001), respectively. Conclusion: Both the number of cases with plantar fasciitis and the internet search data for related keywords showed seasonality, which was the highest in summer. The number of cases showed a significant correlation with the internet search data for the seasonality of plantar fasciitis. Internet big data could be a complementary resource for researching and monitoring plantar fasciitis.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.93-104
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• 2022

In forecasting realized volatility of the major US stock price indexes (S&P 500, Russell 2000, DJIA, Nasdaq 100), internet search volume reflecting investor's interests and implied volatility are used to improve forecast via a deep learning method of the LSTM. The LSTM method combined with search volume index produces better forecasts than existing standard methods of the vector autoregressive (VAR) and the vector error correction (VEC) models. It also beats the recently proposed vector error correction heterogeneous autoregressive (VECHAR) model which takes advantage of the cointegration relation between realized volatility and implied volatility.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• The Korea Journal of Herbology
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• v.36 no.5
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• pp.1-14
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• 2021

Objectives : From January 2020, when the first confirmed case of COVID-19 in Korea, the use of health information using the Internet is expected to increase. It is expected that there will be a significant change in the general public's interest in Korean herbal medicines for health care. Therefore, in this study, we tried to confirm the change in the search trend of Korean herbal medicines after the COVID-19 epidemic. Methods : Using the "Naver DataLab (http://datalab.naver.com)" service of a Korean portal site Naver, search volume was investigated with 606 Korean herbal medicines as keywords. The search period was from January 2020, right after the onset of COVID-19, to June 2021. The search results were sorted by the peak search volume and the total search volume. Results : 'Cheonsangap (천산갑, 穿山甲, Manitis Squama)' was the most searched Korean herbal medicine in the peak search volume and total search volume with least bias. Conclusions : The problem of supply and demand of Korean herbal medicines of high public interest was identified. Broadcasting and media exposure were the factors that had a big impact on the search volume for Korean herbal medicines. As it was confirmed that the search volume for Korean herbal medicines increased rapidly due to media exposure, it is necessary to provide correct information about Korean herbal medicines, improve public awareness, and manage stable supply and demand based on continuous search trend monitoring.

• Korean Management Science Review
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• v.32 no.3
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• pp.91-103
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• 2015

This study recognizes that there is a correlation between the movement of the financial market and the sentimental changes of the public participating directly or indirectly in the market, and applies the relationship to investment strategies for stock market. The concerns that market participants have about the economy can be transformed to the search terms that internet users query on search engines, and search volume of a specific term over time can be understood as the economic trend of big data. Under the hypothesis that the time when the economic concerns start increasing precedes the decline in the stock market price and vice versa, this study proposes three investment strategies using casuality between price of domestic stock market and search volume from Naver trends, and verifies the hypothesis. The computational results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior in domestic stock market.