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

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

• The Journal of Asian Finance, Economics and Business
• /
• v.5 no.4
• /
• pp.45-56
• /
• 2018

The paper aims to examine relationships between search-based sentiment and stock market reactions in Vietnam. This study constructs an internet search-based measure of sentiment and examines its relationship with Vietnamese stock market returns. The sentiment index is derived from Google Trends' Search Volume Index of financial and economic terms that Vietnamese searched from January 2011 to June 2018. Consistent with prediction from sentiment theories, the study documents significant short-term reversals across three major stock indices. The difference from previous literature is that Vietnam stock market absorbs the contemporaneous decline slower while the subsequent rebound happens within a day. The results of the study suggest that the sentiment-induced effect is mainly driven by pessimism. On the other hand, optimistic investors seem to delay in taking their investment action until the market corrects. The study proposes a unified explanation for our findings based on the overreaction hypothesis of the bearish group and the strategic delay of the optimistic group. The findings of the study contribute to the behavioral finance strand that studies the role of sentiment in emerging financial markets, where noise traders and limits to arbitrage are more obvious. They also encourage the continuous application of search data to explore other investor behaviors in securities markets.

• Land and Housing Review
• /
• v.13 no.4
• /
• pp.45-53
• /
• 2022

This paper investigates empirically the lead-lag relation between the 'apartment price index' and 'Internet search volume'. This study uses Naver Trend Index as a proxy for Internet search volume. An increase in Internet search volume on the apartment price index indicates an increase in people's attention to an apartment. Different from previous studies exploring the relation between 'the released price index of the apartment' and 'Naver Trend Index', this study investigates the relation of the Naver Trend Index with 'the fundamental price component of an apartment' and 'the transitory price component of an apartment', respectively. The results of the Granger causality test reveal that there are bidirectional Granger causalities between the 'released price' and Naver Trend Index. In addition, the 'fundamental price component of an apartment' and Naver Trend Index have a feedback relation, while 'the transitory price component of an apartment' Granger causes the Naver Trend Index uni-directionally. The impulse response function analysis indicates that the shock of apartment prices increases Naver Trend Index in the first month. Overall, The close relationship between apartment prices and Naver Trend Index suggests that increases in the movement of apartment prices are positively associated with public attention on the apartment market.

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

• Journal of the Korean Institute of Landscape Architecture
• /
• v.29 no.5
• /
• pp.84-91
• /
• 2001

The purpose of this thesis is to search for appropriate design elements to manage ponds on golf courses. This paper showed that a variable, width, and volume have significant influence on satisfaction(Sig 0.001). This research investgated golf courses in Kyung-ki province. In short, we measured the physical design elements of ponds to bring out major factors which could determine satisfaction of golf courses for golfcourse manager(greenkeeper). The results between satisfaction and physical variable came out as follows; 1, Golfcourse managers(Green keepers) responded to questions that they were satisfied in only 29.9% of the ponds. We found that they considered management very highly. we could evaluated the value of necessity and importance are high, that is importance to manage ponds 2. Some physical design elements(volume, width) increased dependent variable(satisfaction) and others(length, area, circumference, index of shape) decreased dependent variable(satisfaction) 3. Volume has an influence on dependent variable more than depth on index of shape. 4 If the result of \`index of shape\` decreased, the result of \`management satisfaction\` would be high, and when volume is 8500ton∼17000ton, depth is 27m∼3.1m, \`management satisfaction\` would be high. The research findings can be used for planning and designing of golfcourses for designers, and by management for greenkeepers, and will provide pertinent design elements far design of golfcourses. We suggest that the interrelation between ponds and strategic play must be examined in future research.

• The Journal of Society for e-Business Studies
• /
• v.22 no.2
• /
• pp.153-168
• /
• 2017

Recent studies focus on the role of investor attention and transparency in stock-related information in explaining stock return and trading volume. Moreover, recent literatures predict that firm opacity will increase the likelihood of future stock price crashes. In this paper, we investigate, using Naver Trend, the relation between portal search intensity and stock price crash. Using various alternative measures of stock price crash risk and search intensity, we demonstrate that stocks with larger volume of portal search are less likely to experience stock price crashes. These results are consistent with our hypothesis that accumulated firm opacity cause future stock price crash. Finally, our results still hold even after we control for the potential effect of endogeneity in the regression specifications.

• International Journal of Contents
• /
• v.3 no.3
• /
• pp.1-5
• /
• 2007

The main memory DBMS (MMDBMS) efficiently supports various database applications that require high performance since it employs main memory rather than disk as a primary storage. An index manager is an essential sub-component of a DBMS used to speed up the retrieval of objects from a large volume of a database in response to a certain search condition. Previous research efforts on indexing proposed various index structures. However, they hardly dealt with the practical issues occurred in implementing an index manager on a target DBMS. In this paper, we touch these issues and present our experiences in developing the index manager. The main issues are (1) compact representation of an index entry, (2) support of variable-length keys. (3) support of multiple-attribute keys, and (4) support of duplicated keys.

• Journal of Information Technology Services
• /
• v.7 no.3
• /
• pp.145-157
• /
• 2008

The existing XML storage methods which use relational data model, usually store path information for every node type including literal contents in order to keep the structural information of XML documents. Such path information is usually maintained by an inverted index to efficiently process XPath queries for large XML documents. In this study, We propose an improved approach that retrieve information from the large volume of XML documents stored in a relational database, while using a segment-based inverted index for path searches. Our new approach can reduce the number of searching an inverted index for getting target path information. We show the effectiveness of this approach through several experiments that compare XPath query performance with the existing methods.

• The KIPS Transactions:PartD
• /
• v.10D no.3
• /
• pp.501-512
• /
• 2003

This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure than Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequences without false dismissals. It applies the normaliration for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verify that the proposed method significantly outperforms the naive method and scales well in a large volume of image sequence databases.