• The Korean Journal of Financial Studies
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• v.3 no.1
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• pp.233-265
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• 1996

We examine the impact of public and private information on price movements using the thirty DJIA stocks and twenty-one NASDAQ stocks. We find that the standard deviation of daily returns on information days (dividend announcement, earnings announcement, insider purchase, or insider sale) is much higher than on no-information days. Both public information matters at the NYSE, probably due to masked identification of insiders. Earnings announcement has the greatest impact for both DJIA and NASDAQ stocks, and there is some evidence of positive impact of insider asle on return volatility of NASDAQ stocks. There has been considerable debate, e.g., French and Roll (1986), over whether market volatility is due to public information or private information-the latter gathered through costly search and only revealed through trading. Public information is composed of (1) marketwide public information such as regularly scheduled federal economic announcements (e.g., employment, GNP, leading indicators) and (2) company-specific public information such as dividend and earnings announcements. Policy makers and corporate insiders have a better access to marketwide private information (e.g., a new monetary policy decision made in the Federal Reserve Board meeting) and company-specific private information, respectively, compated to the general public. Ederington and Lee (1993) show that marketwide public information accounts for most of the observed volatility patterns in interest rate and foreign exchange futures markets. Company-specific public information is explored by Patell and Wolfson (1984) and Jennings and Starks (1985). They show that dividend and earnings announcements induce higher than normal volatility in equity prices. Kyle (1985), Admati and Pfleiderer (1988), Barclay, Litzenberger and Warner (1990), Foster and Viswanathan (1990), Back (1992), and Barclay and Warner (1993) show that the private information help by informed traders and revealed through trading influences market volatility. Cornell and Sirri (1992)' and Meulbroek (1992) investigate the actual insider trading activities in a tender offer case and the prosecuted illegal trading cased, respectively. This paper examines the aggregate and individual impact of marketwide information, company-specific public information, and company-specific private information on equity prices. Specifically, we use the thirty common stocks in the Dow Jones Industrial Average (DJIA) and twenty one National Association of Securities Dealers Automated Quotations (NASDAQ) common stocks to examine how their prices react to information. Marketwide information (public and private) is estimated by the movement in the Standard and Poors (S & P) 500 Index price for the DJIA stocks and the movement in the NASDAQ Composite Index price for the NASDAQ stocks. Divedend and earnings announcements are used as a subset of company-specific public information. The trading activity of corporate insiders (major corporate officers, members of the board of directors, and owners of at least 10 percent of any equity class) with an access to private information can be cannot legally trade on private information. Therefore, most insider transactions are not necessarily based on private information. Nevertheless, we hypothesize that market participants observe how insiders trade in order to infer any information that they cannot possess because insiders tend to buy (sell) when they have good (bad) information about their company. For example, Damodaran and Liu (1993) show that insiders of real estate investment trusts buy (sell) after they receive favorable (unfavorable) appraisal news before the information in these appraisals is released to the public. Price discovery in a competitive multiple-dealership market (NASDAQ) would be different from that in a monopolistic specialist system (NYSE). Consequently, we hypothesize that NASDAQ stocks are affected more by private information (or more precisely, insider trading) than the DJIA stocks. In the next section, we describe our choices of the fifty-one stocks and the public and private information set. We also discuss institutional differences between the NYSE and the NASDAQ market. In Section II, we examine the implications of public and private information for the volatility of daily returns of each stock. In Section III, we turn to the question of the relative importance of individual elements of our information set. Further analysis of the five DJIA stocks and the four NASDAQ stocks that are most sensitive to earnings announcements is given in Section IV, and our results are summarized in Section V.

• Archives of design research
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• v.13
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• pp.45-53
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• 1996

After the callapse of the 80's bubble economy. consumers tend to consider the fundamental values of a product such as price, usage, and quality more significantly than ever before. Due to this change in attitude. the most important factor in a consumer's decision for choosing a product becomes the quality of a product that safisfies consumer's practical values whith convincing features and logical differentiations devoted to fundamental values. Under the circumstances. Factor Oriented Process and Multi-Sequential Process are proposede not just as merely defining concept through study of consumers' needs. but as methods of gaining competitive edge and eatablishing corporate identity in market, competition by bringing out consumers' various wants and needs to lead them to a specific product. Factor Oriented Process emphasizes the analysis of factors within the process itself, especially the synthesis of factors which would bring about new solutions as its special feature and acts as a logical element for further design development. Thus, the synthesis process consists of re-organizing analyzed factors, andduring this process, analyzing correlation between the restrictions of factors would lead to discovery of 'dominant factors'. Afterward, design basis may be formed with design concepts proposed by several concept codes made up of one dominant factor and other associate factors. Multi-Sequential Process is an extensive approach to discover differentiated design proposals through careful examination of dominant factors within the product, and furthermor, to discount 'anterior factor' (directional factors that decide design directions based on multi-value criteria) for self-determined decision of design directions.

• Journal of Distribution Science
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• v.17 no.3
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• pp.107-116
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• 2019

Purpose - Why, why is it difficult to predict the appropriateness of self-employment, and what are the countermeasures and policy proposals to overcome. This study intends to further develop the field of statistical variables. It is necessary to overcome the limitation of existing proper scale research in Korea. We need to find statistical variables that can determine the appropriateness of self-employment in Korea. These efforts will be helpful in evaluating OECD countries and statistics and developing domestic economic indicators. Research design, data, and methodology - It is the discovery of statistical indicators and complementary indicators that have not been revealed in previous studies. Therefore, we sought to find new statistical parameters based on the statistics of the Korea National Statistical Office, the Bank of Korea, and overseas OECD statistics. (Proper Size of Adequacy) is defined as the specific gravity or number of the self-employed in Korea, which is shown as "Out Put" by statistical analysis of STATA panel statistical data. It is possible to further develop variables such as gross domestic product, gross national product, economic growth rate, unemployment rate, income tax rate, consumer price, tax level, exports, import amount, bill default I want to dig. Results - In addition to expanding economic indicators that can be explained by self-employment determinants, we have developed a variety of methods such as linear and non-linear (U-shaped, inverted U-shaped). It is the improvement of the self-employment determinants and the analysis method to estimate the appropriate scale. Conclusions - The proposed contents are reflected in self - employment appropriateness evaluation data and hope to help the government to select the policy support and to evaluate the government business after the policy support. These efforts are expected to be of great help to operators operating their own businesses, and to government and related institutional practitioners who support them. In this way, self-employment will be created in accordance with the Korean situation, where the happy life of all the people becomes the premise and the inclusive economic activities are guaranteed. It will improve the method of analyzing proper scale of small business owners and self-employed in Korea.

• Management & Information Systems Review
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• v.31 no.4
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• pp.287-307
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• 2012

We empirically examine the price discovery dynamics among the VKOSPI, the KOSPI200 spot, and the KOSPI200 futures markets. The analysis employs the vector-autoregression, Granger causality, impulse response function, and variance decomposition using both daily data from 2009. 04. 13 to 2011. 12. 30 and 1 minute data from the bull market, bear market, and the flat period. The main results are as follows; First, the lead lag relationships between KOSPI200 spot(futures) yield VKOSPI returns could not be found from the daily data analysis. But KOSPI200 spot(futures) have a predictive power for VKOSPI from 1 minute data. Especially KOSPI200 spot(futures) and VKOSPI show the bi-directional effects to each other during the return rising period Second, We chose the VAR(1) the model in daily data but adopt the VAR(3) model in the one minute data to determine the lead lag time. We know that there is predictability during the very short period Third, Spot returns and futures returns makes no difference in daily data results. According to the one minite data results, VKOSPI returns have a predictive power for KOSPI200 spot return, but have no predictive power for KOSPI200 futures return.

• Journal of Ginseng Culture
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• v.4
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• pp.59-102
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• 2022

Since there were only few countries that could find wild ginseng grown in nature, the culture of digging wild ginseng was only developed in a small number of countries. In a time when the orthodox head-Simmemani has disappeared, the tangible and intangible culture of Simmemani (wild ginseng digger) is disappearing more and more with the passage of time. So far, the conducted research on wild ginseng diggers was very partial and simplistic as follows: ① Research on the argot of Simmemani, ② Research on the customs of Simmemani, ③ Research on the change of customs of Simmemani, ④ Additional records through interview with Simmemani. Accordingly, no comprehensive study on the Simmemani culture has been done yet. This study supplements the historical materials that were not reflected in previous studies, and discuss on diverse subjects including the definition and classification of wild ginseng, the distribution of wild ginseng and Simmemani, interpretation of wild ginseng digging from a legal and social point of view, the organization responsible for the digging, determination of the date of entering the mountain, preparations, taboos, departure and entry into a mountain, religious events, psalmbook, dream interpretation, search and discovery of wild ginseng, digging, profit sharing, the amount of harvested wild ginseng, and the price of wild ginseng. In addition, Korean wild ginseng digging culture was comprehensively studied by attaching the photos and illustrations of historical documents with the psalmbook of the head-Simmemani.

• Management & Information Systems Review
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• v.36 no.3
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• pp.37-49
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• 2017

This study investigates the dynamic relationship between KOSPI200 stock index and stock index futures and stock index option markets which is its derived from KOSPI200 stock index. We use 5-minutes rate of return data from 2012. 06 to 2014. 12. To empirical analysis, this study use autocorrelation and cross-correlation analysis as a preliminary analysis and then following Stoll and Whaley(1990) and Chan(1992), the multiple regression is estimated to examine the lead-lag patterns between the stock index and stock index futures and option markets by Newey and West's(1987) Empirical results of our study shows as follows. First, there exist a strong autocorrelation in the KOSPI200 stock index before 10minutes but a very weak autocorrelation in the stock index futures and option markets. Second, there is a strong evidence that stock index future and option markets lead KOSPI200 stock index in the cross-correlation analysis. Third, based on the multiple regression, the stock index futures and option markets lead the stock index prior to 10-15 minutes and weak evidence that the stock index leads the future and option markets. This results show that the market efficient of KOSPI200 stock index market is improved as compared to the early stage of stock index future and option market.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.