• Review of Culture and Economy
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• v.19 no.2
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• pp.51-72
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• 2016

This research dwells on categorization and industrialization of cultural heritage resources using marketing. Since many organization would not grab the chance of business while doing marketing using cultural heritage, the paper intend to suggest proper way to develop strategy. For that purpose, this research analyzed some advanced cases of corporate like Google and other national endeavors. This paper founded out 4 models of marketing using cultural heritage resources. These are re-creation, patron, capitalization and media. Also we got practical implications of the case analysis and modeling which are collaboration partnership scheme between marketers and filed expert group. Marketers better find fitful information and right person utilizing more reliable sources such as national archives, and academic achievements. If the marketers implement such strategic program, really abundant types of cultural heritage resources using marketing could bring more favorable profit which means business chance and new horizon for global culture industry.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.186-198
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• 2021

The introduction of digital technologies affects most socio-economic processes and activities in the economy, from agriculture to public services. Even though the world is currently only in the early stages of digital transformation, the digital economy is growing rapidly, especially in developing countries. Shortly, digital platforms will be able to replace the "invisible hand" of the market and turn it into digital. Some digital platforms have already reached global reach in some sectors of the economy. The growing value of data and artificial intelligence is reflected in the high capitalization of these enterprises. Their growing role has far-reaching consequences for the organization of economic activity and integration into the field of e-business. However, their importance and level of development in different countries differ significantly. The main purpose of this article is an assessment of the level and trends of the digital economy in the world and the identification of homogeneous groups of states following the main trends in the development of its components from among the EU countries. The methodology of the conducted research is based on the use of general scientific research methods in the analysis of secondary sources and the application of statistical methods of correlation-regression and cluster analysis. Macroeconomic indicators and components of DESI (Digital Economy and Society Index) were used for the analysis. Results. Based on the analysis established that most developed countries have a medium level of digitalization of the business environment and a high level of digitalization of socially oriented public services, while countries with lower GDP focus their policies on building digital infrastructure and training qualified personnel. The study summarizes and analyzes current trends in digital technology, analyzes the level and dynamics of integration of digital technologies of the studied EU countries, the level of development of e-business and e-commerce. The conceptualization of mechanisms of creation of added value in the digital economy is offered and the possible consequences of digitalization of the economy of developing countries are generalized.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.118-124
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• 2021

The article examines the formation of a competitive paradigm of economic security of industrial enterprises in the formation of a circular economy. The basic laws of industrial enterprises are formed, which determined the characteristics of competitive positions and threats. The basic competitive concepts and their application at maintenance of economic safety of the industrial enterprises in the conditions of formation of circular economy are described. Thus, the technological approach to the formation of a competitive paradigm is based on production technologies, opportunities for technological modernization and development of infrastructure and intellectual competencies. The institutional concept reveals the regulatory mechanisms for ensuring competitiveness through the protectionism of national industrial enterprises, standardization and regulation of market imbalances. The innovation-investment approach within the competitive paradigm is also manifested in the creation of competitive advantages due to the presence of active innovative developments and their commercialization, knowledge and competencies of staff, capitalization of intelligence and communications, constant updating of infrastructure and technologies. Collectively, innovation and investment effects on the level of economic security allow industrial enterprises to ensure resilience to increasing competition, the emergence of new market challenges in the formation of a circular economy. A strategic approach to the application of a competitive paradigm to ensure the economic security of industrial enterprises allows you to justify the prospects for development and design behavioral models to predict and assess potential threats. The concept of system management is based on the complexity of threat analysis, the integrity of the economic security system, system-forming functions and patterns of implementation of industrial development tasks in the formation of a circular economy. The application of the described concepts is formalized by the authors through the definition of the basic patterns, directions and characteristics of their impact on the elements of the security system of industrial enterprises in the formation of a circular economy.

• Asia-Pacific Journal of Business
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• v.12 no.3
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• pp.251-269
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• 2021

Purpose & methodology - This study is to answer the following three questions, analyzing data on Top 50 Indian Billionaires, Top 50 Indian companies and Top 50 Indian start-ups. The first is whether or not traiditonal merchant caste like Vaisya's ability of creating wealth declined as some studies argue. The second is whether non-merchant caste like Brahmin, Kshatriya, OBC, Dalit, emerged as an India's new business elite. The third is whether the results of top 50 Indian companies and top 50 start-ups' analyses have similarities with that of top 50 Indian Billionaires analysis. Findings - The result is firstly, the ability of creating wealth of businessmen from the merchant caste was still overwhelming. Secondly, the big businessmen' rising from non-merchant castes as a new business elite was extremely rare. Thirdly, according to the analysis of top 50 Indian start-ups, the percentage of Vaisya has significantly declined while the the percentage of upper castes like Brahmin has noticeably increased. Fourthly, no evidence was found that businessmen from the lower castes like OBC or Dalit were increasing the expanding business influence. Research implications - The Studies mean that in big-sized Indian companies, the power of traditional merchant caste remains the same as before and is even reinforcing, while in small start-ups, Vaisya's power is considerably decreasing, and the upper castes like Brahmin who are highly educated are noticeably increasing.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.18-30
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• 2023

We compared empirically the forecast accuracies of the LSTM model, and the ARIMA model. ARIMA model used auto.arima function. Data used in the model is 100 days. We compared with the forecast results for 50 days. We collected the stock closing prices of the top 4 companies by market capitalization in Korea such as "Samsung Electronics", and "LG Energy", "SK Hynix", "Samsung Bio". The collection period is from June 17, 2022, to January 20, 2023. The paired t-test is used to compare the accuracy of forecasts by the two methods because conditions are same. The null hypothesis that the accuracy of the two methods for the four stock closing prices were the same were rejected at the significance level of 5%. Graphs and boxplots confirmed the results of the hypothesis tests. The accuracies of ARIMA are higher than those of LSTM for four cases. For closing stock price of Samsung Electronics, the mean difference of error between ARIMA and LSTM is -370.11, which is 0.618% of the average of the closing stock price. For closing stock price of LG Energy, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. For closing stock price of SK Hynix, the mean difference is -830.7269 which is 1.00% of the average of the closing stock price. For closing stock price of Samsung Bio, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. The auto.arima function was used to find the ARIMA model, but other methods are worth considering in future studies. And more efforts are needed to find parameters that provide an optimal model in LSTM.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.51-59
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• 2024

This study examines the relationship between the release of the news and the individual stock returns. Investors utilize a variety of information sources to maximize stock returns when establishing investment strategies. News companies publish their articles based on stock recommendation reports of analysts, enhancing the reliability of the information. Defining release of a stock-recommendation news article as an event, we examine its economic impacts and propose a binary classification model that predicts the stock return 10 days after the event. XGBoost and LightGBM models are applied for the study with accuracy of 75%, 71% respectively. In addition, after categorizing the recommended stocks based on the listed market(KOSPI/KOSDAQ) and market capitalization(Big/Small), this study verifies difference in the accuracy of models across four sub-datasets. Finally, by conducting SHAP(Shapley Additive exPlanations) analysis, we identify the key variables in each model, reinforcing the interpretability of models.

• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.207-222
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• 2024

Purpose - This study investigates whether the lagged price difference ratio between preferred and common stocks is related to the return and closing price of the preferred stock using three panel models. Design/methodology/approach - As a first step, we use a two-way fixed effect panel model with stationary preferred stock returns as a dependent variable. For robustness, we then apply the autoregressive distributed lag model (ARDL) and error correction model (ECM) with nonstationary closing prices of the preferred stocks as a dependent variable and compare the results of each model. The ARDL and ECM models provide an advantage of estimating a long-run equilibrium equation together if a long-run relationship exists between the two time-series variables compared to the fixed effect model. Findings - Our sample consists of 107 preferred stocks with at least four years of daily observations as of the end of December 2023. The coefficients of the error correction terms in the ARDL and ECM models are highly statistically significant, approximately -0.08. This indicates that the disequilibrium between the closing prices of common and preferred stocks adjusts by about 8% per day toward equilibrium. In all three models, the price difference ratio on day t-1 was statistically significant in explaining the preferred stock returns or closing prices on day t, implying that trading based on the previous day's price difference ratio is effective for one day. Research implications or Originality - Furthermore, the returns on preferred stocks are higher for firms with a lower proportion of foreign investors or a lower foreign market capitalization of preferred stocks. This suggests that foreign investors with informational advantages do not actively engage in profit-taking by trading preferred stocks, thus not narrowing the price difference. In summary, the recent surge in preferred stock prices is likely driven mainly by the irrational behavior of retail investors.

• The Journal of Fisheries Business Administration
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• v.37 no.1 s.70
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• pp.25-44
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• 2006

Under the provision of Article 63 of the Enforcement Regulation of the Act on Acquisition and Compensation of Land and Others for Public Project that is recently enacted and implemented (hereinafter referred to as the 'Lend Compensation Act') the compensation is required to make 'When the Actual Damage Amount' is confirmed for the damage in fishery affairs that is outside of the public project area. The compensation for fishery business on the indirect damage area has been excluded from the advance compensation subject to conflict with the existing laws on fishery business compensation with the controversy in method, procedure, time and others to confirm the actual damage amount, and it lacks the standard of calculation for detailed compensation on partial damages outside of business implementation area, which caused the ceaseless conflicts and straggles between the project implementation party and the victimized fishermen regarding the calculation method of damages, standard, compensation period and others. In particular, from the numerous problems in damage compensation in fishery on the indirect damage area, the most recent problem emerged is the issue on application method of damage period in calculating the damage compensation amount that the struggle has been deepened with the differences between the project implementation party and the victimized fishermen without the stipulation on the compensation, that caused the difficulties in carrying out the public project and other serious social problems. In this study, the reasonable application method for the damage period and the calculation plan of the damage amount for calculating the damages on fishery industry outside of the public project implementation zone that is not fully specified under the Land Compensation Act, and the indirect damage area is not influenced for the notification of project recognition, and the compensation to undertake with the damage in the fishery industry in project implementation area to have the nature of damage compensation, the right to engage in fishery industry has the perpetual nature of rights, the fishery damage compensation system of Japan also recognizes the perpetual right on fishery industry to calculate the compensation amount, and the compensation for damage amount has been exercised for the period of actual damage occurrence period regardless of remaining effective period for most of fishery permit and license for fishery compensation outside of the project implementation area following the recent various public projects as well as the development process of theory on fishery loss compensation that the calculation of damage amount on the fishery industry outside of the project implementation zone would be prudent to compensate by calculating the applicable damages during the period of actual damages, and by doing so, the 'just compensation' guaranteed under the Constitution may be materialized. Therefore, the calculation of the damages from the implementation of the public project shall consider the actual period of damages and the degree of damage from the public project to calculate by the income capitalization method, however, considering the equitable consideration with the compensation following the cancellation, it shall not exceed the compensation following the termination of the applicable fishery businesses. Furthermore, the calculation method of partial damage amount on the fishery business following the project implementation shall apply, depending on the period of damage occurrence, by (1) the case of calculating the future damage amount at the present time, and (2) calculating the damage from the past to the present time as well as the damage to be incurred later, by selecting the calculation method for damages following the damage occurrence type.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.75-106
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• 2007

In this study, it is analyzed whether oil price plays a major role in the pricing return on Koran stock market and examined why the covariance risk between oil and return on stock is different in each industry. Firstly, this study explores whether the expected rate of return on stock is pricing due to global oil price factors as a function of risk premium by using a two-factor APT. Also, it is examined whether spill-over effects of oil price volatility affect the beta risk to oil price. Considering the asymmetry of oil price volatility, we use the GJR model. As a result, it shows that oil price is an independent pricing factor and oil price volatility transmits to stock return in only electricity and electrical equipment. Secondly, the two step-analyzing process is introduced to find why the covariance between oil price factor and stock return is different in each industry. The first step is to study whether beta risk exists in each industry by using two proxy variables like size and liquidity as control variables. The second step is to grasp the systematic relationship between the difference of liquidity and size and beta to oil price factor by using the panel-data model which can be analyzed efficiently using the cross-sectional data formed with time series. Through the analysis, we can argue that oil price factor is an independent pricing factor in only electricity and electrical equipment having the greatest market capitalization, and know that beta risk to oil price factor is a proxy of size in the other industries. According to the result of panel-data model, it is argued that the beta to oil price factor augments when market capitalization increases and this fact supports the first assertion. In conclusion, the expected rate of return of electricity and electrical equipment works as a function of risk premium to market portfolio and oil price, and the reason to make beta risk power differentiated in each industry attributes to the size.

• 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.