• Environmental and Resource Economics Review
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• v.29 no.3
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• pp.363-388
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• 2020

The purposes of this study are to not only estimate optimal harvests and efforts using the surplus production methods for Spanish mackerel caught by multiple fishing gears, but provide dynamic optimal fisheries management for these gears using the current value Hamiltonian method. To achieve the above purposes this study uses several models such as Gavaris's general linear model for standardizing fishing efforts, surplus production method for estimating biological and technological coefficients, current value Hamiltonian method for estimating dynamic optimal harvest and efforts, and sensitivity analysis for diagnosing economic influences of these fisheries. As a result, this study showed that Spanish mackerel was overfished by multiple fishing gears based on surplus production method and the current value Hamiltonian method. Also, this study found that when the price and cost proportionally changed, the optimal harvest and fishing effort sensitively responded to the stock level of Spanish mackerel. Next, this study suggested that the multiple fishing gears for Spanish mackerel should reduce unnecessary costs such as operating time or inefficient fuel consumption. Finally, this study provided reasons Spanish mackerel should be included in the TAC system in a view of profit maximization based on sustainable use of the Spanish mackerel.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.149-161
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• 2017

This paper analyzed the impact of alliances on the market value of the 106 bio-pharmaceutical companies listed on the KOSPI or KOSDAQ in Korea by using the 'Event study methodology'. Although general alliances did not impact the corporate value significantly, in the analysis corresponding to the alliance type, R&D alliances created positive value, as technology acts as an important factor for the alliance. Among the R&D alliances, 'Technology Transfer alliances', in particular 'Development Technology Transfer alliances', had a positive influence on the corporate value. We interpret these differentiated results as market tends to screen for types of alliances. Meanwhile, we confirmed that the possibility of a stock price increase before the alliance announcement is high by analyzing the impact of the timing of corporate alliance announcements on the company value. It can be inferred that the possibility of information leakage is high. This paper analyzes the impact of alliances for managers and practitioners seeking to create value for domestic bio-pharmaceutical companies, and suggests the need to prevent information leakages by establishing a suitable policy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.231-237
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• 2017

This study examines the relationship between voluntary information disclosure and the effective tax rate using Investor Relation (IR) as the proxy for the level of the firm's voluntary information disclosure, and effective corporate tax rate as the proxy for the level of tax avoidance. This study considers sample data from 1,396 firms listed on the Korea Composite Stock Price Index (KOSPI) from 2011-2014. The results of this study are as follows: Investor Relation (IR) had a positive correlation with effective corporate tax rate. This result got on with the result of additional analysis using extra measurement of effective corporate tax rate. According to these results, we expect that firms featuring greater voluntary information disclosure report enhanced business performance. This study contributes understanding how Investor Relation (IR) affects tax avoidance. We hope that this study can promote the development of capital markets and provide good news to investors for firms that have greater information disclosure.

• Korea Trade Review
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• v.41 no.4
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• pp.61-90
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• 2016

This study aims to identify the sources of productivity change in export manufacturing firms. After estimating the Malmquist productivity index, a panel regression was used to calculate the source of productivity change. Upon conducting a literature review of this field, six variables were selected as explanatory variables. The results of an analysis of 355 export manufacturing firms operating from 2009 through 2015 are as follows: First, both innovation activity and total assets had a positive impact on productivity change. However, employment cost intensity, equity ratio, and current ratio had a negative impact on productivity change in export manufacturing firms. Second, innovation activity and intangible assets had a positive impact on productivity change, but employment cost intensity, selling expense intensity, and equity ratio had a negative impact on productivity change in large export manufacturing firms. Third, innovation activity had a positive impact on productivity change, but employment cost intensity and equity ratio had a negative impact on productivity change in small and medium export manufacturing firms. Fourth, intangible assets had a positive impact on productivity change, but employment cost intensity, selling expense intensity, and current ratio had a negative impact on productivity change in export manufacturing firms listed on the Korea Composite Stock Price Index. Fifth, innovation activity and total assets had a positive impact on productivity change, but employment cost intensity and equity ratio had a negative impact on productivity change in manufacturing firms listed on the Korean Securities Dealers Automated Quotations. The managerial implications of this study are also discussed.

• Management & Information Systems Review
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• v.35 no.1
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• pp.189-206
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• 2016

It was required to attach the documents on the details of external audit including the number of the participants in external audit, audited parts and audit times under the Article 7-2 on the audit report to the accounting audit report from 2014 in accordance with the amendment to the Act on External Audit of Stock Companies. This study aim to calculate the audit fee based on the estimated cost of service calculation of the government contribution agencies by reflecting the implementation of the revised external audit. This study calculated the audit fee for the target company (a listed company assumed to have no internal control risks and relevant audit risks for unqualified opinion in the previous year, 100 billion won of total amount of asset, manufacturing company in the previous year and preliminary client request) by putting together four items of expenditure including employment costs, expenditure, general management expenses and profit in accordance with the calculation system of cost of service under the State Contract Act. Then, it used the data collected from the documents on the details of the revised external audit after requesting estimation on the target company with the estimated cost to Big-4 accounting firms to identify the participants and times of the accounting audit. The employment costs applied 150% of participation rate of the base price of employment costs for the academic research service cost in 2014, the expenditure used the average value of accounting firms of corporate business management analysis of the Bank of Korea (2013), the general management expenses applied 5% of the general management rate of service business under Article 7-1 of the Enforcement Rule of the Act on Contracts to which the State is a Party and the profit applied 10% of profit rate of service business under Article 7-2 of the Enforcement Rule of the Act on Contracts to which the State is a Party. Based on the calculation of the estimated costs by applying the above, the audit fee was estimated at 50,617,769won. Although the result is not the optimal audit fee, it may be used as a basic scale to compare the audit fees of companies without criteria. Also, such amendment to the Act on External Audit of Stock Companies may improve independence of auditors and transparency of the accounting system rather than previous announcing only the total audit times.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.1-13
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• 2019

As a way to find new and innovative technologies, many companies have invested in and acquired skilled startups. Because startups are usually small in size and have a small history of past business experience, there are many risks involved in acquiring them as they have limited technical skills and business feasibility verification methods. Thus, venture capital plays an important role in discovering and investing competitive startups. While Independent Venture Capital generally values financial returns, Corporate Venture Capital, which plays investment roles in the firm, values business synergies with the parent company from a strategic perspective. In an industry sector where development of technology is rapid and whether new technology is held determines a company's competitiveness, existing companies incorporate startups with innovative technologies into their investment portfolios, collaborate together, and take over for comprehensive cooperation. In addition, new investments and acquisitions are carried out through the management of portfolio companies to obtain and utilize industry information. In this paper, major U.S. companies listed in the U.S. verified their investment activities through corporate venture capital and their impact on parent companies and startups through regression, while the parent company's acquisition performance was analyzed through an event study based on a stock price analysis. The criteria for startup were defined as companies with less than 12 years of experience, and the analysis showed that the parent companies with corporate venture capital with a larger number of investments actively take over startups. In addition, increasing corporate venture capital's financial investment activities shows a negative impact on the parent companies' acquisition activities, and the acquisition performance increased when the parent companies took over startups in its portfolio.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.