• Journal of Distribution Science
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• v.14 no.6
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• pp.107-118
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• 2016

Purpose - Over 90% of Domestic logistics industry is small enterprise and they are experiencing growth stagnation due to price-based competition structure rather than constructing logistics service of high added value. In order to get over this situation and pursue the development of logistics industry, strengthening its competitiveness, through inter-enterprise cooperative network build-up, would be a key alternative. Therefore, in this study, an index for measuring inter-enterprise cooperation level of Joint logistics business will be developed as a typical collaborative business model in logistics industry. Moreover, a strengthening competitiveness method suggests a developmental step and a key management index to mature in logistics industry. Research Design, Data, Methodology - This study is an index development research for measuring inter-enterprise cooperation level of logistics industry. Such a level was measured by performing a survey by targeting enterprises that participated in Joint logistics business. The targeting enterprises are typical cooperative models in logistics industry. Measurement items were developed which were based on the presented items in existing research. Question items were composed of selection type questions as answering Yes/No. They measures implementation status of corporate activity and detailed activity items measuring qualitative level. Total samples were based on 116 enterprise samples including 90 logistics enterprises and 26 shippers. In addition, by evaluating the importance for Joint logistics business recognition with personnel working level, the weight of measuring variable was extracted. This study has built an assessment tools (LPCI) on Joint logistics business cooperation level in a situation where there are no previous studies on joint logistics business, this study is meaningful for other studies. Results - As a result of analyzing LPCI presented in this study, the score of logistics enterprise was represented as 59.9 points based on full score of 100 points and that of shippers as 47.2 points and cooperation level among enterprises participated in Joint logistics business was revealed to be very low. In particular, as a result of measuring the importance between logistics enterprise and shippers, the difference by each measurement standard was represented among those enterprises. This difference is considered to be a key factor that cooperative operational conformity between logistics enterprises and shippers is represented to be low. Conclusions - As most joint logistics business, being promoted at present, is sharing facility and information with joint logistics business, it is hard to find such a joint logistics business in reality based on cooperative business model in main cooperation agents. Therefore, competitiveness of logistics industry could be strengthened by promoting joint logistics business based on their mutual cooperation among enterprises. In other words, it is to secure sustainable competitiveness of joint logistics business together with creation of new market by inter-enterprise cooperation based on integration of basic logistics business.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Information Systems Review
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• v.13 no.3
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• pp.123-139
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• 2011

This paper researches the value of domestic telecommunication bundle products. Customers evaluate each telecommunication products differently according to their attributes. The attributes affecting the customer value of telecommunication bundling can be categorized in 3 ones as follows; corporate image, service feature, and service price. Also authors analyze the difference of importancy that customers consider when they evaluate each products, and propose the optimal scenario for bundling. In conclusion, other two companies A, C excluding B should invest more resources into the portion that strengthen the attributes of company image, and service feature to upgrade their 'corporate image', and 'service feature'. According to 6-scenarios analysis on the bundle products, the QPS expansion of company A was the most advantageous position, but if companies B, C expand DPS made use of their strengths, they can prevent from decreasing additivity rapidly with sequential scenario. The above results show that one company may have equable power in each area, but if another company having strengths in special areas makes up for its weakness and differentiates gradually it can contribute to strengthen its competitiveness. This contributed much more theoretical and practical than the existing researches. Supposing that additivity index evaluated by consumers can be changed by efforts of companies, this scenario planning is the result of study showing that the investment and publicity of each company have to be considered as its characteristic of each product at the same time.

• Information Systems Review
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• v.26 no.1
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• pp.289-313
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• 2024

Korean pharmaceutical and bio-health companies began applying for FDA approval in 2000. However, drug companies in South Korea are not required to obtain FDA approval to market their products on the South Korean market, and the approval process is highly resource-intensive. This study utilizes event study methodology to examine the information effect of US FDA approval announcements on the stock prices of pharmaceutical and bio-health companies listed on South Korean stock markets. The study's results show that FDA approval announcements caused abnormal increases in corporate stock prices, indicating that these announcements have a transnational information effect on South Korean companies' value. Furthermore, the results show that the impact of FDA approval announcements on stock prices is greater for small companies than mid-sized and large companies and in bio and healthcare industries than in the traditional pharmaceutical industry. This impact is also more significant on the KOSDAQ (Korea Securities Dealers Automated Quotation) companies than the KOSPI (Korean Composite Stock Price Index) companies and after the expansion of stock price limits. These findings signal that the information effect is more significant when regulatory controls are weaker. The results also indicate that obtaining FDA approval brings above-normal returns for companies and that FDA application is a high-risk, high-return investment.

• Journal of Distribution Research
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• v.16 no.5
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• pp.171-192
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• 2011

This paper focuses on the retail industry of industrial share of the GDP, productivity of distribution industry and input-out analysis between Korea and Japan, also results are summarized as follows. First, the share of GDP in agriculture, forestry and fisheries of the both countries is falling. That of manufacture increases in South Korea, while Japan is falling. While distribution industry shows vice versa. Employed population by industry is falling both countries also. The relative labor productivity shows that agriculture, forestry and fisheries, retail industry needs more labor, while manufacture has been met for both countries. Second, compare to Japan, the retail industry of Korea has been increased since 1990. Likewise, overall productivity of distribution industry in Korea has been increased while almost that of Japan has declined. Third, production inducement effects of Japan are greater than that of Korea. On the other hand, import inducement effects show vice versa. Fourth, as shown from the final demand of distribution industry and the rate of dependence on production inducement, we can see that the “increase in stocks” increases while gross government fixed capital formation shows vice versa. Korea's private consumption expenditure increases while Japan shows versa. South Korea's government consumption expenditure and exports are rising, on the other hand, that of Japan is declining. Fifth, the rate of dependence on distribution industry and import inducement shows the same tendency from both countries. As we can see from the private consumption expenditure, government consumption expenditure, gross government fixed capital formation, gross private fixed capital formation, increase in stocks, the rate of dependence on import inducement is more effective than the rate of dependence on production inducement. While the exports are comparatively ineffective. Sixth, the degrees of influence of retail industry are similar between Korea and Japan, while sensitivity of the Korean industry has been weakened. In this sense, strong policies are needed to boost the industry. Seventh, the investments in the retail industry of Korea showed the public-led trend, while Japan showed private sector-led investment trend. The investment trend of Korea's retail industry will be switched into private sector-led investment step by step in the future. This finding will be an important clue to set the policy direction of Korea distribution industry. Finally, both Korea and Japan are still in need of employment in retail industry. Not addressed in this paper, such as value-added-induced effects, employment inducement effect, will be remaining challenges in the following paper.