• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.5 no.4
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• pp.1-34
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• 2010

Recently, failure rates of Kosdaq IPO firms are increasing and their survival rates tend to be very low, and when these firms do fail, often times backed by a number of governmental financial supports, they may inflict severe financial damage to investors, let alone economy as a whole. To ensure investors' confidence in Kosdaq and foster promising and healthy businesses, it is necessary to precisely assess their intrinsic values and survivability. This study investigates what contributed to the failure of IPO firms and analyzed how these elements are factored into corresponding firms' stock returns. Failure risks are assessed at the time of IPO. This paper considers factors reflecting IPO characteristics, a firm's underwriter prestige, auditor's quality, IPO offer price, firm's age, and IPO proceeds. The study further went on to examine how, if at all, these failure risks involved during IPO led to post-IPO stock prices. Sample firms used in this study include 98 Kosdaq firms that have failed and 569 healthy firms that are classified into the same business categories, and Logit models are used in estimate the probability of failure. Empirical results indicate that auditor's quality, IPO offer price, firm's age, and IPO proceeds shown significant relevance to failure risks at the time of IPO. Of other variables, firm's size and ROA, previously deemed significantly related to failure risks, in fact do not show significant relevance to those risks, whereas financial leverage does. This illustrates the efficacy of a model that appropriately reflects the attributes of IPO firms. Also, even though R&D expenditures were believed to be value relevant by previous studies, this study reveals that R&D is not a significant factor related to failure risks. In examing the relation between failure risks and stock prices, this study finds that failure risks are negatively related to 1 or 2 year size-adjusted abnormal returns after IPO. The results of this study may provide useful knowledge for government regulatory officials in contemplating pertinent policy and for credit analysts in their proper evaluation of a firm's credit standing.

• KIEAE Journal
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• v.17 no.3
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• pp.75-81
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• 2017

Purpose: The energy consumption of apartment units is affected by the lifestyle of the residents rather than system technology. In this study the numerical analysis of assumed energy consumption correlation factors with arbitrary value due to uncertainty. It is intended to be used as a simulation correction value which can be utilized as a predicted value of actual energy usage. The correction value of the simulation is set in the developed form of the existing process that derives the actual usage amount. The simulation results used in the existing evaluation system are used to maintain the useful value as the current system evaluation scale and predict the actual capacity. Method: The method of the study is to statistically analyze the data frames of all complexes capable of collecting the annual energy usage and to reconstruct the population by adding the variables that are expected to be correlated. Repeat the data frame configuration with variables that are assumed to be highly correlated with energy use levels. Determine whether there is correlation or not. The intensity of the external characteristics of the building equipment related to the energy consumption is presented as the quantitative value. Result: The correlation between electricity consumption and trading price since 2010 is analyzed as (Correlation coefficient 0.82). These results are higher than (Correlation coefficient 0.79), which is the correlation between residential area and trading price. This paper signifies the starting point of the methodology that broadens the field of view of verification of simulation feasibility limited to the prediction technique focused on the simulation tool and the element technology scope.The diversified phenomenon reproduction method develops the existing energy simulation method.It can be completed with a simulation methodology that can infer actual energy consumption.

• Journal of Distribution Science
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• v.14 no.6
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• pp.81-87
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• 2016

Purpose - In recent, a research finds that advanced selling can influence a consumer's choice(Kim et al., 2013). Advanced selling is defined as the new product launching strategy which company allows consumers to preorder new product before its release(Chu & Zhang, 2011). Prior researches have focused on the benefits of advanced selling(e.g., information gathering for demand prediction, an advantage for pricing strategy, and so on) for companies using this strategy(Chen, 2001; Chu & Zhang, 2011; Li & Zhang, 2013; Tang et al., 2004; Xie & Shugan, 2009). However, Kim et al.(2013) find it can also influence a consumer's choice. In detail, they suggest that when consumers use advanced selling, they are likely to prefer high-performance options rather than low-price options based on construal level theory(Trope & Liberman, 2003). In this paper, we tried to expand the prior researches for finding the mechanism of the influence of advanced selling on a consumer's choice. The purpose of this research is to test the mediating effect on the influence of advanced selling. Research design, data, and methodology - To find the mechanism of the influence of advanced selling, we designed an experiment for testing mediation effect. we recruited 93 students from a university. We assigned participants into one of two groups using randomization method. The participants with each group were given a scenario describing the sales strategy. Finally, they made a choice between high-performance option and low-price option. Sequentially, they also responded some questions for testing mediation effect. Results - First, we replicated prior research to test the influence of advanced selling. As a result, we could find that consumers prefer the high-performance option when they preorder it to purchase at the time of consumption. Thus, the replication result is the same as prior research. Second, we tested that advanced selling can influence the perception of temporal distance. The results confirmed that consumers perceived longer temporal distance in advanced selling condition(β = 1.575, SE = 0.272, p < 0.001). Third, we predicted that temporal distance can increase the importance of desirable attributes and decrease the importance of feasible attributes. The results suggested that temporal distance decreased significantly the importance of attributes related to feasibility(β = -0.19, SE = 0.07, p < 0.01), however, it had non-significant effect on increasing the importance of desirable attributes. Finally, we used Sobel-test for testing mediation effect, and it confirmed that the importance of feasible attributes had mediating role of the influence of advanced selling(Sobel test statistic = -2.110, SE = 0.111, p < 0.05). Conclusions - In this paper, we tried to find the mechanism of the influence on advanced selling from a consumer's choice. With an experiment, we confirmed that the importance of feasible attributes could mediate the effect on advanced selling. Therefore, we suggested some theoretical and practical contributions from this research. Finally, we discussed research limitations and suggested future research topics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5964-5970
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• 2013

The recent budget planning for highway infrastructure implied that the investments for Operation & Maintenance(O&M) became greater than that for new construction. This circumstance made many stakeholders pay attention to the O&M of road infrastructure and adopt other countries' policies and system for effective management. In other countries, most O&M for road infrastructure have been done by private entities using long-term contract and Korea is about to shift from one year contract to long-term contract. The most important parts for the expansion of the long-term O&M contract for road infrastructure are valuation of the O&M contract based on accurate prediction of O&M costs and instrument for proper risk sharing between contracting parties. Thus, this study provides a methodology to estimate a reasonable O&M contract price and a framework to share contract risk between contracting parties using real option. The analysis results showed that the contract price and ceiling and floor conditions for the 20 year-contract of 20 km-highway project were 45.7, 60 and 42.3 billion won, respectively.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.3
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• pp.45-52
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• 2018

Currently, apartments have become an important research subject for the overall area of politics, economics, and culture as well as urban architectural study. However, there are few analyses of the research trends related to the current interest in the apartment research and prediction of the future changes of an apartment in politics and industry. In this study, the research information related to the apartment has classified, and the changes in the research trends have analyzed. Based on the classified data, the first thesis and dissertation related to the apartment and changes of academic notation have discovered. In addition, future interests and future research directions through Frequency of Appearance, Degree Centrality Analysis, and Betweenness Centrality Analysis of author keywords were predicted. As a result of the analysis, 'Space,' 'Residential Mobility' and 'Apartment Complex' studies were found to be important research topics throughout the entire period. 'Han Gang Apartment,' 'Small Size Apartment,' 'Civic Apartments,' 'Jamsil,' and 'Child' were newly interested topics until 70's era. '(Super) High-rise Apartment,' 'Perception,' 'Jugong Apartment,' 'Housing Environment,' 'Housewife,' 'Apartment Layout,' and 'Busan' were newly interested topics during the 80's and 90's era. 'Apartment Price,' 'Energy,' 'Remodeling,' 'Noise,' 'Resident Satisfaction,' 'Community,' and 'Apartment Lotting-out' were newly interested topics after the year 2000. New concerns for last decade are found to be 'Super High-rise Apartment', 'Remodeling', 'Indoor'(2007), 'Apartment Reconstruction Project', 'Brand', 'AHP', 'Housing Environment'(2008), 'Ventilation'(2009), 'Apartment Lotting-out'(2010), 'Economic Assessment'(2011), 'Cost'(2012), 'Green Building', 'Apartment Sales', 'Law', 'Society'(2013), 'Floor Impact Noise', 'Seoul'(2014), 'Noise'(2015), 'Hedonic Model'(2016). In addition, following research topics are expected to be active in the future: In maturity stage of the research development is going to be 'Apartment Price', 'Space', 'Management of Apartment Housing'; the hedonic model, which is research growth and development stage, is going to be '(Floor Impact) Noise', 'Community', 'Energy.

• Knowledge Management Research
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• v.22 no.2
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• pp.247-268
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• 2021

Investors mainly use PER and PBR among financial ratios for valuation and investment decision-making. I conduct an analysis of two basic financial ratios from a statistical perspective. Financial ratios contain key accounting numbers which reflect firm fundamentals and are useful for valuation or risk analysis such as enterprise credit evaluation and default prediction. The distribution of financial data tends to be extremely heavy-tailed, and PER and PBR show exceedingly high level of kurtosis and their extreme cases often contain significant information on financial risk. In this respect, Extreme Value Theory is required to fit its right tail more precisely. I introduce not only GPD but exGPD. GPD is conventionally preferred model in Extreme Value Theory and exGPD is log-transformed distribution of GPD. exGPD has recently proposed as an alternative of GPD(Lee and Kim, 2019). First, I conduct a simulation for comparing performances of the two distributions using the goodness of fit measures and the estimation of 90-99% percentiles. I also conduct an empirical analysis of Information Technology firms in Korea. Finally, exGPD shows better performance especially for PBR, suggesting that exGPD could be an alternative for GPD for the analysis of financial ratios.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.81-93
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• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.572-578
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• 2020

The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.