• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.239-251
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• 2011

The need for statistical analysis to discern the existence and the type of international business comovement has increased as business and economic variations in one country is directly transmitted to business and financial market conditions in another without a long lag. This study performs the statistical tests for th locomotive hypothesis to understand the structural character of the long-run mechanism among Korea-US current and future business movements and the domestic stock market. The U.S. future business prospect, rather than the US current and the domestic current and future business conditions, appears to signi cantl a ect the domestic stock market movement.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.757-773
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• 2012

Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.525-532
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• 2021

This study examines the association between a firm's business strategy and audit report lags. This study employs 5,072 firm-year observations from 2015 to 2019. Our sample comprises all of the firms listed on the Korea Composite Stock Price Index (KOSPI) market and Korea Securities Dealers Automated Quotation (KOSDAQ). We perform OLS regression analysis to test our hypothesis. The OLS regression analysis was conducted through the SAS and STATA programs. We find that business strategy is positively associated with audit report lags. Especially, we find that defender firms are negatively associated with audit report lags. The findings of this study suggest that prospector-like firms would increase their performance uncertainty as well as audit risk. Therefore, prospector-like firms interfere with the efficient audit procedures of auditors. On the other hand, our findings indicate that defender-like firms would decrease their performance uncertainty as well as an audit risk because they focus on simple product lines and cost-efficiency. For this reason, auditors will be able to carry out the audit procedures much more easily. Our results present that a prospector-like business strategy degrades audit effectiveness as it exacerbates a company's financial risk, willingness to accept uncertainty, and the complexity of organizational structure.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.271-282
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• 2022

The paper analyzes the effects of the emergence of Fintech on the banking and savings banking industries in Korea. From the analysis, we find that net interest margin decreases and credit supply increases with the advent of Fintech in the banking industry. Similarly, in the savings banking industry, a profitability index decreases. This is interpreted as the result of reduced monopoly power and increased efficiency in the industries, inducing an increase in overall consumer benefits. Individual financial institutions may, however, experience difficulties such as reduced profitability and increased Fintech investment costs.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.221-240
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• 2023

Purpose - This paper investigates how business cycle impacts on corporate credit spreads since global financial crisis. Furthermore, it tests how the impact changes by the phase of the cycle. Design/methodology/approach - This study collected dataset from Barclays Global Aggregate Bond Index through the Bloomberg. It conducted multi-regression analysis by projecting business cycle using Hodrick-Prescott filtering and various cyclical variables, while ran dynamic analysis of 5-variable Vector Error Correction Model to confirm the robustness of the test. Findings - First, it proves to be statistically significant that corporate credit spreads have moved countercyclicaly since the crisis. Second, It indicates that the corporate credit spread's countercyclicality to the macroeconomic changes works symmetrically by the phase of the cycle. Third, the VECM supports that business cycle's impact on the spreads maintains more sustainably than other explanatory variable does in the model. Research implications or Originality - It becomes more appealing to accurately measure the real economic impact on corporate credit spreads as the interaction between credit and business cycle deepens. The economic impact on the spreads works symmetrically by boom and bust, which implies that the market stress could impact as another negative driver during the bust. Finally, the business cycle's sustainable impact on the spreads supports the fact that the economic recovery is the key driver for the resilience of credit cycle.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• 한국노년학
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• v.38 no.2
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• pp.271-290
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• 2018

The study aims to analyze the effects of socio-economic deprivation on suicidal ideation. The analysis data were used as a guide for Korea Welfare Panel Study 9. the frequency analysis, mean difference analysis, correlation analysis, and logistic regression were performed by SPSS programs. The results of analysis are as follows. First, The results of frequency analysis by deprivation type showed a high frequency of deprivation in the following order. Experience of not receiving a public pension, experience of being able to work but unemployed, experience of not being able to eat a balanced diet due to financial difficulties, and experience where you had nothing to eat but no more money to buy. Second, the average difference analysis shows that when a person does not have a spouse, the lower the academic background and the income level, the higher the likelihood of suicide. Third, regression analysis shows that the following deprivation patterns have a statistically significant effect on older adults' thoughts of suicide. Experience in which the respondents or their family could not go to hospital because they had no money, experience that move house because is back rent more than 2 months or can not pay rent, experience that they could not afford to buy food and eat well-balanced meals, experience of failing to pay your bills on time, experience of being able to work but not having a job, and experience in which financial difficulties left them short of food and no money to live. Based on such research results, some policy measures, such as the expanding management of medical care benefits cases, the improvement of elderly housing, residential conditions and the diet survey for the elderly, and the expansion of measures to support elderly people's tax rates, were proposed.

• The Journal of Industrial Distribution & Business
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• v.11 no.8
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• pp.31-38
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• 2020

Purpose: This study uses 'Autoregressive Integrated Moving Average Model' to predict the impact of a sharp drop in the base rate due to COVID-19 at the present time when government policies for stabilizing house prices are in progress. The purpose of this study is to predict implications for the direction of the government's house policy by predicting changes in house transaction prices and house rental prices after a sharp cut in the base rate. Research design, data, and methodology: The ARIMA intervention model can build a model without additional information with just one time series. Therefore, it is a time-series analysis method frequently used for short-term prediction. After the subprime mortgage, which had shocked since the global financial crisis in April 2007, the bank's interest rate in 2020 is set at a time point close to zero at 0.75%. After that, the model was estimated using the interest rate fluctuations for the Bank of Korea base interest rate, the house transaction price index, and the house rental price index as event variables. Results: In predicting the change in house transaction price due to interest rate intervention, the house transaction price index due to the fall in interest rates was predicted to change after 3 months. As a result, it was 102.47 in April 2020, 102.87 in May 2020, and 103.21 in June 2020. It was expected to rise in the short term. In forecasting the change in house rental price due to interest rate intervention, the house rental price index due to the drop in interest rate was predicted to change after 3 months. As a result, it was 97.76 in April 2020, 97.85 in May 2020, and 97.97 in June 2020. It was expected to rise in the short term. Conclusions: If low interest rates continue to stimulate the contracted economy caused by COVID-19, it seems that there is ample room for house transaction and rental prices to rise amid low growth. Therefore, In order to stabilize the house price due to the low interest rate situation, it is considered that additional measures are needed to suppress speculative demand.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.119-130
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• 2011

This paper measures and analyzes the performance of insurance companies in Korea in respect to sustainable development and suggest strategic implications based on the analysis. The correlation, regression, ANOVA, and t-test are employed. The results of this study are summarized as follows. First, it shows tat social index is important in the life insurance industry; however, the environmental index, is important in the non-life insurance industry. Second, the result gained by regressing the size and financial soundness on the performance of sustainable development demonstrates that the size variable is statistically significant. It suggests that size is a necessary condition for sustainable development. Finally, ANOVA shows that the small and medium sized companies have a significantly poor performance compared to the large companies concerning the social index and reputation index in the life insurance industry. The small and medium sized companies in the non-life insurance industry exhibit a significantly poor performance compared to the large companies in respect to all the indexes, except for the social index. Therefore, the small and medium sized companies make every endeavor in the poor indexes to improve performance.