• Journal of Digital Convergence
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• v.19 no.1
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• pp.177-186
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• 2021

This study examined the effects of the firm characteristics of the manufacturing industry on the female employment ratio. Three hypotheses regarding female employment ratio, were tested for 5,729 firms that could use financial data among the firms listed on the KOSPI from 2011 to 2019, in terms firm size, listing period, and foreign ownership. Follwing the analyses, three hypotheses were mostly adopted. It was found that as the size firm and foreign ownership ratio increased, the female employment ratio increased in the number of regular and permanent contract employees, fixed-term employees, and total employees. As for the listing period, the higher the value, the lower the female employment ratio in the number of regular and permanent contract employees, fixed-term employees, and total employees. These research results may be used as basic data for gender equal employment policies such as Affirmative Atcion for Gender Equal Employment.

• Knowledge Management Research
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• v.15 no.4
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• pp.1-14
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• 2014

This study investigates earnings management of IPO firms in knowledge-based-industry. we analyse the relation between earnings management and R&D expenses(Research and development expense)which is an important expenditure in knowledge based management. First, we found that the earnings management is the largest in the year when the firm is enrolled on the market. Second, the IPO firms have higher DA(discretionary accruals) than existing firms on the market and the size of R&D expenses is larger, too. Finally, in the IPO firms in knowledge-based-industry, the higher accounting receivable and R&D expenses are, the more happens earnings management. Our study shows that the IPO firms of knowledge-basedindustry have high R&D expenses which are core expenditure. Also, earnings management has happened frequently in the IPO firms.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.213-222
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• 2018

This study attempts to empirically investigate the excess returns of growth stocks in the Korean stock market comparing with those of value stocks. Recently, a few of IT and bio-pharmaceutical stocks with high growth potentials have accomplished dramatically high returns in the Korean stock market. Whereas, important prior studies in this line have observed negative excess returns from investment of growth stocks on average. And a few studies have reported that the distribution of excess returns from growth stocks is not normal but positively skewed. Empirical results of the present study are consistent with those of prior studies. Interestingly, the present study observed serial inverse correlation between excess returns of growth stocks and value stocks. Also, regardless of growth or value stocks, the stocks with higher PEG(=PER/ROE) showed higher excess returns.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.420-430
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• 2013

In this study, we investigate the current status of family friendly management and determinants of its level. We choose measures about family friendly management and use their results from ESG evaluation model by Korea Corporate Governance Service. We find that firms with larger size, lower leverage and higher firm value are more family friendly. And in contrast to previous studies, firms are more family friendly when they have less women to total employees. But firms with higher largest shareholder holdings are less family friendly. These results show that listed firms in Korea should be more family friendly considering their level of woman employment. Also the interest and support from top management are needed for activating family friendly management, but largest shareholder of korean firms are not active yet.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.259-266
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• 2020

Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.