• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.314-323
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• 2022

This study examines the effecs of National Pension Service blockholders on accounting conservatism. The sample consists of 10,117 non-banking firm-years listed in Korea Stock Exchange(KOSPI) during the period 2011 to 2018. The results of this study are as follows. First, it was found that companies in which the National Pension Service as a major shareholder hold more than 5% of the shares are less prone to conservative accounting treatment than those that do not. Second, such a negative relationship between investment by the National Pension Service and conservative accounting was consistently found even when the investment period of the National Pension Service was divided into short-term (less than 1 year) and long-term (more than 3 years). It is expected that the National Pension Service, the largest institutional investor in Korea, will be able to carry out meaningful management control activities on investment companies. As the monitoring function of the National Pension Service works effectively in the capital market, agency costs are reduced, and investors' demands for corporate conservative accounting have decreased.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.225-241
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• 2020

Purpose - The purpose of this study was to examine the effect of the ratio of outside directors, especially the ratio of outside directors according to their tenure, on firm value. Design/methodology/approach - This study collected total 3,861 firm-year data about companies listed KRX KOSPI market in Korea. The Pooled Ordinary Least Square Model and Panel Fixed Effects Model were hired in order to analyze the data. Findings - First, it was found that the ratio of outside directors for total sample had no significant effect on firm value, and the estimation coefficient of dummy variable for the average tenure less than 3 years had a significant positive(+) effect on firm value. Second, the ratio of outside directors corresponding to the tenure of less than 3 years had a significant positive(+) effect on the firm value. On the contrary, the ratio of outside directors corresponding to the tenure of 3 years or more had a significant negative(-) effect on firm value. Third, the ratio of outside directors corresponding to the tenure for more than 6 years did not show any significant influence on firm value. Research implications or Originality - First, if other matters are not additionally considered, keeping the tenure of outside directors shortly on average could help to increase firm value. Second, in the case of firms facing the decision to reappoint outside directors for the first time, it is highly likely that the firm value would decrease on average, so careful decisionmaking considering various aspects is required. However, this study does not take into account the legal standards for the appointment of outside directors, diversity of outside directors, and the actual independence of outside directors according to other criteria in the analysis. Therefore, if these factors are considered, there is a possibility that the empirical analysis results of this study may show different patterns.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Information Systems Review
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• v.26 no.1
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• pp.269-288
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• 2024

The main objective of this study is to empirically explore how the organizational culture influences financial performance of companies. To achieve this, 58 companies included in the KOSPI 200 were selected from an online job platform in South Korea, JobPlanet. In order to understand the organizational culture of these companies, data was collected and analyzed from 81,067 reviews written by current and former members of these companies on JobPlanet over a period of 9 years from 2014 to 2022. To define the organizational culture of each company based on the review data, this study utilized well-known text analysis techniques, namely Word2Vec and FastText analysis methods. By modifying, supplementing, and extending the keywords associated with the five organizational culture values (Innovation, Integrity, Quality, Respect, and Teamwork) defined by Guiso et al. (2015), this study created a new Culture Dictionary. By using this dictionary, this study explored which cultural values-related keywords appear most often in the review data of each company, revealing the relative strength of specific cultural values within companies. Going a step further, the study also investigated which cultural values statistically impact financial performance. The results indicated that the organizational culture focusing on innovation and creativity (Innovation) and on customers and the market (Quality) positively influenced Tobin's Q, an indicator of a company's future value and growth. For the indicator of profitability, ROA, only the organizational culture emphasizing customers and the market (Quality) showed statistically significant impact. This study distinguishes itself from traditional surveys and case analysis-based research on organizational culture by analyzing large-scale text data to explore organizational culture.

• Journal of Distribution Science
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• v.15 no.8
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• pp.47-54
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• 2017

Purpose - According to agency theory, managers have incentives to adjust firm revenues to meet earnings expectations or delay bad news disclosure because of performance-based compensation and their reputation in the market. When the bad news accumulates, stock prices fail to reflect all available information. Thus, market prices of stocks are higher than their intrinsic value. After all, bad news crosses the tipping point, it comes out all at once. That results in stock crashes. Auditors can decrease stock crash risk by reducing agency costs through their informational role. Especially, stock price crash risk is expected to be lower for firms adopting high-quality audits. We focus on distribution and service industry to examine the relation between audit quality and stock price crash risk. Industry specialization and auditor size are used as proxies for auditor quality. Research design, data and methodology - Our sample contains distribution and service industry firms listed in KOSPI and KOSDAQ during a period of 2004-2011. We use a logistic regression to test whether auditor quality influences crash risk. Auditor quality was measured by industry specialist auditor and Big4 / non-Big4 dichotomy. Following the approach in prior researches, we use firm-specific weekly returns to measure crash risk. Firms experiencing at least one stock price crash in a specific week during year are classified as the high risk group. Results - The result of analyzing 429 companies in distribution and service industry is summarized as follows: Above all, it is shown that higher audit quality has a significant negative(-) effect on the crash risk. Crash risk is alleviated for firms audited by industry specialist auditors and Big 4 audit firms. Therefore, our results show that hypotheses are supported. Conclusions - This study is very meaningful as the first study which investigated the effects of high audit quality on stock price crash risk. We provide evidence that high-quality auditors reduce stock price crash risk. Our finding implies that the risk of extreme losses can be reduced through screening of high-quality auditors. Therefore investors and regulators may utilize our findings in their investment and rule making decisions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.560-565
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• 2016

In this study, we empirically scrutinize the relationship between short selling transactions and stock price behaviors using the stock market data in Korea during the period from January 2005 to March 2016. We chose the short selling volume ratio (SVR), stock lending volume ratio (LVR), and stock lending open interest ratio (LIR) as variables of the short selling trading activities. We construct portfolios based on the percentile of the short selling volume ratio during the sample period; upper-10%-SVR portfolio, upper-25%-SVR portfolio, upper-50%-SVR portfolio. We estimate the monthly firm-specific return and monthly skewness of the daily firm-specific returns of each portfolio. The firm-specific return or skewness is specified as a dependent variable and the short selling activities as explanatory variables. The results show that all of the statistically significant estimates of the short selling activities for the firm-specific returns are negative and that all of the statistically significant estimates of the skewness of the short selling activities are positive. These results support the hypothesis that short selling activities cause the stock price to decrease.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.585-600
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• 2012

The purpose of this study related to the liquidity impact of the housing market variables using vector auto-regressive model(VAR) and empirical analysis is to derive some policy implications. October 2003 until May 2012 using monthly data for liquidity variables mortgage rates, mortgage, financial liquidity, as the composite index and nation, Seoul, Gangnam, Gangbuk, the Apartment sales prices were analyzed. Granger Causality Test Results, mortgage rates and mortgage at a bargain price two regions had a strong causal relationship. Since the impulse response analysis, Geothermal difference there, but housing price housing price itself, the most significant ongoing positive (+) reactions were liquidity-related variables are mortgage loans is large and persistent positive (+), financial liquidity weakly positive (+), mortgage interest rates are negative (-), KOSPI, the negative (-) reacted. Liquidity and housing prices that the rise can be and Gangnam in Gangbuk is greater than the factor that housing investment was confirmed empirically. Government to consider the current economic situation, while maintaining low interest rates and liquidity of the market rather than the real estate industry must ensure that activities can be embedded and local enforcement policies should be differentiated according to the policy will be able to reap significant effect.