• Journal of Distribution Science
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• v.19 no.1
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• pp.27-35
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• 2021

Purpose: This study is to investigate the effect of managerial ownership level in distribution and service companies on the stock price crash. The managerial ownership level affects the firm's information disclosure policy. If managers conceal or withholds business-related unfavorable factors over a long period, the firm's stock price is likely to plummet. In a similar vein, management's equity affects information opacity, and information asymmetry affects stock price collapse. Research design, data, and methodology: A regression analysis is conducted using the data on companies listed on the Korea Composite Stock Price Index (KOSPI) between 2012-2017 to examine the effect of the managerial ownership level on stock price crash risks. Results: Logistic and regression results indicate that the stock price crash risk was reduced as managerial ownership levels are increased. The managerial ownership level has a significant negative coefficient on stock price crash risk, negative conditional return skewness of firm-specific weekly return distribution, and asymmetric volatility between positive and negative price-to-earnings ratios. Conclusions: As the ownership and management align, the likeliness of withholding business-related information is reduced. This study's results imply that the stock price crash risk reduces as the managerial ownership level increases because shareholder and manager interests coincide, thereby reducing information asymmetry.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.257-267
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• 2019

The paper aims to test long-term and short-term causality from four exchange rates, the Korean won/$US, the Korean won/Euro, the Korean won/Japanese yen, and the Korean won/Chinese yuan, to the Korea Composite Stock Price Index in the presence of several macroeconomic variables using monthly data from January 1986 to June 2018. The results of Johansen cointegration tests show that there exists at least one cointegrating equation, which indicates that long-run causality from an exchange rate to the Korean stock market will exist. The results of vector error correction estimates show that: for long-term causality, the coefficient of the error correction term is significant with a negative sign, that is, long-term causality from exchange rates to the Korean stock market is observed. For short-term causality, the coefficient of the Japanese yen exchange rate is significant with a positive sign, that is, short-term causality from the Japanese yen exchange rate to the Korean stock market is observed. The coefficient of the financial crises i.e. 1997-1999 Asian financial crisis and 2007-2008 global financial crisis on the endogenous variables in the model and the Korean economy is significant. The result indicates that the financial crises have considerably affected the Korean economy, especially a negative effect on money supply.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.39-44
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• 2018

The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.

• International Journal of Quality Innovation
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• v.1 no.1
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• pp.106-120
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• 2000

The purpose of this research is to understand the effects of Quality Management Award on stock prices movement and to examine the comparative advantages of quality award system in Korea and the U.S. This study compares the performances of QM Award companies in the stock market with those of the market index in both countries. We develop Korean Quality Award Index(KQA Index) based on the Baldrige Index of NIST in the U.S. We inspect three studies. Study 1 tests if the performances of MB Award winners and S&P500 index have a difference in the stock market. Study 2 tests if the performances of KQA winners and KOSPI(Korean Composite Stock Price Index) have a difference in the stock market. Study 3 tests if the performances of KQA winners and MB Award winners have a difference in the stock market. From the empirical tests, the performances of KQA winners are superior to those of KOSPI and the performances of MB Award winners are superior to those of S&P500 and the performances of MB Award winners are superior to those of KQA winners.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• Korean System Dynamics Review
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• v.8 no.2
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• pp.175-190
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• 2007

Stock market volatility largely depends on firms' value and growth opportunities. However, with the globalization of world economy, the effect of the synchronization in major countries is gaining its importance. Also, domestically, the business cycle and cash market of the country are additional factors needed to be considered. The main purpose of this research is to attest the application and usefulness of System Dynamics as a general stock market forecasting tool. Throughout this research, System Dynamics suggests a conceptual model for forecasting a KOSPI(Korea Composite Stock Price Index), taking the factors of the composite stock price indexes in traditional researches. In conclusion of this research, System Dynamics was proved to bean appropriate model for forecasting the volatility and direction of a stock market as a whole. With its timely adaptability, System Dynamic overcomes the limit of traditional statistic models.

• Korean Management Science Review
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• v.24 no.2
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• pp.115-126
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• 2007

Weekly minima of daily log returns of Korean composite stock price index 200 and its five industry-based business divisions over the period from January 1990 to December 2005 are fitted using two block-based extreme distributions: Generalized Extreme Value(GEV) and Generalized Logistic(GLO). Parameters are estimated using the probability weighted moments. Applicability of two distributions is investigated using the Monte Carlo simulation based empirical p-values of Anderson Darling test. Our empirical results indicate that both the GLO and GEV models seem to be comparably applicable to the weekly minima. These findings are against the evidences in Gettinby et al.[7], who claimed that the GEV model was not valid in many cases, and supported the significant superiority of the GLO model.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.7-12
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• 2019

The application of neural networks to stock forecasting has received a great deal of attention because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from data, which is required to describe nonlinear input-output relations of stock forecasting. The paper builds neural network models to forecast daily KOrea composite Stock Price Index (KOSPI), and their performance is demonstrated. MAPEs of NN1 model show 0.427 and 0.627 in its learning and test, respectively. Based on the predicted KOSPI price, the paper proposes an alpha trading for trades in Exchange Traded Funds (ETFs) that fluctuate with the KOSPI200. The alpha trading is tested with data from 125 trade days, and its trade return of 7.16 ~ 15.29 % suggests that the proposed alpha trading is effective.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.603-608
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• 2008

In this paper an approach to building fuzzy model to predict daily open, close, high, and low stock prices is presented. One of prior problems in building a stock prediction model is to select most effective indicators for the stock prediction. The problem is overcome by the selection of information used in the analysis of stick-chart as the input variables of our fuzzy model. The fuzzy rules have the premise and the consequent, in which they are composed of trapezoidal membership functions, and nonlinear equations, respectively. DE(Differential Evolution) searches optimal fuzzy rules through an evolutionary process. To evaluate the effectiveness of the proposed approach numerical example is considered. The fuzzy models to predict open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) on a daily basis are built, and their performances are demonstrated and compared with those of neural network.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.745-748
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• 1996

Korea Composite Stock Price Index (KOSPI) was predicted using Cascade Correlation Network (CCN) model. CCN was suggested, by Fahlman and Lebiere [1990], to overcome the limitations of backpropagation algorithm such as step size problem and moving target problem. To test the applicability of CCN as a function approximator to the stock price movements, CCN was used as a tool for univariate time series analysis. The fitting and forecasting performance fo CCN on the KOSPI was compared with those of Multi-Layer Perceptron (MLP).