• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.189-200
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• 2015

Stationary bootstrapping is applied to a Lagrangian multiplier (LM) test to test market microstructure noise (MMN) in financial asset prices. A Monte-Carlo experiment shows that the bootstrapping method improves the size of the original LM test which has some size distortion for conditional heteroscedastic models. The proposed test is illustrated for real data sets like KOSPI index and Won-Dollar exchange rate.

• 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.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.153-160
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• 2015

The domestic stock market has been subjected to a major change since the September 1997 financial crisis. Foreign capital came repeat themselves in the stock market and bond market, foreign exchange market opening up domestic financial markets after the financial crisis. The domestic stock market has been most affected by domestic capital before the financial crisis. But it has been receiving an absolute influenced by foreign capital after the financial crisis. The purpose of this study is to analyze the trends in the two sections that look at any changes in the volatility of the KOSPI appears after the crisis. To this, obtained a daily weekly monthly normal distribution and kurtosis, skewness degree it should be analyze the tilt phenomenon and variability of the two intervals. This study also predict the future movement of the domestic stock market Based on this, look at the difference between the two sections. Analysis result, after the financial crisis change width has a reduction but direction of the KOSPI has appeared relatively distinct in the medium to long term. Based on this future market seems desirable the mid- to long-term investment looking for direction.

• Journal of Convergence for Information Technology
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• v.10 no.3
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• pp.67-75
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• 2020

This paper investigated the information spillover effect between stock market and bond market with the KOSPI daily index and MMF yield data. The overall analysis period is from May 2, 1997 to August 30, 2019. The empirical analysis was conducted by dividing the period from May 2, 1997 to December 30, 2008 before the global financial crisis, and from December 30, 2008 to August 30, 2019 after the global financial crisis, and the overall analysis period. The analysis shows that the EGARCH model considering asymmetric variability is suitable. The price spillover effect and volatility spillover effect existed in both directions between the stock market and the bond market, and the price transfer effect was greater in the period before the global financial crisis than in the period after the global financial crisis. Asymmetric volatility in information between stock and bond markets appears to exist in both markets.

• Management & Information Systems Review
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• v.33 no.1
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• pp.45-63
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• 2014

A trading system is a computer trading program that automatically submits trades to an exchange. Mechanical a trading system to execute trade is spreading in the stock market. However, a trading system to trade a single asset might occur instability of the profit because payoff of this system is determined a asset movement. Therefore, it is necessary to develop a trading system that is trade two assets such as a pair trading that is to sell overvalued assets and buy the undervalued ones. The aim of this study is to propose a relative value based trading system designed to yield stable and profitable profits regardless of market conditions. In fact, we propose a procedure for building a trading system that is based on the rough set analysis of indicators derived from a price ratio between two assets. KOSPI 200 index futures and S&P 500 index futures are used as a data for evaluation of the proposed trading system. We intend to examine the usefulness of this model through an empirical study.

• Korean Management Science Review
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• v.32 no.3
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• pp.91-103
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• 2015

This study recognizes that there is a correlation between the movement of the financial market and the sentimental changes of the public participating directly or indirectly in the market, and applies the relationship to investment strategies for stock market. The concerns that market participants have about the economy can be transformed to the search terms that internet users query on search engines, and search volume of a specific term over time can be understood as the economic trend of big data. Under the hypothesis that the time when the economic concerns start increasing precedes the decline in the stock market price and vice versa, this study proposes three investment strategies using casuality between price of domestic stock market and search volume from Naver trends, and verifies the hypothesis. The computational results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior in domestic stock market.

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.85-107
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• 2006

This paper investigates whether the futures market responds to the news more sensitively and uses more diverse information than the spot market. The sensitivity to the news is measured by the coefficients of the model which regresses the daily changes in the futures prices to the daily changes in the theoretical prices computed from spot prices using the spot-futures parity. The diversity of news is measured by the mean range differences ($\overline{RD}$), mean hi-price differences($\overline{HD}$) and mean low-price differences. The data in this paper is the closing prices of the nearest-to-maturity and the second-nearest-to-maturity contracts of the KOSPI 200 index futures. As the estimates of the relative sensitivity of the futures prices($^{\beta}$) for the whole-period sample are not significantly different from 1, the sensitivity of two markets to the news are not different. However, $\hat{\beta}$ of the most recent period(Nov. 2002 to Dec. 2005) are strongly different from 1. And, in the most recent period, the futures price changes for the good news, which is defined as the price increase of KOSPI of more than 1.5% in a day, show additional sensitivity. Since the mean range different which measures the relative diversity of information used, are not significantly different from 0 for the whole-period and subperiod samples, and this can be interpreted that the futures market does not use more diverse information than the spot market. However, the mean high-price difference, which measures the relative diversity of good news, are significantly different from 0 for the nearest-maturity contracts in the whole-period and subperiod samples. This evidence supports that the futures prices reflects more diverse good news which brings price increase in the market.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.15-27
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• 2012

We have studied the realized variance(RV) of intra-day returns and market microstructure noise based on high-frequency stock transaction data for the four largest companies in terms of market capitalization in the KOSPI. First, non-negligible biases are observed for the RV and for the bias-corrected realized variance($RV_{AC_1}$) which is constructed by adjusting RV for the first order autocorrelation in intra-day returns. Bias is more obvious for the RV and the $RV_{AC_1}$ when intra-day returns are sampled more frequently than every 2 minutes. Transaction Time Sampling(TTS) is shown to be better than Calendar Time Sampling(CTS) in terms of biases of the RV and the $RV_{AC_1}$ for the 4 companies. The analysis reveals that market microstructure noise is temporally dependent. Second, by using the Noise-to-Signal Ratio(NSR), we estimate sampling frequencies that are optimal in terms of the Mean Square Errors(MSE) of the RV and the $RV_{AC_1}$. The optimal sampling frequencies are around 200 for RV and is around 5000 for the $RV_{AC_1}$ for all the four stock prices. For the 6 hour transaction period of the Korean stock trading, these correspond to about 2 minutes and 6 seconds.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.36-46
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• 2019

If stock market is efficient, any well-devised trading rule can't consistently outperform the average stock market returns. This study aims to verify whether the strategy based on bid-ask volume information can beat the stock market. I suggested a day trading strategy using order imbalance indicator and empirically analyzed its profitability with the KOSPI 200 index futures data from 2001 to 2018. Entry rules are as follows: If BSI is over 50%, enter buy order, otherwise enter sell order, assuming that stock price rises after BSI is over 50% and stock price falls after BSI is less than 50%. The empirical results showed that the suggested trading strategy generated very high trading profit, that is, its annual return runs to minimum 71% per annum even after the transaction costs. The profit was generated consistently during 18 years. This study also improved the suggested trading strategy applying the genetic algorithm, which may help the market practitioners who trade the KOSPI 200 index futures.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.353-361
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• 2018

The purpose of this study is to analyze how Corporate Real Estate Ratio affects Firm's Value for KOSPI Companies and KOSDAQ Companies. The period of analysis is the period of the Financial Crisis (2007-2008), the period immediately after the Financial Crisis (2009-2011), and the period following the introduction of IFRS (2012-2016). The samples of 2761 KOSPI Companies years and 3719 KOSDAQ Companies years are used in this study. The result of Regression Analysis shows that the higher Corporate Real Estate Ratio is, the more negative Firm's Value is. In the comparison of markets, both KOSPI Companies and KOSDAQ Companies had no statistically significant effects during the Financial Crisis. Although KOSDAQ Companies had a greater negative effect on the Firm's Value than KOSPI Companies right after the Financial Crisis, KOSPI Companies have had a greater negative influence on the Firm's Value than KOSDAQ Companies since the introduction of IFRS. Therefore, each corporation should pay more attention to identifying the appropriate amount of their Corporate Real Estate Ratio and should continue to analyze and make decisions on the most efficient use of real estate which is owned by each company. This study shows speciality in dividing into three period, such as the period of the Financial Crisis, the period immediately after the Financial Crisis, and the period following the introduction of IFRS and comparing the degree of influence on the KOSPI Companies and KOSDAQ Companies for the each period. I hope to study the factors that affect the company's real estate policy.