Korea Composite Stock Price Index Research Articles

Forecasting international tourist arrivals in South Korea: a deep learning approach

Purpose This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism. Design/methodology/approach This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism. Findings This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management. Originality/value This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

Sequential monitoring for conditional quantiles of general conditional heteroscedastic time series models

AbstractIn this study, we introduce an online monitoring procedure designed to sequentially detect change points in the conditional quantiles of location‐scale time series models. This statistical process control issue holds great significance in risk management, particularly in measuring the value‐at‐risk or expected shortfall of financial assets. Our approach employs suitable detectors, including cumulative sum statistics. We then define a stopping rule and determine control limits based on asymptotic theorems to signal an anomaly. To further evaluate the proposed methods, we conduct a comprehensive empirical study analyzing various aspects of our monitoring procedures when applied to location‐scale time series models. Additionally, we perform a real data analysis using the daily returns of the Korea Composite Stock Price Index (KOSPI) and EuroStoxx 50 indices to affirm the adequacy of the proposed monitoring procedures in real‐world applications.

Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression.

This study proposes a control chart that monitors conditionally heteroscedastic time series by integrating the Huber support vector regression (HSVR) and the one-class classification (OCC) method. For this task, we consider the model that incorporates nonlinearity to the generalized autoregressive conditionally heteroscedastic (GARCH) time series, named HSVR-GARCH, to robustly estimate the conditional volatility when the structure of time series is not specified with parameters. Using the squared residuals, we construct the OCC-based control chart that does not require any posterior modifications of residuals unlike previous studies. Monte Carlo simulations reveal that deploying squared residuals from the HSVR-GARCH model to control charts can be immensely beneficial when the underlying model becomes more complicated and contaminated with noises. Moreover, a real data analysis with the Nasdaq composite index and Korea Composite Stock Price Index (KOSPI) datasets further disclose the validity of using the bootstrap method in constructing control charts.

Robust monitoring conditional volatility change for time series based on support vector regression

This study considers a robust monitoring procedure aimed at detecting an anomaly of conditional volatility from sequentially observed time series following a (nonlinear) generalized autoregressive conditional heteroscedastic (GARCH) model. We employ a specifically designed cumulative sum (CUSUM) method hybridized with the asymmetric Huber support vector regression, named AHSVR. AHSVR-GARCH model provides an effective way to model nonlinear GARCH time series while significantly augmenting the performance of the proposed monitoring process in terms of stability and detection ability. Monte Carlo simulations illustrate the functionality of AHSVR-GARCH monitoring scheme, demonstrating AHSVR-GARCH model’s superiority over the standard SVR-GARCH model when monitoring GARCH-type time series. Data analysis using the S&P 500, NASDAQ index, and Korea Composite Stock Price Index (KOSPI) also affirms our method’s efficacy in real-world applications.

A Study on Corporate Social Responsibility and Corporate Scandals of Chaebol

Purpose - This study reviews the corporate social responsibility (CSR) activities of Korea Composite Stock Price Index (KOSPI) companies involved in scandals. Additionally, we study Chaebol companies’ CSR activities of involved in the scandal and analyzes how much Chaebol companies with scandals spend on charitable donations.
 Design/Methodology/Approach - We exploit 11,975 firm-years listed on the Korea Exchange from 2001 to 2021, using the KOSPI firms as samples. We estimate the multiple regression model to test hypotheses. Also, we construct a one-to-one matching sample using the propensity score matching method to overcome endogeneity problems and to check the robustness.
 Findings - The analysis finds that companies with scandals donate more than those without. In addition, among these companies, Chaebol companies with scandals tend to spend less on donations.
 Research Implications - When corporate scandals occur, listed companies in Korea Exchange try to improve their image by actively engaging in CSR activities. However, Chaebol companies are not likely to engage in vigorous CSR activities even during scandals. The ethical level of a company can be inferred through its donation activities, depending on the situation.

A Study on Co-movements and Information Spillover Effects Between the International Commodity Futures Markets and the South Korean Stock Markets: Comparison of the COVID-19 and 2008 Financial Crises

Purpose - This paper aims to compare and analyze the co-movements and information spillover effects between the international commodity futures markets and the South Korean stock markets during the COVID-19 and the 2008 financial crises.
 Design/methodology - The DCC-GARCH model is used in the co-movements analysis. In contrast, the BEKK-GARCH model is used to evaluate information spillover effects. The statistical data used is from January 1, 2005, to December 31, 2022. It comprises the Korea Composite Stock Price Index data and daily international commodity futures prices of natural gas, West Texas Intermediate crude oil, gold, silver, copper, nickel, soybean, and wheat.
 Findings - The results of the co-movement analysis were as follows: First, it was shown that the co- movements between the international commodity futures markets and the South Korean stock markets were temporarily strengthened when the COVID-19 and 2008 financial crises occurred. Second, the South Korean stock markets were shown to have high correlations with the copper, nickel, and crude oil futures markets. The results of the information spillover effects analysis are as follows: First, before the 2008 financial crisis, four commodity futures markets (natural gas, gold, copper, and wheat) were shown to be in two-way leading relationships with the South Korean stock markets. In contrast, seven commodity futures markets, except for the natural gas futures market, were shown to be in two-way leading relationships with the South Korean stock markets after the financial crisis. Second, before the COVID-19 crisis, most international commodity futures markets, excluding natural gas and crude oil future markets, were shown to have led the South Korean stock markets in one direction. Third, it was revealed that after the COVID-19 crisis, the connections between the South Korean stock markets and the international commodity futures markets, except for natural gas, crude oil, and gold, were completely severed.
 Originality/value - Useful information for portfolio strategy establishment can be provided to investors through the results of this study. In addition, it is judged that financial policy authorities can utilize the results as data for efficient regulation of the financial market and policy establishment.

Financial Distress and Audit Report Lags: An Empirical Study in Korea

This study examines the association between a firm’s financial distress and audit report lags. Through this analysis, we intend to reveal whether auditors consider theclients’ financial distress when performing external audits. This study employs 2,786 firmyear observations from 2011 to 2018. The sample of this study consists of companies listedon the Korea Composite Stock Price Index (KOSPI) and the Korea Securities Dealers Automated Quotation (KOSDAQ). We perform OLS regression analysis to test our hypothesis. The OLS regression analysis is conducted through the SAS and STATA programs.We find that there is a significant and positive association between financial distress andaudit report lags. The audit report lags increase as the likelihood of clients’ financial distress increases. The results indicate that audits take different amounts of audit effort whenauditors consider financial distress as a business risk when they conduct audits. In otherwords, we provide evidence that auditors increase the amount of audit effort when thelikelihood of clients’ financial distress is high. In the absence of studies on how externalauditors respond to audited firms' financial distress, this study analyzes whether externalauditors change their audit efforts by assessing the audited firms' financial distress. Second, the empirical result that external auditors actually follow the guidelines related tobusiness risk and financial distress specified in the Korean Auditing Standards supportsthe effectiveness of the business risk-related regulations specified in the Korean AuditingStandards

Global Diversification, Real Earnings Management, and Future Performance: Evidence from Korea

ABSTRACT Research has generally focused on demonstrating real earnings management (REM) stemming from managerial opportunism using the agency framework. Examining a sample of firms on the Korea Composite Stock Price Index from 2011 to 2019, we explore the benefits of REM. Empirical results show a positive relationship between corporate globalization and the extent of REM, and corporate globalization mitigates the negative relationship between REM and future (operating and market) performance, observed only in firms with a low incentive to engage in REM for zero earnings. Global firms with more operational flexibility may strategically utilize REM to avoid temporary crises and perform better in the future.

Does investor sentiment create value for asset pricing? An empirical investigation of the KOSPI‐listed firms

AbstractThis paper proposes the development of an improved investor sentiment index (ISI) to apply on the Korea Composite Stock Price Index (KOSPI) and assess the vitality of sentiment‐based factor for explaining critical equity market anomalies in asset pricing in Korea. We follow the methodology of Huang et al. (2015), the align sentiment index, and employ the partial least squares method to overcome the drawbacks of the pioneering BM index of Baker and Wurgler (2006, 2007). Based on the daily trading and price data for individual companies from 2006 to 2021, we construct a novel ISI, which has robust predicting ability for the aggregate stock market return, in comparison to other popular measures of sentiment in the contemporary finance literature. Furthermore, the sentiment‐based factor in this paper captures the small firm effect that the asset pricing modelling, containing the more topical Fama–French five factor modelling (5F‐FF), has struggled to illuminate completely. Given that our results have shown Korean stock market as fairly well‐organised in terms of the availability of the market intelligence, we speculate our results to have important managerial implications for financial regulators in Korea and countries holding similar economic features.

Impact of International Mineral Price on Korea Composite Stock Price Index Market Capitalization

This study investigates market capitalization elasticity with respect to changes in mineral price by analyzing the relationship between the Korea Composite Stock Price Index market capitalization and the international prices of six major strategic minerals. Weekly data from the 1st week of 2009 to the 52nd week of 2022 were analyzed using the autoregressive distributed lag model. The model comprises market capitalization, mineral prices, exchange rate, money supply, and leading economic index variables with time lags. The results revealed that the changes in the prices of all six strategic minerals except bituminous coal had a positive effect on the market capitalization, while bituminous coal had no effect. The findings of this study support recent developments in the importance of strategic minerals in ensuring resource and economic security.

Finding hidden structure of sparse longitudinal data via functional Eigenfunctions

ABSTRACT In this research, we are interested in finding the hidden dependence structure of sparse longitudinal data. Finding the hidden dependence structure of sparse longitudinal data is difficult due to the starting and end times being different. We propose that finding the directional dependence structure of the eigenfunctions by sparse functional principal component analysis (FPCA) may be a good alternative solution to find the hidden dependence structure of sparse longitudinal data. To verify this idea, we apply sparse FPCA to simulated data and two real datasets, wage sparse longitudinal data and Korea composite stock price index (KOSPI) high-frequency minute tick data and then apply vine copula and copula dynamic conditional correlation with asymmetric GARCH model to the functional eigenfunctions from FPCA.

ESG 등급 변화를 이용한 책임투자전략 연구

Purpose - The purpose of this study was to examine the impact of ESG rating changes of companies listed in Korean Stock Exchange on stock returns.
 Design/methodology/approach - This study collected prices and ESG ratings of all the companies listed on the Korea Composite Stock Price Index. Based on yearly change of ESG ratings we grouped companies as 2 portfolios(upgrade and downgrade) and calculated portfolios’ return.
 Findings - First, the difference in returns between upgraded and downgraded portfolios is small and statistically insignificant. Second, however, in the COVID-19 period (2020 ~ 2021), the upgraded portfolio outperforms the downgraded portfolio by 0.7 percentage points per month. The difference in returns between upgraded and downgraded portfolios is statistically significant after controlling for the Carhart four factors. Lastly, there are much higher volatility when the ESG rating changes are made of companies with low levels of ESG ratings.
 Research implications or Originality This study is the first to examine the impact of ESG rating changes on stock returns in Korea. Furthermore, the findings can serve as a reference for managers who want to control a firm's risk by ESG rating changes. Practically, asset managers can use the findings to construct portfolios that are less risky or more profitable than the market portfolio.

Change point detection for the intraday volatility using functional ARCH and conditional Copula

In this research, we are concerned with intraday volatilities computed by functional ARCH(1) (fARCH(1), for short) model for high-frequency financial time series. A conditional-Copula multiple change point detection (CPD) for intraday volatilities is proposed using fARCH(1), bivariate Gaussian Copula and t-Copula conditional distributions. We employ current available multivariate CPD models which include energy test based control chart (ETCC) and nonparametric multivariate change point model (NPMVCP) to implement the proposed CPD method for the intraday volatilities. A simulation study is conducted to demonstrate that the functional ARCH based conditional-Copula CPD for the intraday volatilities can be a useful econometrics method to detect abnormal intraday volatilities in the financial market. We analyze intraday volatilities of the Korea composite stock price index (KOSPI) and the Hyundai-Motor (HDM) company stock data with one minute high-frequency to illustrate our proposed CPD method.

Market versus limit orders of speculative high-frequency traders and price discovery

The Korea Composite Stock Price Index (KOSPI) 200 futures market is one of the largest and most liquid index derivatives markets globally. We utilize high-quality intraday data on KOSPI 200 futures and find that high-frequency traders’ (HFTs’) market orders contribute much more to price discovery than their limit orders, as opposed to the findings of Brogaard, Hendershott, and Riordan (2019) in the Canadian equity market. To explain this phenomenon, we suggest that HFTs in the KOSPI 200 futures market are more speculative traders rather than market makers, which makes market orders more informative.

The Effect of Board Characteristics on the Relationship between Managerial Overconfidence and Audit Report Lag: Evidence from Korea

This study aimed to empirically analyze the effect of board characteristics on the relationship between managerial overconfidence and audit report lag. Managerial overconfidence was measured according to the method proposed by Schrand and Zechman (2012). The study sample comprised 4,179 firm-year observations listed on the Korea Composite Stock Price Index from 2011 to 2017. The results of the analysis were as follows. First, there was a significant positive correlation between managerial overconfidence and audit report lag. Second, the larger the board size, the more it mitigated the relationship between managerial overconfidence and audit report lag. Third, as the ratio of outside directors on the board increased, the positive relationship between managerial overconfidence and audit report lag decreased. This study is meaningful because it directly examined how the complex relationship between the characteristics of managers and the characteristics of the board of directors affects audit report lag. Managerial overconfidence increased the firm's audit risk and acted as a determinant of audit performance, and, by suggesting that it results in an increase in audit effort, the results of this study have implications for supervisory agencies, auditors, and audit target companies. In addition, the study is meaningful because it suggests that the characteristics of the board of directors can reduce audit risk by functioning as an excellent corporate governance structure.

ESG and Firm Performance: Focusing on the Environmental Strategy

In this study, we investigate whether firms’ eco-friendly strategies affect their value. For the analysis, we study 210 firms in the Republic of Korea. These firms were listed on the Korea Composite Stock Price Index and the Korea Securities Dealers Automated Quotations during 2017–2021. We measure the dependent variable by return on assets, return on equity, and Tobin’s Q as firm value and use the ordinary least square estimation. The results show that firms’ eco-friendly strategies have a positive effect on firm value. Additionally, we examine the effect of eco-friendly strategies on performance by industry and by duration. In the nonservice industry, there is a positive effect of environmental strategy on firm value for a 5-year window, but not for a 3-year window. In the service industry, in contrast, eco-friendly strategies have no effect on firm value for the 5-year window but have positive effects for the 3-year window. In the robustness check, for the endogeneity issue, we perform a two-stage least squares analysis. This study demonstrates that environmental actions are reflected in firm value and that the performance varies by industry. Thus, these results provide critical insights for managers and policy makers who consider the environmental issues of firms.

Linear time-varying regression with copula–DCC–asymmetric–GARCH models for volatility: the co-movement between industrial electricity demand and financial factors

ABSTRACT This paper examines the dependence structure of industrial electricity demand and financial indicators (the Korea Composite Stock Price Index [KOSPI], Korean Securities Dealers Automated Quotations [KOSDAQ], exchange rate, government bonds and exports) using the copula dynamic conditional correlation with symmetric and asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models to forecast volatility. We investigated symmetric and asymmetric GARCH types, such as the standard, exponential, Glosten–Jagannathan–Runkle and asymmetric power models to fit the marginal distribution. The two types of elliptical copula, Gaussian and Student’s t-distributions, were also considered to investigate the tail dependence between financial and electricity time series. We analysed the monthly log returns for January 2002 to April 2020. The empirical results reveal that the best-fit models for the Akaike information criteria are the asymmetric GARCH models, specifically the exponential-GARCH (E-GARCH). Moreover, the asymmetric GARCH model is superior to the symmetric GARCH in terms of forecast volatility. Extreme tail dependence exists for the KOSDAQ and exports indicators with the electricity demand. The KOSPI, Korea’s primary stock market, is the best-fit financial variable and presents the highest forecasting accuracy.

Understanding BOXPI — Industry portfolio perspectives

“BOXPI” is an acronym for the boxed Korea Composite Stock Price Index (KOSPI). Uniquely, KOSPI has remained within an extremely narrow range during 2012–2016 despite the global liquidity expansion. This study develops a continuous-time model to describe sector rotation and interprets the BOXPI phenomenon from industry portfolio perspectives. We find that the upper bound of the BOXPI can be interpreted as a consequence of the rotations from cyclical to defensive sector in the Korean stock market during that period. Furthermore, a Bayesian variable selection analysis shows that the lower bound of the BOXPI can be regarded as a result of low price-to-book ratio of the KOSPI in the BOXPI period.

Can investors profit by utilizing technical trading strategies? Evidence from the Korean and Chinese stock markets

The idea of this study is derived from observing the profitability of stock investments following the phenomena of continuously rising (or falling) prices of stocks and continuously overbought (or oversold) signals emitted by technical indicators. We employ the standard event study approach and technical trading strategies to explore whether investors would exploit profits in trading the constituent stocks of the Korea Composite Stock Price Index 50 and Shanghai Stock Exchange 50 when the aforementioned continuous phenomena occur. We find that both the Korean and Chinese stock markets are not fully efficient; this finding may enhance the robustness of the existing literature. In addition, we reveal that contrarian strategies are appropriate for the trading stocks listed on the Korean stock market for all the cases investigated in this study. However, momentum strategies are appropriate for the Chinese stock market when continuously rising stock prices and overbought signals are simultaneously observed. These findings imply that the difference in investor behaviors between the Korean and Chinese stock markets might result in dissimilar trading strategies being employed for these two markets.

Tax Avoidance, Tax Risk, and Corporate Governance: Evidence from Korea

This study examines the association between the effective corporate tax rate and the volatility of future effective corporate tax rates in Korean companies. We analyzed the effect of corporate governance on the association between tax avoidance and tax risk. Our sample is comprised of all the firms listed on the Korea Composite Stock Price Index market. We measure each firm’s tax avoidance as GAAP ETR, Cash ETR, and BTD, and use the corporate governance rating of the Korea Corporate Governance Service to measure corporate governance. Our results show that the volatility of the effective corporate tax rate and the effective corporate tax rate would have a significant negative association. Our results show that tax risk decreases when the corporate tax avoidance level increases and the tax risk increases when the corporate tax avoidance level decreases. In addition, we find that the better the corporate governance structure, the higher the level of supervision and control of managers, thereby mitigating the impact of tax evasion on future corporate tax risk. The findings of this study regarding tax avoidance and corporate governance are important for investors because tax risk can significantly affect investor welfare.