• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.707-715
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• 2021

This article examines the potential impact of the Covid-19 Lockdown on earnings manipulation and stock market sensitivity to earnings announcements. It also explores the effects of earnings manipulation after the COVID-19 outbreak on the share price sensitivity to the earnings disclosures. The study uses a quantitative method to analyze the financial data consisting of 87 firms listed on the Iraq Stock Exchange for the period from 2018 to 2020, which constitutes a total of (174 observations). We used Ohlson (1995) model to estimate financial market reaction and sensitivity to earnings manipulation fluctuations and accounting information. The results show that companies practice earnings manipulation to maintain earnings over a time series, which means a negative impact of earnings manipulation on all earnings measures' value relevance (EPS, BVS, and CFS). Accordingly, earnings manipulation negatively influences investor behavior in the financial market, based mainly on financial reporting. The value relevance of financial reports has also decreased because of the COVID-19 outbreak and related economic Lockdown. These results reflect a long-term adverse impact of earnings manipulation on investor behavior and financial statements reliability.

• The Korean Journal of Financial Studies
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• v.3 no.1
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• pp.179-211
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• 1996

This study examines whether trading mechanisms or market microstructures of markets have an effect on the integration issue of the international equity market. If the international equity market is integrated, identical stocks listed on different international stock exchanges should have the same rates of return, the same characteristics of stock price behavior and similar distributions of return. If different market microstructures, or trading mechanisms cause differences in characteristics of stock price behavior, those can lead to different rates of return because of different liquidity risk for the same stocks between markets. This study proposes international asset pricing with liquidity risk related to trading mechanisms. Systematic risk by itself cannot predict the sign of expected rate of return difference for the same stocks between international markets. Liquidity risk factors related to market microstructure provide explanations for the sign of rate of return differences between markets, However, liquidity risk factors related to market microstructure do not have a significant effect on the rate of return differences and sensitivity of return differences between markets, Trading mechanisms or market microstructures might not have a significant effect on the interpretation of the international equity market integration studies, if trading volume or other factors are controlled.

• Journal of Navigation and Port Research
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• v.47 no.2
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• pp.100-105
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• 2023

This study investigated determinants of stock betas of shipping companies in Korea. Beta is a measurement of sensitivity of an individual stock to the movement of the whole stock market. It is widely accepted that stock betas are not constant, but time-varying, which implies that they are affected by other factors. In this regard, this study examined betas of six shipping companies listed on the Korea Exchange for the period of 2000-2021 and their relationship with financial leverage, operating leverage, and cyclicality in the shipping market. Empirical analysis showed that betas of Korean shipping companies were positively associated with financial and operating leverages but negatively with cyclicality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.1-7
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• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.

• Korean Management Science Review
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• v.26 no.2
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• pp.19-35
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• 2009

This paper investigated performance of the Markowitz's portfolio selection model with applications to Korean stock market. We chose Samsung-Group-Funds and KOSPI index for performance comparison with the Markowitz's portfolio selection model. For the most recent one and a half year period between March 2007 and September 2008, KOSPI index almost remained the same with only 0.1% change, Samsung-Group-Funds showed 20.54% return, and Markowitz's model, which is composed of the same 17 Samsung group stocks, achieved 52% return. We performed sensitivity analysis on the duration of financial data and the frequency of portfolio change in order to maximize the return of portfolio. In conclusion, according to our empirical research results with Samsung-Group-Funds, investment by Markowitz's model, which periodically changes portfolio by using nonlinear programming with only financial data, outperformed investment by the fund managers who possess rich experiences on stock trading and actively change portfolio by the minute-by-minute market news and business information.

• The Korean Journal of Financial Management
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• v.26 no.4
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• pp.103-140
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• 2009

This paper investigates various channels through which liquidity can affect stock returns and examines whether behavioral explanation for liquidity risk is reasonable. First, we examine whether liquidity level (average liquidity) plays a significant role in determining asset returns. The result is consistent with the hypothesis that a stock with higher average illiquidity will have a higher expected return. Second, we focus on the argument that liquidity has a non-diversifiable systematic component. If systemic liquidity has a different impact across individual securities, a stock that is more sensitive to systematic liquidity will have a higher expected return. The results of various tests are inconsistent with each other, not completely supporting the argument. Finally, the intra-market tests in Korea support the behavioral explanation for the liquidity premium, and the effect is stronger in the liquidity level than in the liquidity beta related to systematic liquidity.

• Asia-Pacific Journal of Business
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• v.10 no.2
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• pp.27-41
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• 2019

Following the methodology of Ben-Rephael, Kadan and Wohl (2015), this paper examines whether firm-level liquidity premium exists and whether the premium exhibits a long-term trend in the Korean stock market. The results show that over the whole sample period (1998-2018), a liquidity premium of 0.083% exists in the cross-section of stocks. Interestingly, the pricing of liquidity declines significantly over the sample period. Sub-period analysis indicates that liquidity is priced mainly in the first sub-period (1998-2004) with a significant monthly premium of 0.304%, while the pricing of liquidity becomes weaker or insignificant in the second (2005-2011) and the third (2012-2018) period. I also find that the significance of the liquidity premium in the first period is attributed to small stocks. To explore underlying reasons that might affect the decline in the liquidity premium, I decompose liquidity premium into the product of firm-level liquidity and the sensitivity of expected stock returns on liquidity. The results reveal that the long-term decline is explained by both an increase in firm-level liquidity and a decrease in the sensitivity of expected returns on liquidity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.123-130
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• 2022

Pair trading is a statistical arbitrage investment strategy. Traditionally, cointegration has been utilized in the pair exploring step to discover a pair with a similar price movement. Recently, the clustering analysis has attracted many researchers' attention, replacing the cointegration method. This study tests a clustering-driven pair trading investment strategy in the Korean stock market. If a pair detected through clustering has a large spread during the spread exploring period, the pair is included in the portfolio for backtesting. The profitability of the clustering-driven pair trading strategies is investigated based on various profitability measures such as the distribution of returns, cumulative returns, profitability by period, and sensitivity analysis on different parameters. The backtesting results show that the pair trading investment strategy is valid in the Korean stock market. More interestingly, the clustering-driven portfolio investments show higher performance compared to benchmarks. Note that the hierarchical clustering shows the best portfolio performance.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.181-190
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• 2021

This paper empirically examines the asymmetric response of the Indonesian Islamic stock market to macroeconomic variables encompassing money supply, domestic output, exchange rate, and Federal Reserve rate. Our study employs the Jakarta Islamic Index (JII) after the financial crisis in the Southeast Asian country using monthly data from January 2000 to December 2019. Non-linear Autoregressive Distributed lag (NARDL) is applied. Our study considers two models consisting of the model without the Federal Reserve rate and the model with it. Our findings confirm the long-run link between Jakarta Islamic Index and macroeconomic factors being studied. Furthermore, the Jakarta Islamic Index asymmetrically responds to broad money supply and exchange rate, but not to domestic output and Federal Reserve rate. A reduction in the money supply has a worse effect on Islamic stock prices as compared to an increase in the money supply. The Jakarta Islamic Index responds differently to depreciation and appreciation. The transmission of the exchange rate to Islamic stock prices occurs only for appreciation. Our study finds an absence of transmission mechanism from the domestic output and the interest rate to Islamic stock prices. Our results imply that the easy money policy and stabilizing currency are key to supporting Indonesian Islamic stock prices.