• The Korean Journal of Financial Management
• /
• v.11 no.1
• /
• pp.233-262
• /
• 1994

본 연구는 주식시장(株式市場)의 이상현상(異狀現象)중의 하나인 요일효과(曜日效果)(day of the week effect)를 전통적인 회귀분석(回歸分析)이 아닌 ARCH 또는 GARCH 모형을 사용하여 조건부(條件附) 평균수익률(기대수익률)(平均收益率(期待收益率)) 뿐만아니라 조건부(條件附) 분산(分散)에도 나타나는지에 대하여 분석하였으며, 규모별(規模別)에 따라 요일효과(曜日效果)에 어떠한 차이가 나타나는지를 분석하였다. 본 연구의 추정결과를 요약하면, 조건부(條件附) 평균수익률(기대수익률)(平均收益率(期待收益率)) 및 조건부(條件附) 분산(分散) 모두에 있어 요일효과(曜日效果)가 뚜렷하게 존재하는 것으로 나타났다. 즉, 조건부(條件附) 평균수익률(平均收益率)에 대해서는 월요일(月曜日)은 부(負)의 효과, 토요일(土曜日)은 정(正)의 효과가 나타났으며, 조건부(條件附) 분산(分散)에 대해서는 월요일(月曜日)은 정(正)의 효과가, 토요일(土曜日)은 부(負)의 효과가 발견되었다. 그러나 한국(韓國)의 주식시장의 본격적인 성장기이면서 주식가격의 등락이 심했던 $86\sim92$년(年)간의 표본기간 동안에는 조건부(條件附) 분산(分散)에 대한 요일효과(曜日效果)는 존재하였으나, 조건부(條件附) 평균수익률(平均收益率)에 대한 요일효과(曜日效果)는 존재하지 않는 것으로 나타났다. 그리고 소형지수(小型指數)가 중(中) 대형지수(大型指數)와는 다른 주가행태를 보이는 것으로 나타났으며, 다음과 같은 몇 가지의 규모별(規模別) 차이(差異)를 보였다. 첫째, 조건부(條件附) 평균수익률(平均收益率)에 대한 분석에서 중(中) 대형지수수익률(大型指數收益率)을 사용하였을 경우에는 요일효과(曜日效果)가 나타난 반면에, 소형(小型) 지수수익률(指數收益率)의 경우에는 화요효과(火曜效果)가 존재하는 것으로 나타났다. 둘째, 조건부(條件附) 분산(分散)에 대한 분석에서 정(正)의 공휴일효과(公休日效果)가 다른 규모별 지수수익률(指數收益率)의 경우에는 나타나지 많았지만 소형(小型) 지수수익률(指數收益率)의 경우에는 존재하는 것으로 나타났다. 세째, 소형(小型) 지수수익률(指數收益率)의 경우 모형 추정후의 정규잔차(定規殘差)(normalized residuals) 및 정규자승잔차(定規自乘殘差)(normalized squared residuals)에 대한 시계열상관(時系列相關) 검정결과 모형의 부적합성(不適合性)이 나타났다. 본 연구는 기존의 기대수익률(期待收益率) 위주의 요일효과(曜日效果) 분석에서 주식수익률(株式收益率)의 분산(分散) 즉, 변동성(變動性)에 촛점을 두어 분석하였으며, 이는 투자자의 정확한 위험측정(危險測定)수단의 제공이라는 면에서 의의(意義)가 있을 것으로 생각된다.

• Journal of Digital Contents Society
• /
• v.18 no.3
• /
• pp.585-592
• /
• 2017

As development of personal devices have made everyday use of internet much easier than before, it is getting generalized to find information and share it through the social media. In particular, communities specialized in each field have become so powerful that they can significantly influence our society. Finally, businesses and governments pay attentions to reflecting their opinions in their strategies. The stock market fluctuates with various factors of society. In order to consider social trends, many studies have tried making use of bigdata analysis on stock market researches as well as traditional approaches using buzz amount. In the example at the top, the studies using text data such as newspaper articles are being published. In this paper, we analyzed the post of 'Paxnet', a securities specialists' site, to supplement the limitation of the news. Based on this, we help researchers analyze the sentiment of investors by generating a domain-specific sentiment lexicon for the stock market.

• Journal of Industrial Convergence
• /
• v.18 no.5
• /
• pp.1-13
• /
• 2020

The purpose of this study is to find out how much the investment effect of convertible bond(CB) is from the perspective of investors and to present efficient investment plans to investors. The research method is to investigate the coupon interest rate, maturity interest rate, conversion price, etc. for CBs. As a result of the study, it was analyzed that CB's investment efficiency was low because the conversion price excess days ratio was only about 1/4 of the conversion date. The conversion day yield was -6.3% and the maturity day yield was -5.2% on average. It was analyzed that the number of stocks with negative conversion day yield was 2.4 times higher than the number of positive stocks and 3.7 times higher than the number of positive stocks with a maturity day yield, so the expected return on equity conversion of CB was low.

• Journal of Korea Port Economic Association
• /
• v.39 no.4
• /
• pp.269-283
• /
• 2023

Based on daily data from January 4, 2016 to September 27, 2022, the impact of extreme movements of BDI on shipping companies' network connectivity was analyzed using CoVaR network connectivity. The main results and policy implications are as follows. First, according to the copula model results, the Student-t copula was selected as the most suitable model for COSCO, HMM, HRAG, MAERSK, and WAN. EVER was selected as a time-varying Gumbel copula, and YANG was selected as a time-varying rotated-Gumbel copula. Second, as a result of analysis using the TVP-VAR model, the linkage between shipping companies tended to increase when the BDI turned into an extreme risk state. In the comparison of net connectivity, the roles of COSCO and EVER changed. In addition, in the analysis of net pairwise connectivity, it was found that the change in the extreme risk state of BDI also affected the connectivity of shipping companies. In particular, EVER, WAN, and COSCO showed large changes. Taken together, the extreme fluctuations in BDI changed the role of Asian shipping companies, intensifying competition among shipping companies and strengthening risk delivery. It was confirmed that BDI has a great influence on the network connectivity of shipping companies and has an important influence on the stability of the stock market network. Therefore, the results of this study should consider not only the connectivity of shipping companies according to market conditions, but also the connectivity in extreme situations.

• Journal of Korea Port Economic Association
• /
• v.26 no.1
• /
• pp.222-233
• /
• 2010

This paper aims at predicting the BDI from Jan. to Dec. 2010 using such econometric techniues of the univariate time series as stochastic ARIMA-type models and Hodrick-Prescott filtering technique. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the two ARIMA models and five Intervention-ARIMA models. The monthly data cover the period January 2000 through December 2009. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared error (RMSE), mean absolute error (MAE) and mean error (ME). The RMSE and MAE indicate that the ARIMA-type models outperform the random walk model And the mean errors for all models are small in magnitude relative to the MAE's, indicating that all models don't have a tendency of overpredicting or underpredicting systematically in forecasting. The pessimistic ex-ante forecasts are expected to be 2,820 at the end of 2010 compared with the optimistic forecasts of 4,230.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.63-83
• /
• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Service Research and Studies
• /
• v.10 no.4
• /
• pp.71-88
• /
• 2020

CB(Convertible bond) is mezzanine security that have the characteristics of bonds and stocks. From the perspective of investors, the purpose of the research is to empirically investigate the degree of investment efficiency of CB and to suggest efficient investment plans. The research method investigated the maturity interest rate, conversion price, and conversion date for CB, and then linked it with daily stock price fluctuations after the conversion date to determine the degree of investment efficiency and stock conversion effect of CB. As a result of the study, it was analyzed that the ratio of the conversion price exceeded days was only about 1/4 of the conversion date, so the investment efficiency was low. The conversion day yield was -6.3% on average and the maturity day yield was -5.2% on average, showing a minus return on average, which was calculated differently from investor expectations. It was analyzed that the number of stocks with a minus conversion day is 2.4 times greater than the number of plus stocks and 3.7 times more than the number of plus stocks with a minus maturity return, so the expected return on stock conversion of CB is low. The research contribution was derived from the problem that the expected rate of return of CB is not high, and it is that the investor's point of view when purchasing CB was established.

• Management & Information Systems Review
• /
• v.33 no.1
• /
• pp.83-97
• /
• 2014

This study examined the effects institutional block ownership on the stock market liquidity in Korean Stock Market. The two measures of institutional block ownership are used. They are the percentage of a stock owned by institutional blockholder and the number of institutional blockholder that own the stock. This study used the Amihud(2002) illiquidity measure to measure stock market liquidity. The results are as fellows. First, this study showed that the number of institutional blockholder is significantly negatively correlated with the Amihud(2002) illiquidity measure in the analysis which is used the whole data. But we found no a consistent results between the number of institutional blockholder and the Amihud(2002) illiquidity measure in the grouped institutional blockholder's number analysis. This indicates that the effects institutional blockholder on market liquidity is not simple. Second, this study showed that the percentage of a stock owned by institutional blockholder are negatively related with Amihud(2002) illiquidity measure, especially revealed statistically significant in the group 3(11.71%~17.38%) and group 4(7.45%~11.65%). This results suggest that the institutional blockholder have positive effect on the market liquidity in the group 3 and 4. Third, the significance of the percentage of institutional block ownership and the number of institutional block ownership in explaining illiquidity are more showed in the term of the global financial crisis(2008) than the before and the after of the global financial crisis.

• Journal of Digital Convergence
• /
• v.19 no.8
• /
• pp.161-171
• /
• 2021

According to the equity home bias theory, foreign investors are considered to have less information than native investors. However, as the economy becomes liberalized and overseas economic innovation has a great influence on the local economy, it is possible for foreign investors to invest as informed traders. This study analyzes whether information on trade amount by nationality has specific characteristics. The findings are summarized as follows. First, the increase in trading by foreign investors has negative effects on stock returns. There is no significant difference in these negative effects by nationality. This means that foreign investors show strong herd behavior regardless of nationality. Second, foreigners' investment activities increase stock price volatility, but the impact is not significant. Third, the behavior of foreign investors is still positive feedback. However, there are signs that positive feedback behavior may be changing, especially for funds from the United States and the Cayman Islands. Finally, tax haven zone funds have different investment strategies than other foreign investors. However, Cayman Islands funds, which are estimated to be closely related to Korea, are different from Luxembourg and Ireland funds. These findings undermine the fundamentals of the equity home bias theory.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.19-43
• /
• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.