• The Korean Journal of Financial Management
• /
• v.26 no.1
• /
• pp.65-94
• /
• 2009

The event of change in KOSPI 200 Index composition is one of the main subjects for the test of EMH. According to EMH, when a certain event is not related with firm's fundamental value, stock price should not change after the announcement of news. This hypothesis leads us to the conclusion of horizontal demand curve of stock. This logic was questioned by Shleifer(1986) and argued that downward sloping demand curve hypothesis was supported. But Harris and Gruel(1986) found a different empirical evidence that price reversal occurs in the long run, which is called price pressure hypothesis. They argued that short term price effect by large block trading (price pressure) is offset in the long run because these event is unrelated to fundamental value. Therefor, they argued that EMH can not be rejected in the long run. Until now, there are two empirical studies with Korean market data in this area. Using a data with same time period of $1996{\sim}1999$, Kweon and Park(2000) and Ahn and Park(2005) showed that stock price or beta is not significantly affected by change in index composition. This study retested this event expanding sample period from 1996 to 2006, and analyzed why this event was considered an uninformative events in the preceding studies. We analyzed a market impact by separating samples according to firm size and market condition. In case of newly enlisted firm, we found the evidence supporting price pressure hypothesis on average. However, we found the long run price effect in the sample of large firms under bearish markets. At the same time, we know that the number of samples under the category of large firms under bearish markets is relatively small, which drives the same result of supporting the hypothesis that change in index composition is a non-informative event on average. Also, the long run price effect of large size firms under bearish markets was supported by the analyses using trading volumes. On the other hand, in case of delisting from the index, we found the long run price effect but that was not supported by trading volume analyses.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.3
• /
• pp.73-85
• /
• 2020

The study aims to provide better understanding of sustainable earnings by a comprehensive analysis of earnings persistence of business firms in Vietnam as an example of developing economies in South-East Asia. Dataset of 1,278 publicly listed firms (excluding banking and financial services firms) on Vietnam Stock Exchange for the period from 2008 to 2017 was collected. By applying fixed effect regression model, the empirical results provided the basis to measure the persistence index (Pers index) and find low level of their earnings persistence. The literature of earnings quality analysis in developed countries suggests earnings persistence as a noteworthy determinant of future earnings forecast and stock valuation. However, research of sustainable earnings in developing countries is still highly underdeveloped. For Vietnamese listed firms, the average Pers index was estimated for the period from 2008 to 2010, indicating low level of earnings persistence. We also incorporated earnings persistence level into future earnings forecast by running the quintile regression model divided the data into four equal levels and conducted each section independently to see the difference in each percentile, thence assessed the factors' influence on the specific model. The findings provide important information on the expected returns of firms, especially helping investors make sound decisions.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.7
• /
• pp.149-158
• /
• 2020

The purpose of this study was to investigate the impact of corporate governance index on the cost of equity in companies listed on the Tehran Stock Exchange. This study collects data from 975 observations during the period 2012 to 2018 to test the hypotheses using multiple linear regression model for the panel data. In this research, the independent variable of corporate governance index comprises of 27 specific corporate governance attributes. The results of hypothesis testing showed that corporate governance has a negative and significant effect on the rate of capital cost. In other words, the quality of corporate governance can lower the rate of capital cost. This result suggests that, by using a powerful corporate governance system and by declining the information asymmetry (increasing transparency) and agency conflict, we would be able to enhance the quality of financial reports. It would strengthen the capital market, attract financial suppliers and investors, and absorb the required financial resources of the firm by a lower rate. The findings of the study suggest that companies are able to reduce the cost of equity by establishing strong corporate governance. This conclusion suggests the importance and effectiveness of corporate governance in the cost of equity.

• Journal of Korean Society of Transportation
• /
• v.20 no.6
• /
• pp.45-56
• /
• 2002

In this research we estimated regional gross fixed capital stock of transport sector, such as road railroad, airport and seaport during 1968-1997 in Korea. We also compared our estimation results with those of Korea and Japan. As basic analytic method, we used the regional allocation method. To estimate regional gross fixed capital stock of transport sector, we used the basic data on national wealth surveys in 1997, regional land price index and regional facilities index in transport sectors. We used the most reasonable data in the process of estimation after reviewing the collected data In order to get the reasonable capital stock by regions. we chose the allocation index which can minimize the difference between the estimated result and the real regional capital stock in the process to allocate the total gross capital into the regions. Compared our results with those of other researches in Korea, estimates in our research project could be said more accurate than those.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.5
• /
• pp.95-101
• /
• 2008

Modeling of stock prices forecast has been considered as one of the most difficult problem to develop accurately since stock prices are highly correlated with various environmental conditions including economics and political situation. In this paper, we propose a agent system approach to predict Korea Composite Stock Price Index (KOSPI) using neural network and statistical methods. To minimize mean of prediction error and variation of prediction error, agent system includes sub-agent modules for feature extraction, variables selection, forecast engine selection, and forecasting results analysis. As a first step to develop agent system for KOSPI forecasting, twelve economic indices are selected from twenty two basic standard economic indices using principal component analysis. From selected twelve economic indices, prediction model input variables are chosen again using best-subsets regression method. Two different types data are tested for KOSPI forecasting and the Prediction results showed 11.92 points of root mean squared error for consecutive thirty days of prediction. Also, it is shown that proposed agent system approach for KOSPI forecast is effective since required types and numbers of prediction variables are time-varying, so adaptable selection of modeling inputs and prediction engine are essential for reliable and accurate forecast model.

• Land and Housing Review
• /
• v.14 no.4
• /
• pp.39-46
• /
• 2023

This study explores the interrelationship between 'KRX Construction' and 'Business Survey Index'. KRX Construction is a leading economic indicator of construction industry, implying the potential interdependence with BSI Construction. Previous papers have investigated the relationship between the released stock price index and BSI. Using Granger causality tests, this study investigates how the BSI Construction is associated with the trend and noise-trading components of KRX Construction, respectively. The decomposition of KRX Construction of trend and noise-trading is based on the state-space model. The results document unilateral Granger causalities from released KRX Construction, trend component, noise-trading component to BSI Construction. In sum, this study demonstrates that construction company CEOs view stock price index as a leading economic indicator.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.5
• /
• pp.689-694
• /
• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.3
• /
• pp.123-128
• /
• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.

• Journal of Industrial Convergence
• /
• v.20 no.6
• /
• pp.135-140
• /
• 2022

In the domestic stock market, the capital market opened in January 1992, and the proportion of foreign capital has steadily increased, accounting for 30% of the domestic market in Overall stock market trend infers that the domestic stock market is more influenced by foreign issues than domestic issues. The trading trend of foreign capital displays a similar flow to exchange rate fluctuations,; thus, preparing an investment strategy by using the Pearson analyzing method the effect of exchange rates of foreign capital trading, fluctuations in exchange rates, and predicting one of the macroeconomic indicators will yield high returns in the stock market. Therefore, this research was conducted to help investment by predicting foreign variables comparing and analyzing exchange rates and foreign capital trading patterns, and predicting appropriate time for buying and selling.

• The Journal of Information Systems
• /
• v.32 no.1
• /
• pp.179-199
• /
• 2023

Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.