• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.67-74
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• 2021

This study aims to examine and analyze the effect of Risk Profile, Good Corporate Governance (GCG), Earnings, Capital (RGEC), and Earnings per Share (EPS) on stock prices with financial distress as an intervening variable. The sampling technique used purposive sampling based on certain criteria and data used was secondary data, that is, annual reports of commercial banks in Indonesia for the period of 2012-2018 with a sample of 23 banks from a total population of 81 banks. This type of research is explanative with a quantitative descriptive approach to describe or explain quantitative data. The data obtained was analyzed using SEM (Structural Equation Model) with the AMOS Program. The results showed that RGEC, EPS, and financial distress affect stock prices. This is based on testing the direct effect as indicated by a p-value that is smaller than 0.05. Based on the mediation test, the results show that financial distress cannot mediate the effect of RGEC and EPS on stock prices as indicated by a p-value greater than 0.05. The implication of this research is very important for investors to analyze stock price changes based on RGEC, EPS, and financial distress to gain profits. In addition, there are various warning signs indicating that a company is experiencing financial distress or it is heading towards such a state. Being aware of these signs can help prevent failure.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.278-281
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• 1998

This paper examines the mutlivariate nearest neighbor forecasting model which considers the volume traded as well as the stock price. The empirical results using the data from KOSPI indicate that the predictive power of the nearest neighbor model increases as the model becomes mutlivariate.

• The Journal of Industrial Distribution & Business
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• v.8 no.5
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• pp.87-93
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• 2017

Purpose - This paper investigates whether managerial overconfidence is associated with firm-specific crash risk. Overconfidence leads managers to overestimate the returns of their investment projects, and misperceive negative net present value projects as value creating. They even use voluntary disclosures to convey their optimistic beliefs about the firms' long-term prospects to the stock market. Thus, the overconfidence bias can lead to managerial bad news hoarding behavior. When bad news accumulates and crosses some tipping point, it will come out all at once, resulting in a stock price crash. Research design, data and methodology - 7,385 firm-years used for the main analysis are from the KIS Value database between 2006 and 2013. This database covers KOSPI-listed and KOSDAQ-listed firms in Korea. The proxy for overconfidence is based on excess investment in assets. A residual from the regression of total asset growth on sales growth run by industry-year is used as an independent variable. If a firm has at least one crash week during a year, it is referred to as a high crash risk firm. The dependant variable is a dummy variable that equals 1 if a firm is a high crash risk firm, and zero otherwise. After explaining the relationship between managerial overconfidence and crash risk, the total sample was divided into two sub-samples; chaebol firms and non-chaebol firms. The relation between how I overconfidence and crash risk varies with business group affiliation was investigated. Results - The results showed that managerial overconfidence is positively related to crash risk. Specifically, the coefficient of OVERC is significantly positive, supporting the prediction. The results are strong and robust in non-chaebol firms. Conclusions - The results show that firms with overconfident managers are likely to experience stock price crashes. This study is related to past literature that examines the impact of managerial overconfidence on the stock market. This study contributes to the literature by examining whether overconfidence can explain a firm's future crashes.

• 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 Industrial Convergence
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• v.1 no.1
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• pp.143-157
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• 2003

This paper examines the changed consumer perception to internet based channel. Internet based channel has time merit, place merit, assortment merit, compatibility merit, and so on. For seller, in the mean while, it has merits in the way of diminishing physical distribution cost, promotion cost, and reaching globally in the same time. In spite of so many merits of internet based channel, there were many types complain in past. Most of all, consumers expect that it will provide low-price merit to consumer, because it doesn't need shop, warehouse, stock, etc. Based on the empirical analyses in past, it didn't work, especially to price oriented consumer's perception. But in this research, it shows changing consumer's perception. Comparing past data with current data, we found outstanding gross in price related variables figure. But, in goods delivery related factors and personal credit information related factor, consumer recognized much more negatively yet. So, we conclude that even though some factors show improved perception, there are tasks to solve. We will observe the tuning point at that time.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.297-309
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• 2021

In this study, to capture the skewness and kurtosis detected in both conditional and unconditional return distributions of the stock markets of Kazakhstan and Russia, two versions of normal mixture GARCH models are employed. The data set consists of daily observations of the Kazakhstan and Russia stock prices, and world crude oil price, covering the period from 1 June 2006 through 1 March 2021. From the empirical results, incorporating the long memory effect on the returns not only provides better descriptions of dynamic behaviors of the stock market prices but also plays a significant role in improving a better understanding of the return dynamics. In addition, normal mixture models for time-varying volatility provide a better fit to the conditional densities than the usual GARCH specifications and has an important advantage that the conditional higher moments are time-varying. This implies that the volatility skews implied by normal mixture models are more likely to exhibit the features of risk and the direction of the information flow is regime-dependent. The findings of this study contain useful information for diverse purposes of cross-border stock market players such as asset allocation, portfolio management, risk management, and market regulations.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.23-30
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• 2021

The purpose of this study is to compare the level of risk and return of Islamic stocks in the Jakarta Islamic Index (JII) with conventional stocks on the IDX30 in the period from January 2017 to July 2019. The Sharpe ratio method is used to calculate risk and stock returns. The performance of the stock portfolio is measured by comparing the risk premium portfolio with the portfolio risk that is expressed as a standard deviation of the total risk. This study uses secondary data collected by the Indonesia Stock Exchange (IDX), which provides the names of stock issuers included in the JII and IDX30 indices along with their montly closing price. The results of the descriptive analysis show that the JII Sharpe ratio index from January 2017 to July 2019 is from the minimum range of -0.28820 to a maximum range of 0.05622, while the IDX30 Sharpe ratio index from January 2017 to July 2019 is from the minimum range of -0.09290 to the maximum range of 0.17436. The results of inferential analysis using a different test show that there is a significant difference between the Sharpe ratio JII and IDX30 in measuring the performance of the stock portfolio.

• Korea Trade Review
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• v.45 no.2
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• pp.229-262
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• 2020

The price-to-earnings ratio (PER) is an important indicator to measure the stock price and profitability of a firm; it is also the most used valuation indicator among investors. When using the PER to compare the investment values of different stocks, these stocks must come from the same sector. This study mainly focuses on the China's listed manufacturing firms. By learning from previous research results and analyzing the current situation, we studied the correlation between the manufacturing sector's PER and its influencing factors from both macro and micro perspectives, the combination of which eventually sheds light on such correlation. Analyzing GDP growth rate data, Manufacturing Purchasing Managers' Index, and other macroeconomic variables from 2008 to 2018, we conclude that these variables jointly have a certain impact on the average PER of the manufacturing sector. We then form panel data based on relevant (2014-2018) data gathered from 317 of China's A-listed manufacturing firms to study the impact of micro-variables on PER. By using Stata and other software to analyze the panel data, we reach the conclusion that the Debt to Asset Ratio, Return on Equity, EPS growth rate, Operating Profit Ratio, Dividend Payout Ratio, and firm size have a significant impact on PER. The Current Ratio, Treasury Stock ratio and Ownership Concentration have no distinct effect on PER. Based on our empirical findings, we design a theoretical model that affects the PER.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.133-141
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• 2021

Governments of each country are actively implementing fiscal expansion policies to recover the real economy after Corona 19. In Korea, the stock market and housing market are greatly affected as liquidity in the market increases due to the implementation of disaster subsidies and welfare policies. The purpose of this study is to analyze the relationship between stock market and housing market trends and liquidity. Data were collected by the Bank of Korea and Kookmin Bank. The analysis period is from January 2000 to December 2020, and monthly data are used. For empirical analysis, the rate of change from the same month of the previous year was calculated for each variable, and numerical analysis, index analysis, and model analysis were performed. As a result of the analysis, it was found that the stock index showed a positive(+) relationship with the house price, while a negative(-) relationship with M2. Previous studies have suggested that, in general, an increase in liquidity affects the stock market and the housing market, and inflation also rises. In this study, it was found that the stock market and the housing market had an effect on each other. However, it was investigated that liquidity showed an inverse relationship with the stock market and had no relationship with the housing market. Through this, this study estimated that there is a time difference in the relationship between liquidity and the stock market & housing market.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1435-1440
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• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.