• The Journal of Information Systems
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• v.14 no.1
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• pp.107-124
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• 2005

ERP systems are comprehensive sorfware packages that seek to integrate the complete range of lbusiness processes and functions in order to present a heuristic perspectives of a firm from a single information and information technlogy architecture. The ERP systems have delicate internal controls with built-in devices. It is known that the delicate internal controls help to enhance the accounting transparency. We empirically investigate the relationship between the ERP systems inplementations and an accounting transparency. In order to measure the accounting transparency differences, we compare the ERP systems implementation firms with firms which did not implement the ERP systems by 6 financial ratios (accruals, net profit margin, operation cash folo to sales, total debt to equity, accounts receivable changes, assets quality). Data are collecte from 135 firms implemented the ERP systems and 135 firms non-implemented the systems (the firms listed in the Korea Stock Exchange). We analyze financial statements from 270 firms for the period 2001-2003 to ezamine the 6 financial ratios differences. The results of 810 firms analyses over the 3-year period indicate that the ERP systems implementation firms show the statistically significant differences in the accrual ratio, the net profit margin ratio, operating cash flow to sales ratio, and total debt to equity ratio from the ERP systems non-implementation firms. But there is statistically no differences between the two groups for accounts receivable changes to sales ratio and assets quality.

• Journal of Korea Trade
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• v.24 no.5
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• pp.35-54
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• 2020

Purpose - It seems common sense that corporate social responsibility (CSR) is a key driver of business sustainability. Nevertheless, there has been little research on the performance of socially responsible activities, including economic and environmentally responsibility activities, in internationally diversified firms. Design/methodology - The purpose of this study was to evaluate the effects of CSR activities on management performance. For this evaluation, an empirical analysis was conducted with total of 2,520 cases, selected from companies listed on the Korea Composite Stock Price Index market for six years from 2013 to 2018. As proxies for management performance, financial data such as a total asset net profit ratio and a total asset operating ratio were used. A multivariate regression analysis was conducted to test hypotheses. Findings - The results of this analysis indicated that firms in the CSR outstanding group were ranked significantly higher than other groups in management performance. In addition, CSR activities of internationally diversified firms positively influenced the total asset net profit ratio and total asset operating ratio. Originality/value - The results suggest that the CSR activities of these firms can play a significant role in enhancing management performance in the economic status of Korea, where the degree of export dependency is high.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.839-850
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• 2021

Risk-adjusted return is believed to be one of the optimal parameters to determine an optimum portfolio. A risk-adjusted return is a calculation of the profit or potential profit from an investment that takes into account the degree of risk that must be accepted to achieve it. This paper presents a new procedure in portfolio selection and utilizes these results to optimize the risk level of risk-adjusted Islamic stock portfolios. It deals with the weekly close price of active issuers listed on Jakarta Islamic Index Indonesia for a certain time interval. Overall, this paper highlights portfolio selection, which includes determining the number of stocks, grouping the issuers via technical analysis, and selecting the best risk-adjusted return of portfolios. The nominated portfolio is modeled using Quadratic Programming (QP). The result of this study shows that the portfolio built using the lowest Value at Risk (VaR) outperforms the market proxy on a risk-adjusted basis of M-squared and was chosen as the best portfolio that can be optimized using QP with a minimum risk of 2.86%. The portfolio with the lowest beta, on the other hand, will produce a minimum risk that is nearly 60% lower than the optimal risk-adjusted return portfolio. The results of QP are well verified by a heuristic optimizer of fmincon.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.251-265
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• 2021

It seems a common sense that corporate social responsibility (CSR) is a key driver to attain business sustainability. Nevertheless, there has been little research on the performance of socially responsible activities, including economic and environmental responsibility activities in internationally diversified firms. The purpose of this study was to evaluate the effects of CSR activities on management performance. For this evaluation, an empirical analysis was conducted with a total of 2,520 cases, selected from companies listed on the Korea Composite Stock Price Index market for six years from 2013 to 2018. As proxies for management performance, financial date such as a total asset net profit ratio and a total asset-operating ratio were used. A multivariate regression analysis was conducted to test hypotheses. The results of this analysis indicated that firms in the CSR outstanding group are significantly higher than other groups in management performances. In addition, CSR activities of internationally diversified firms positively influence their total asset net profit ratio and total asset-operating ratio. The results suggested that CSR activities of these firms can play a significant role in enhancing management performances amid the economic status of Korea, where a degree of export dependency is high.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.19-26
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• 2019

The paper aims to measure stock price volatility on Ho Chi Minh stock exchange (HSX). We apply symmetric models (GARCH, GARCH-M) and asymmetry (EGARCH and TGARCH) to measure stock price volatility on HSX. We used time series data including the daily closed price of VN-Index during 1/03/2001-1/03/2019 with 4375 observations. The results show that GARCH (1,1) and EGARCH (1,1) models are the most suitable models to measure both symmetry and asymmetry volatility level of VN-Index. The study also provides evidence for the existence of asymmetric effects (leverage) through the parameters of TGARCH model (1,1), showing that positive shocks have a significant effect on the conditional variance (volatility). This result implies that the volatility of stock returns has a big impact on future market movements under the impact of shocks, while asymmetric volatility increase market risk, thus increase the attractiveness of the stock market. The research results are useful reference information to help investors in forecasting the expected profit rate of the HSX, and also the risks along with market fluctuations in order to take appropriate adjust to the portfolios. From this study's results, we can see risk prediction models such as GARCH can be better used in risk forecasting especially.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.97-125
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• 2014

This study investigates the relationship between stock index and its associated nearby futures markets based on the cost-of-carry model. The purpose of this study is to explore the existence of mispriced futures contracts, and to test whether traders can earn trading profits in real financial market using the information about the mispriced futures contracts. This study suggests the concordance correlation coefficient to investigate the existence of mispriced futures contracts. The concordance correlation coefficient gives a desirable result for trading profits that results from a comparative analysis among profits from trading at the time to indicate trading opportunities determined by the degree of the difference between the observed market price and the theoretical price of a futures contract. In addition, this study also explains that the concordance correlation coefficient developed from the mean square error (MSE) has a statistically theoretical meaning. In conclusion, this study shows that the concordance correlation coefficient is appropriate for analyzing the relationship between the observed stock index futures market price and the theoretical stock index futures price derived from the cost-of-carry model.

• Korean Journal of Construction Engineering and Management
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• v.6 no.6 s.28
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• pp.98-106
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• 2005

The construction cost index and the forecasting model of apartment house can be efficient for evaluating the validness of the fluctuating price, and for making guidelines for construction firms when calculating their profit. In this study the previous construction cost index of apartment house was improved, and the forecasting model based on X-12 ARIMA was developed. According to the result, during the last five years the construction cost, excluding labor expense, has risen approximately to 22.7%. And during next three years, additional 16.8% rise of construction cost is expected. Those quantitative results can be utilized for evaluating the apartment house's selling price in an indirection, and be helpful to understand the variation pattern of the price.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.487-496
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• 2021

This study examines the environmental performance of firms in Vietnam and its determinants. The contribution of the paper is on both theoretical and empirical aspects. On the theoretical matter, the research proposes a new index measuring environmental management at the firm level, namely the Environmental Management Index with a clear illustration for the case of Vietnam. On the empirical matter, the study points out and estimates determinants of the corporate environmental performance of Vietnamese firms measured by the newly proposed index. Due to data availability and the impossibility of getting more updated data, the empirical analysis covers only the period from 2004-2009. However, findings are still meaningful because, on the one hand, it provides some evidence for Vietnamese policymakers; on the other hand, with the robust methodology proposed, when more recent data are available, researchers can easily replicate the estimation for more insights. Empirical results show that factors having positive impacts on the environmental performance of Vietnamese firms are profit, capital stock, and interestingly public pressure proxied by the population of the province where the firm is located. Firm ownership does also matter in explaining the corporate environmental performance of Vietnam.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.