• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.444-453
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• 2018

The study addresses the analysis on the financial determinants of corporate research and development (R&D) expenditure in finance. Overall level of R&D spending was estimated as one of the top-tier on a global basis and a majority of the expenditure was invested by large domestic firms in private sector. Consequently, financial factors that influence R&D intensity were empirically tested in the first hypothesis by using conditional quantile regression model for firms listed in KOSPI stock market in the post-era of the global financial turmoil. Firms in the groups of high- and low-R&D intensity were statistically compared to detect financial differences in the second hypothesis which was accompanied by the test of multi-logit model that included firms without R&D outlay. Concerning the results of the hypothesis tests, R&D spending of the prior fiscal year, firm size, business risk and advertising expense overall showed statistically significant impacts to determine the level. As an extended study of [1] that had examined financial factors of R&D intensity at the macro-level, the results of the present study are anticipated to contribute to maximizing shareholders' wealth in advance or emerging capital markets, when applied to find an optimal level of R&D expenditure.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• Management & Information Systems Review
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• v.32 no.4
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• pp.189-207
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• 2013

Using high-frequency data for 2 years, this study investigates intraday lead-lag relationship between stock index and stock index futures markets in Korea and China. We found that there are some differences in price discovery and volatility transmission between Korea and China after the stock index futures markets was introduced. Following Stoll-Whaley(1990) and Chan(1992), the multiple regression is estimated to examine the lead-lag patterns between the two markets by Newey-West's(1987) heteroskedasticity and autocorrelation consistent covariance matrix(HAC matrix). Empirical results of KOSPI 200 shows that the futures market leads the cash market and weak evidence that the cash market leads the futures market. New market information disseminates in the futures market before the stock market with index arbitrageurs then stepping in quickly to bring the cost-of-carry relation back into alignment. The regression tests for the conditional volatility which is estimated using EGARCH model do not show that there is a clear pattern of the futures market leading the stock market in terms of the volatility even though controlling nonsynchronous trading effects. This implies that information in price innovations that originate in the futures market is transmitted to the volatility of the cash market. Empirical results of CSI 300 shows that the cash market is found to play a more dominant role in the price discovery process after the Chinese index started a sharp decline immediately after the stock index futures were introduced. The new stock index futures markets does not function well in its price discovery performance at its infancy stage, apparently due to high barriers to entry into this emerging futures markets. Based on EGAECH model, the results uncover strong bi-directional dependence in the intraday volatility of both markets.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.135-144
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• 2017

This study is to identify the growth rate and volatility of logistics related firms in the stock market. To do this, we used monthly data for 197 years from June 2000 to October 2016 by selecting KOSPI and Transport & Storage(T&S), KOSDAQ, Transportation(TRANS) index. The purpose of this study is to compare the T&S and TRANS stock index returns with the KOSPI and KOSDAQ index. And we are to judge whether the development potential of the logistics industry and the value of the investment of related companies in the future is high. For this purpose, we will analyze the basic statistics, correlation and growth rate of each index, and compare T&S and TRANS with market returns. Analysis result, for the past 197 months logistics related T&S and TRANS have been higher than market returns. The correlation was highly related to TRANS and T & S in KOSPI, but it was not related to KOSDAQ. TRANS represents high risk and high return, while KOSDAQ represents high risk and low return market. TRANS is considered to be an efficient investment. We expect the future development of logistics related industries and T & S and TRANS to show a high rate of increase compared to the market returns.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.219-228
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• 2022

In order to improve audit quality, it is essential to understand the occurrence of disagreement between auditors and managers, and this study aims to analyze the impact of Business Strategies on audit risk and accounting audit delay. To this end, we conducted an empirical analysis using sample 2,910 firm-year data from 2018 to 2020 of KOSPI-listed and KOSDAQ-listed companies. The results of the empirical analysis of this study are as follows. First, compared to the companies of defender type, prospectors can expand audit procedures for new products, R&D costs, and intangible assets, and increase audit delays due to disagreement between managers and auditors. Second, compared to KOSPI-listed companies, the prospectors in KOSDAQ are more likely to have lower financial reporting quality, which further increases audit delays. The results of this study analyzed whether a company's Business Strategy affects the possibility of disagreement between an auditor and a company, and verified whether there is a difference in the audit report lag by stock market. The results of this study show that auditors' strong duty of care is needed for the companies of prospector type with high audit risk, and it is meaningful to present reinforced audit systems and specific guidelines for the companies of prospector type through the definition of prospector type. It also enables the expansion of research to identify the relationship between non-financial factors and audit risks that make up the companies of prospector type.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.283-286
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• 2007

This paper proposes a genetic algorithm (GA) approach to instance selection in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in CBR.

• The Korean Journal of Financial Studies
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• v.8 no.1
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• pp.55-76
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• 2002

본 연구는 KOSPI 200 선물시장의 거래자료를 이용하여 시장의 미시구조에 의한 하루중 수익률, 변동성 및 거래량 형태를 검토하였다. 본 연구의 주요 결과는 다음과 같다. 첫째, 지수선물시장의 하루중 변동성과 거래량 형태는 일말효과보다 일초효과가 크게 나타나는 조잡한 W자형 형태이다. 이러한 형태는 Brock-Kleidon(1992)의 시장폐장이론에 의해 설명되지만, 동시호가제 등 국내시장의 운영제도에 의해서도 영향을 받고 있음을 보여준다. 둘째, 현물시장의 폐장시간대의 변동성 감소는 한 금융시장의 폐장에서 다른 관련 금융시장의 가격변동성 하락을 예측한 King-Wadhwani(1990)의 이론적 연구결과와 일치한다. 셋째, 수익률의 하루중 형태는 요일에 따라 상이하며 매우 노이즈한 행태를 보여주었다. 그리고 수익률의 요일효과 분석에서 일주일 중 가장 낮은 수익률이 화요일에 발생하는 화요일 효과를 발견하였다. 월요일 효과도 발견되었지만, 그 크기면에서 화요일 효과가 지배적이었다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.730-733
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• 2004

본 연구에서는 넉아웃 옵션(Knock-out option)이 내재된 주가연계상품(ELS)의 성과를 시뮬레이션을 이용하여 추정한다. 옵션과 기초자산을 결합하여 구성되는 ELS는 상품개발 시점에서 그 수익구조가 결정되며, 실현수익률은 미래의 시장흐름에 의하여 결정된다. 현재 ELS는 옵션가격의 결정, 수익구조의 결정, 그리고 수익률 추정이라는 개별 과정이 각각 옵션발행자, 상품개발자, 고객관리자 등에 의하여 별도로 이루어지고 있는 실정이다. 본 연구에서는 이러한 개별 과정을 통합한 시뮬레이션 모형을 구축한 후, 이 모형의 결과(옵션가격, 수익구조, 실현수익률)를 기존 관행의 결과와 비교하여 본 연구에서 제안한 시뮬레이션 모형의 유용성을 제안한다. 분석 대상은 국내 장외파생상품 및 ELS의 기준이 되는 KOSPI 200 지수로 1990년 1월 3일부터 2002년 12월 30일까지의 1일 자료를 이용한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.177-182
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• 2003

많은 경제 시계열 자료 중에서 주가는 국내외 경제상황은 물론 정부정책 등 시장 외적인 영향에 가장 민감하게 반응한다. 하지만, 지금까지의 주가예측에 있어서는 이러한 외부의 영향, 즉 개입(Intervention)이 발생했을 때 주가의 변동에 능동적으로 대처하는 모형이 부재하였다. 실제로 이러한 개입사실을 예측모형에 반영하지 않는다면, 주가예측 있어 그 예측력을 따진다는 것은 무의미하다고 판단된다. 따라서, 개입시점을 발견하고, 이 개입효과를 측정하여 이를 모형에 반영한다면 좋은 예측결과를 얻을 수 있을 것이다. 이 연구에서는 이상점 탐지절차를 이용하여 개입 시점을 발견하고 개입의 효과가 개입시점에만 영향을 주는 모형과 효과가 일정기간 지속되는 모형으로 두 개의 개입시계열모형을 구축하고, 이러한 두 모형의 예측성과와 인공신경망모형을 이용한 예측성과를 비교하였다. 초단기예측(개입 직후 예측)에 있어서 개입의 효과가 지속되는 경우에는 개입시계열이 인공신경망보다 좋을 결과 를 나타내긴 했지만 그 차이는 크지 않았으며, 개입의 효과가 시점에만 영향을 준 경우에는 인공신경망의 결과가 더 우수한 것으로 나타났다. 단기예측(개입 후 20 일후의 예측)에 있어서는 개입 효과의 지속여부에 상관없이 인공신경망이 개입시계열모형보다 우수한 것으로 나타났다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.375-378
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• 2001

현재까지 행해진 주식시장 거품에 관한 연구들은 주가를 형성하는 근본요인으로서 현금배당만을 고려하고 있다. 그러나 현금배당은 경영자에 의해 조정가능하므로 진정한 기업가치를 나타내는 신호로 보기 어렵다. 따라서 본 연구에서는 기업가치를 더 정확히 반영하기 위하여 기업의 자본상에 나타나는 자금변동을 고려한 '순현금배당'을 도입한다. 상태-공간 모형과 칼만필터링 추정법을 이용하여 KOSPI 시장 자료에 대해 실증분석한 결과, 현금배당만 고려할 때와 '순현금배당'을 고려했을 때 추정된 거품이 매우 다른 양상을 보였다. 특히 한국의 금융위기시점을 고려하여 볼 때 '순현금배당'을 이용한 것이 실제 현상과 더 잘 부합하는 것으로 나타났다. 이는 주식시장의 거품을 연구하는데 있어 현금배당과 자금변동을 함께 고려하는 것이 더 정확한 추정결과를 얻을 수 있음을 시사한다.