• The Korean Journal of Financial Management
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• v.10 no.1
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• pp.249-280
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• 1993

Recent financial theory views insurance policies as financial instruments that are traded in markets and whose prices reflect the forces of supply and demand. This article analyzes individual's insurance purchasing behavior along with capital market investment activities, which will provide a more realistic look at the tradeoff between insurance and investment in the individual's budget constraint. It is shown that the financial economic concept of insurance cost should reflect the opportunity cost of insurance premium. The author demonstrates the importance of riskless and risky financial assets in reaching an equilibrium insurance premium. In addition, the paper also investigates how the investment income could affect the four established theorems on traditional insurance literature. At the present time in Korea, the price deregulation is being debated as the most important current issue in insurance industry. In view of the results of this paper, insurance companies should recognize investment income in pricing their coverage if insurance prices are deregulated. Otherwise. price competition may force insurance companies to restrict coverage or to leave the market.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.171-198
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• 2009

The primary purpose of this study is to examine the impact of new BIS regulation, which is the preparations to incorporate not only credit risk but also market and operation risk, on the bank behaviors. As methodology, SUR(seemingly unrelated regression) and pool unit test are used in the empirical analysis of banks survived in Korea. It is employed that quarterly data of BIS capital ratio, ratio of standard and below loans to total loans, ratio of liquid assets to liquid liabilities, allowances for credit losses, real GDP, yields of corporate bonds(3years, AA) covering the period of 2000Q1~2009Q1. As a result, it could be indicated that effectiveness and promoting improvements of BIS capital regulation policy as follows; First, it is explicitly seen that weight of lending had decreased and specific gravity of international investment had increased until before BIS regulation is built up a step for revised agreement in late 2001. Second, after more strengthening of BIS standard in late 2002, banks had a tendency to decrease the adjustment of assets weighted risk through issuing of national loan that is comparatively low profitability. Also, it is implicitly sought that BIS regulation is a bit of a factor to bring about credit crunch and then has become a bit of a factor of economic stagnation. Third, as the BIS regulation became hard, it let have a effort to raise the soundness of a credit loan because of selecting good debtor based on its credit ratings. Fourth, it should be arranged that the market disciplines, the effective superintendence system and the sound environment to be able to raise enormous bank capital easily, against the credit stringency and reinforce the soundness of banks etc. in Korea capital market.

• Management & Information Systems Review
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• v.35 no.4
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• pp.1-19
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• 2016

The researchers in strategic management have focused on identifying the effects of CEO's demographic characteristics and experience on the strategic choices and performance of firms. On the other hand, they have failed to identifying the effects of CEO's psychological characteristics on them because of the difficulties over data collection and measurement for variables. To overcome this limitation of prior researches, this study is designed to achieve two specific objectives. The first is to examine the effect of CEO narcissism on diversification strategy and performance of listed corporations on Korean securities market in an economic downturn. The other is to examine the moderating effects of various corporate governance systems that are related to board and/or ownership structures on those relationships. The empirical setting for this study was drawn from a multi-year(2011~2014) sample of large listed corporations in Korean securities market. To achieve the objectives, the hypotheses of research are analyzed by implementing multiple regression analyses in two separate models. The results of these analyses show that CEO narcissism is positively related to the diversification of listed large corporations in Korean securities market. Regrading the moderating effects, the stake of institutional investors weakens the positive relationship between CEO narcissism and firm's diversification. The findings of this research imply that CEO narcissism can intensify the tendency of Korean corporations to adopt high-risk and high return strategy in an economic downturn. Thus, firms might be able to use CEO narcissism to drastically restructure the business portfolio in an economic downturn. However, Korean corporations should be very cautions to maximize the positive effect of CEO narcissism. They might be use the institutional investors as their corporate governance system to monitor and control the opportunism of CEO in the decision for diversification in an economic downturn.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.