• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.66-79
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• 2016

A discussion has recently emerged over the increase of approvals of K-REITs, which is concluded on the basis of how to raise funds for business activity, fulfill the expected rate of return and maximize the management of managing investment funds. In addition, corporations need to acknowledge the necessity of the capital structure reflected in the current economic environment and decision-making processes. This research analyzed the characteristics by investment types and influence factors about the debt ratio of K-REITs. The data were collected from general management about business state, investment, and finance from 2002 to 2015 in K-REITs (except for the GFC period of 2007~2009). The results of the research demonstrated the high ratios of the largest shareholder characteristics, which are corporation, pension funds, mutual funds, banks, securities, insurance, and, recently, the increasing ratio of the largest shareholder and major stockholder. The investment of K-REITs is increasing the role of institutional investors that take a leading development of K-REITs. The behaviors of simultaneous investment of institutional investors were analyzed to show that they received higher interest rates than other financial institutions and ran in parallel with attraction and compensation. The results of the multiple regressions analysis, utilizing variables about debt ratio were as follows. The debt ratio showed a negative (-) relation that profitability is increasing, which matches the pecking order theory and trade off theory. On the other hand, investment opportunities (growth potential) showed a negative (-) relation and assets scale that indicated a positive (+) relation. The research results are reflected as follows. K-REITs focused on private equity REITs more than public offering REITs, and in the case of financing the capital of others, loan capital is operated under the guarantee of tangible assets (most of real estate) more than financing of the stock market. Further, after the GFC, the capital of others was actively utilized in K-REITs business, and the debt ratio showed that the determinant factors by the ratio and characteristics of the largest shareholder and investment products.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.4 no.4
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• pp.45-70
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• 2009

This article investigates which types of the strategies announced by the listed firms contribute to enhancing the long-term performance of the companies. Since 2002, Korean Exchange adopted the "faire disclosure policy" which mandates that all publicly traded companies must disclose material information to all investors at the same time. Thanks to the policy, Korean investors can, now, easily access the board's decision on management strategies on the same day the decision is made. If the companies trustfully carry out their announced strategies, we can decide which types of strategies actually enhance or deteriorate the long-term performance, simply by comparing the announced strategies and the firm's performance. The sample companies are confined to 60 firms that became listed in the KOSDAQ market through back-door listing from 2003 to 2005. Using only the newly listed companies, we can avoid the interference on the long-term performance of the strategies pursued before the event date. This often holds true, for many companies radically modify their strategies after the listing. Furthermore, the back-door listing companies serve our purpose better than IPO companies do, because the former tend to have a variety of announcement within a given period of time beginning the listing date. Using these sample companies, this article analyzes the effect on one year buy-and-hold returns and abnormal buy-and-hold returns after the listing of the various types of strategies announced during the same period of time. The results show that those evidences of restructuring such as 'reduction of capital' and 'resignation of incumbent board members', actually contribute to the increase in adjusted long-term stock returns. Those strategies which can be view as evidence of new investment such as 'increase in tangible assets', 'acquisition of other companies', do also helps the stockholders better off. On the contrary, 'increase in bank loans', 'changes of CEO' and 'merger' deteriorate the equity value. The last findings let us to presume that the back-door listing companies appear to use the bank loans for value-reducing activities; the change in CEO is not a sign of restructuring, but rather a sign of failure of the restructuring; another merger carried out after back-door listing itself is also value-reducing activity. This article's findings on reduction of capital, merger and bank loans oppose the results of the former empirical studies which analyze only the short-term effect on stock price. Therefore, more long-term performance studies on public disclosures are in order.

• Journal of the Korea Society of Computer and Information
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• v.6 no.4
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• pp.188-205
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• 2001

Insurer's duty of declaration is based on reciprocity of principle of the highest good, and recently it is widely recognized in the British and American insurance circles. The conception of fiduciary relationship is no longer equity or the legal theory which is only confined to the nations with Anglo-American laws. Therefore, recognizing the fiduciary relationship as the essence of insurance contract, which is more closely related to public interest than any other fields. will serve an efficient measure to seek fair and reasonable relationship with contractor, and provide legal foundation which permits contractor to bring an action for damage against violation of insurer's duty of declaration. In the future, only when the fiduciary relationship is approved as the essence of insurance contract, the business performance and quality of insurance industry is expected to increase. Therefore, to keep well this fiduciary relationship, or increase the fiduciary relationship persistency rates seems to be the bottom line in the insurance industry. In this paper, we developed a fiduciary relationship maintenance ratio based on comparison by case, which is represented with usually maintained contract months to paid months, based on each contract of the basis point. In this paper we have developed a new business model seeking the maximum profit with low cost and high efficiency, management policy of putting its priority on its substantiality, as an improvement measure to break away from the vicious circle of high cost and low efficiency, and management policy of putting its priority on its external growth(expansion of market share).

• KDI Journal of Economic Policy
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• v.12 no.3
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• pp.27-45
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• 1990

Two opposing views prevail regarding the economic impact of rural out-migration on the rural areas of origin. The optimistic neoclassical view argues that rapid rural out-migration is not detrimental to the income and welfare of the rural areas of origin, whereas Lipton (1980) argues the opposite. We developed our own alternative model for rural to urban migration, appropriate for rapidly developing economies such as Korea's. This model, which adopts international trade theories of nontraded goods and Dutch Disease to rural to urban migration issues, argues that rural to urban migration is caused mainly by two factors: first, the unprofitability of farming, and second, the decrease in demand for rural nontraded goods and the increase in demand for urban nontraded goods. The unprofitability of farming is caused by the increase in rural wages, which is induced by increasing urban wages in booming urban manufacturing sectors, and by the fact that the cost increases in farming cannot be shifted to consumers, because farm prices are fixed worldwide and because the income demand elasticity for farm products is very low. The demand for nontraded goods decreases in rural and increases in urban areas because population density and income in urban areas increase sharply, while those in rural areas decrease sharply, due to rapid rural to urban migration. Given that the market structure for nontraded goods-namely, service sectors including educational and health facilities-is mostly in monopolistically competitive, and that the demand for nontraded goods comes only from local sources, the urban service sector enjoys economies of scale, and can thus offer services at cheaper prices and in greater variety, whereas the rural service sector cannot enjoy the advantages offered by scale economies. Our view concerning the economic impact of rural to urban migration on rural areas of origin agrees with Lipton's pessimistic view that rural out-migration is detrimental to the income and welfare of rural areas. However, our reasons for the reduction of rural income are different from those in Lipton's model. Lipton argued that rural income and welfare deteriorate mainly because of a shortage of human capital, younger workers and talent resulting from selective rural out-migration. Instead, we believe that rural income declines, first, because a rapid rural-urban migration creates a further shortage of farm labor supplies and increases rural wages, and thus reduces further the profitability of farming and, second, because a rapid rural-urban migration causes a further decline of the rural service sectors. Empirical tests of our major hypotheses using Korean census data from 1966, 1970, 1975, 1980 and 1985 support our own model much more than the neoclassical or Lipton's models. A kun (county) with a large out-migration had a smaller proportion of younger working aged people in the population, and a smaller proportion of highly educated workers. But the productivity of farm workers, measured in terms of fall crops (rice) purchased by the government per farmer or per hectare of irrigated land, did not decline despite the loss of these youths and of human capital. The kun having had a large out-migration had a larger proportion of the population in the farm sector and a smaller proportion in the service sector. The kun having had a large out-migration also had a lower income measured in terms of the proportion of households receiving welfare payments or the amount of provincial taxes paid per household. The lower incomes of these kuns might explain why the kuns that experienced a large out-migration had difficulty in mechanizing farming. Our policy suggestions based on the tests of the currently prevailing hypotheses are as follows: 1) The main cause of farming difficulties is not a lack of human capital, but the in­crease in production costs due to rural wage increases combined with depressed farm output prices. Therefore, a more effective way of helping farm economies is by increasing farm output prices. However, we are not sure whether an increase in farm output prices is desirable in terms of efficiency. 2) It might be worthwhile to attempt to increase the size of farmland holdings per farm household so that the mechanization of farming can be achieved more easily. 3) A kun with large out-migration suffers a deterioration in income and welfare. Therefore, the government should provide a form of subsidization similar to the adjustment assistance provided for international trade. This assistance should not be related to the level of farm output. Otherwise, there is a possibility that we might encourage farm production which would not be profitable in the absence of subsidies. 4) Government intervention in agricultural research and its dissemination, and large-scale social overhead projects in rural areas, carried out by the Korean government, might be desirable from both efficiency and equity points of view. Government interventions in research are justified because of the problems associated with the appropriation of knowledge, and government actions on large-scale projects are justified because they required collective action.