• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.151-166
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• 2019

New drug development requires 10 to 15 years of long time and more than $ 1 billion in funding, ranging from basic research${\rightarrow}$preclinical medicine${\rightarrow}$clinical medicine${\rightarrow}$product approval${\rightarrow}$sales. Many new drug development bio-venture companies will continue to pursue new drug development with funds secured through listing on the securities market. This study focuses on the impact of the listing on the market of bio-venture companies in the development of new drugs. It is necessary to determine whether the increase in registered patent, preclinical, clinical and technology transfer contracts at the time of listing (D) The results of this study are as follows. We also analyzed whether the registered patent, preclinical, and clinical effects had significant effect on technology transfer contracts at two years after listing and listing. The results of the analysis are as follows. First, Korea's new drug development bio-venture firms increased their registered patents but did not increase their pre-clinical, clinical and technology transfer contracts. Second, at the time of listing and two years after listing, pre-employment has a significant effect on Korea's technology transfer contracts and has a significant effect on overseas technology transfer contracts. However, registered patents and clinics have significant influence on technology transfer contracts. Korea 's new drug development bio-venture firms showed patent increase despite the stock market listing, but pre-clinical, clinical and technology transfer contracts did not increase. In order to strengthen technological commercialization of new drug development bio-venture companies in the future, it is required to establish R & D strategy for efficient use of IPO subscription funds, open innovation through strengthening industry-academia-partnerships, and more sophisticated preclinical and clinical strategy establishment.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Korean Society of Forest Science
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• v.20 no.1
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• pp.1-44
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• 1973

Needless to say, the management of national forest in all countries is very important in view of the national mission and management purposes. Korean national forest is also in particular significant in promoting national economy for the continuous increasing of the demand for wood, conservation of the land and social welfare. But there's no denying the fact that the leading aim of the Korean forest policy has been based upon the conservation of forest resources and recovery of land conservation function instead of improvement of the forest productive capacity. Therefore, the management of national forest should be aimed as an industry in the chain of the Korean national economy. And the increment of the forest productive capacity based on rationalized forest management is also urgently needed. Not only the increment of the timber production but also the establishment of the good forest in quality and quantity are to bring naturally many functions of conservation and other public benefits. In 1908 Korean national forest was historically established for the first time as a result of the notification for ownership, and was divided into two kinds in 1911-1924, such as indisposable national forest for land conservation, forest management, scientific research and public welfare, and the other national forest to be disposed. Indisposable forest is mostly under the jurisdiction of national forest stations (Chungbu, Tongbu, Nambu), and the tother national forests are under custody of respective cities and provinces, and under custody of the other government authorities. As of the end of 1971, national forest land is 19.5% (1,297,708 ha) of the total forest land area, but growing stock is 50.1% ($35,406,079m^3$) of the total forest growing stock, and timber production of national forest is 23.6% ($205,959m^3$) of the year production of total timber in Korea. Accordingly, it is the important fact that national forest occupies the major part of Korean forestry. The author positively affirms that success or failure of the management of national forest controls rise or fall of forestry in Korea. All functions of forest are very important, but among others the function of timber production is most important especially in Korea, that unavoidably imports a large quantity of foreign wood every year (in 1971 import of foreign wood-$3,756,000m^3$, 160,995,000 dollars). So, Korea urgently needs the improvement of forest productive capacity in national forest. But it is difficult that wood production meets the rapid increase of demand for wood to the development of economy, because production term of forestry is long, so national forest management should be rationalized by the effective investment and development of forestry techniques in the long view. Although Korean national forest business has many difficulties in the budget, techniques and the lack of labour due to outflow of rural village labour by development of national economy, and the increase of labour wages and administrative expenses etc. the development of national forest depends on adoption of the suitable forest techniques and management adapted for social and economical development. In this view point the writer has investigated and analyzed the status of the management of national forest in Korea to examine the irrational problems and suggest an improvement plan. The national forestry statistics cited in this study is based on the basic statistics and the statistics of the forest business as of the end of 1971 published by Office of Forestry, Republic of Korea, and the other depended on the data presented by the national forest stations. The writer wants to propose as follows (seemed to be helpful in improvement of Korean national forest management). 1) In the organization of national forest management, more national forest stations should be established to manage intensively, and the staff of working plan officials should be strengthened because of the importance of working plan. 2) By increasing the staff of protection officials, forest area assigned for each protection official should be decreased to 1,000-2,000 ha. 3) The frequent personnel changes of supervisor of national forest station(the responsible person on-the-spot) obstructs to accomplish the consistent management plan. 4) In the working plan drafting for national forest, basic investigations should be carefully practiced with sufficient expenditure and staff not to draft unreal working plan. 5) The area of working-unit should be decreased to less than 2,000 ha on the average for intensive management and the principle of a working-unit in a forest station should be realized as soon as possible. 6) Reforestation on open land should be completed in a short time with a debt of the special fund(a long term loan), and the land on which growing hardwood stands should be changed with conifers to increase productivity per unit area, and at the same time techical utilization method of hardwood should be developed. 7) Expenses of reforestation should be saved by mechanization and use of chemicals for reforestation and tree nursery operation providing against the lack of labour in future. 8) In forest protection, forest fire damage is enormous in comparison with foreign countries, accordingly prevention system and equipment should be improved, and also the minimum necessary budget should be counted up for establishment and manintenance of fire-lines. 9) Manufacture production should be enlarged to systematize protection, processing and circulation of forest business, and, by doing this, mich benefit is naturally given for rural people. 10) Establishment and arrangement of forest road networks and erosion control work are indispensable for the future development of national forest itself and local development. Therefore, these works should be promoted by the responsibility of general accounting instead of special accounting. 11) Mechanization of forest works should be realized for exploiting hinterlands to meet the demand for timber increased and for solving lack of labour, consequently it should promote import of forest machines, home production, training for operaters and careful adminitration. 12) Situation of labour in future will grow worse. Therefore, the countermeasure to maintain forest labourers and pay attention to public welfare facilities and works should be considered. 13) Although the condition of income and expenditure grows worse because of economical change, the regular expenditure should be fixed. So part of the surplus fund, as of the end of 1971, should be established for the fund, and used for enlarging reforestation and forest road networks(preceding investment in national forest).

• Korean small business review
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• v.31 no.4
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• pp.67-93
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• 2009

The purpose of this paper is to explore the relations between financial constraints and dividend smoothing of innovative small and medium sized enterprises(SMEs) listed on Korea Securities Market and Kosdaq Market of Korea Exchange. The innovative SMEs is defined as the firms with high level of R&D intensity which is measured by (R&D investment/total sales) ratio, according to Chauvin and Hirschey (1993). The R&D investment plays an important role as the innovative driver that can increase the future growth opportunity and profitability of the firms. Therefore, the R&D investment have large, positive, and consistent influences on the market value of the firm. In this point of view, we expect that the innovative SMEs can adjust dividend payment faster than the noninnovative SMEs, on the ground of their future growth opportunity and profitability. And also, we expect that the financial unconstrained firms can adjust dividend payment faster than the financial constrained firms, on the ground of their financing ability of investment funds through the market accessibility. Aivazian et al.(2006) exert that the financial unconstrained firms with the high accessibility to capital market can adjust dividend payment faster than the financial constrained firms. We collect the sample firms among the total SMEs listed on Korea Securities Market and Kosdaq Market of Korea Exchange during the periods from January 1999 to December 2007 from the KIS Value Library database. The total number of firm-year observations of the total sample firms throughout the entire period is 5,544, the number of firm-year observations of the dividend firms is 2,919, and the number of firm-year observations of the non-dividend firms is 2,625. About 53%(or 2,919) of these total 5,544 observations involve firms that make a dividend payment. The dividend firms are divided into two groups according to the R&D intensity, such as the innovative SMEs with larger than median of R&D intensity and the noninnovative SMEs with smaller than median of R&D intensity. The number of firm-year observations of the innovative SMEs is 1,506, and the number of firm-year observations of the noninnovative SMEs is 1,413. Furthermore, the innovative SMEs are divided into two groups according to level of financial constraints, such as the financial unconstrained firms and the financial constrained firms. The number of firm-year observations of the former is 894, and the number of firm-year observations of the latter is 612. Although all available firm-year observations of the dividend firms are collected, deletions are made in the case of financial industries such as banks, securities company, insurance company, and other financial services company, because their capital structure and business style are widely different from the general manufacturing firms. The stock repurchase was involved in dividend payment because Grullon and Michaely (2002) examined the substitution hypothesis between dividends and stock repurchases. However, our data structure is an unbalanced panel data since there is no requirement that the firm-year observations data are all available for each firms during the entire periods from January 1999 to December 2007 from the KIS Value Library database. We firstly estimate the classic Lintner(1956) dividend adjustment model, where the decision to smooth dividend or to adopt a residual dividend policy depends on financial constraints measured by market accessibility. Lintner model indicates that firms maintain stable and long run target payout ratio, and that firms adjust partially the gap between current payout rato and target payout ratio each year. In the Lintner model, dependent variable is the current dividend per share(DPS_t), and independent variables are the past dividend per share(DPS_t-1) and the current earnings per share(EPS_t). We hypothesized that firms adjust partially the gap between the current dividend per share(DPS_t) and the target payout ratio(Ω) each year, when the past dividend per share(DPS_t-1) deviate from the target payout ratio(Ω). We secondly estimate the expansion model that extend the Lintner model by including the determinants suggested by the major theories of dividend, namely, residual dividend theory, dividend signaling theory, agency theory, catering theory, and transactions cost theory. In the expansion model, dependent variable is the current dividend per share(DPS_t), explanatory variables are the past dividend per share(DPS_t-1) and the current earnings per share(EPS_t), and control variables are the current capital expenditure ratio(CEA_t), the current leverage ratio(LEV_t), the current operating return on assets(ROA_t), the current business risk(RISK_t), the current trading volume turnover ratio(TURN_t), and the current dividend premium(DPREM_t). In these control variables, CEA_t, LEV_t, and ROA_t are the determinants suggested by the residual dividend theory and the agency theory, ROA_t and RISK_t are the determinants suggested by the dividend signaling theory, TURN_t is the determinant suggested by the transactions cost theory, and DPREM_t is the determinant suggested by the catering theory. Furthermore, we thirdly estimate the Lintner model and the expansion model by using the panel data of the financial unconstrained firms and the financial constrained firms, that are divided into two groups according to level of financial constraints. We expect that the financial unconstrained firms can adjust dividend payment faster than the financial constrained firms, because the former can finance more easily the investment funds through the market accessibility than the latter. We analyzed descriptive statistics such as mean, standard deviation, and median to delete the outliers from the panel data, conducted one way analysis of variance to check up the industry-specfic effects, and conducted difference test of firms characteristic variables between innovative SMEs and noninnovative SMEs as well as difference test of firms characteristic variables between financial unconstrained firms and financial constrained firms. We also conducted the correlation analysis and the variance inflation factors analysis to detect any multicollinearity among the independent variables. Both of the correlation coefficients and the variance inflation factors are roughly low to the extent that may be ignored the multicollinearity among the independent variables. Furthermore, we estimate both of the Lintner model and the expansion model using the panel regression analysis. We firstly test the time-specific effects and the firm-specific effects may be involved in our panel data through the Lagrange multiplier test that was proposed by Breusch and Pagan(1980), and secondly conduct Hausman test to prove that fixed effect model is fitter with our panel data than the random effect model. The main results of this study can be summarized as follows. The determinants suggested by the major theories of dividend, namely, residual dividend theory, dividend signaling theory, agency theory, catering theory, and transactions cost theory explain significantly the dividend policy of the innovative SMEs. Lintner model indicates that firms maintain stable and long run target payout ratio, and that firms adjust partially the gap between the current payout ratio and the target payout ratio each year. In the core variables of Lintner model, the past dividend per share has more effects to dividend smoothing than the current earnings per share. These results suggest that the innovative SMEs maintain stable and long run dividend policy which sustains the past dividend per share level without corporate special reasons. The main results show that dividend adjustment speed of the innovative SMEs is faster than that of the noninnovative SMEs. This means that the innovative SMEs with high level of R&D intensity can adjust dividend payment faster than the noninnovative SMEs, on the ground of their future growth opportunity and profitability. The other main results show that dividend adjustment speed of the financial unconstrained SMEs is faster than that of the financial constrained SMEs. This means that the financial unconstrained firms with high accessibility to capital market can adjust dividend payment faster than the financial constrained firms, on the ground of their financing ability of investment funds through the market accessibility. Futhermore, the other additional results show that dividend adjustment speed of the innovative SMEs classified by the Small and Medium Business Administration is faster than that of the unclassified SMEs. They are linked with various financial policies and services such as credit guaranteed service, policy fund for SMEs, venture investment fund, insurance program, and so on. In conclusion, the past dividend per share and the current earnings per share suggested by the Lintner model explain mainly dividend adjustment speed of the innovative SMEs, and also the financial constraints explain partially. Therefore, if managers can properly understand of the relations between financial constraints and dividend smoothing of innovative SMEs, they can maintain stable and long run dividend policy of the innovative SMEs through dividend smoothing. These are encouraging results for Korea government, that is, the Small and Medium Business Administration as it has implemented many policies to commit to the innovative SMEs. This paper may have a few limitations because it may be only early study about the relations between financial constraints and dividend smoothing of the innovative SMEs. Specifically, this paper may not adequately capture all of the subtle features of the innovative SMEs and the financial unconstrained SMEs. Therefore, we think that it is necessary to expand sample firms and control variables, and use more elaborate analysis methods in the future studies.