Bankruptcy Forecasting Model using AdaBoost: A Focus on Construction Companies (적응형 부스팅을 이용한 파산 예측 모형: 건설업을 중심으로)
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- 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.
Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.
As relationship between buyer and seller has been brought closer and long-term relationship has been more important in B2B markets, the importance of service and service convenience increases as well as product. In homogeneous markets, where service offerings are similar and therefore not key competitive differentiator, providing greater convenience may enable a competitive advantage. Service convenience, as conceptualized by Berry et al. (2002), is defined as the consumers' time and effort perceptions related to buying or using a service. For this reason, B2B customers are interested in how fast the service is provided and how much save non-monetary cost like time or effort by the service convenience along with service quality. Therefore, this study attempts to investigate the impact of service convenience on relationship factors such as relationship satisfaction, relationship commitment, and relationship performance. The purpose of this study is to find out whether service convenience can be a new antecedent of relationship quality and relationship performance. In addition, this study tries to examine how five-dimensional service convenience constructs (decision convenience, access convenience, transaction convenience, benefit convenience, post-benefit convenience) affect customers' relationship satisfaction, relationship commitment, and relationship performance. The service convenience comprises five fundamental components - decision convenience (the perceived time and effort costs associated with service purchase or use decisions), access convenience(the perceived time and effort costs associated with initiating service delivery), transaction convenience(the perceived time and effort costs associated with finalizing the transaction), benefit convenience(the perceived time and effort costs associated with experiencing the core benefits of the offering) and post-benefit convenience (the perceived time and effort costs associated with reestablishing subsequent contact with the firm). Earlier studies of perceived service convenience in the industrial market are none. The conventional studies that have dealt with service convenience have usually been made in the consumer market, or they have dealt with convenience aspects in the service process. This service convenience measure for consumer market can be useful tool to estimate service quality in B2B market. The conceptualization developed by Berry et al. (2002) reflects a multistage, experiential consumption process in which evaluations of convenience vary at each stage. For this reason, the service convenience measure is good for B2B service environment which has complex processes and various types. Especially when categorizing B2B service as sequential stage of service delivery like Kumar and Kumar (2004), the Berry's service convenience measure which reflect sequential flow of service deliveries suitable to establish B2B service convenience. For this study, data were gathered from respondents who often buy business service and analyzed by structural equation modeling. The sample size in the present study is 119. Composite reliability values and average variance extracted values were examined for each variable to have reliability. We determine whether the measurement model supports the convergent validity by CFA, and discriminant validity was assessed by examining the correlation matrix of the constructs. For each pair of constructs, the square root of the average variance extracted exceeded their correlations, thus supporting the discriminant validity of the constructs. Hypotheses were tested using the Smart PLS 2.0 and we calculated the PLS path values and followed with a bootstrap re-sampling method to test the hypotheses. Among the five dimensional service convenience constructs, four constructs (decision convenience, transaction convenience, benefit convenience, post-benefit convenience) affected customers' positive relationship satisfaction, relationship commitment, and relationship performance. This result means that service convenience is important cue to improve relationship between buyer and seller. One of the five service convenience dimensions, access convenience, does not affect relationship quality and performance, which implies that the dimension of service convenience is not important factor of cumulative satisfaction. The Cumulative satisfaction can be distinguished from transaction-specific customer satisfaction, which is an immediate post-purchase evaluative judgment or an affective reaction to the most recent transactional experience with the firm. Because access convenience minimizes the physical effort associated with initiating an exchange, the effect on relationship satisfaction similar to cumulative satisfaction may be relatively low in terms of importance than transaction-specific customer satisfaction. Also, B2B firms focus on service quality, price, benefit, follow-up service and so on than convenience of time or place in service because it is relatively difficult to change existing transaction partners in B2B market compared to consumer market. In addition, this study using partial least squares methods reveals that customers' satisfaction and commitment toward relationship has mediating role between the service convenience and relationship performance. The result shows that management and investment to improve service convenience make customers' positive relationship satisfaction, and then the positive relationship satisfaction can enhance the relationship commitment and relationship performance. And to conclude, service convenience management is an important part of successful relationship performance management, and the service convenience is an important antecedent of relationship between buyer and seller such as the relationship commitment and relationship performance. Therefore, it has more important to improve relationship performance that service providers enhance service convenience although competitive service development or service quality improvement is important. Given the pressure to provide increased convenience, it is not surprising that organizations have made significant investments in enhancing the convenience aspect of their product and service offering.
Just before the Korean War, the total number of the North Korean troops was 198,380, while that of the ROK(Republic of Korea) army troops 105,752. That is, the total number of the ROK army troops at that time was 53.3% of the total number of the North Korean army. As of December 2008, the total number of the North Korean troops is estimated to be 1,190,000, while that of the ROK troops is 655,000, so the ROK army maintains 55.04% of the total number of the North Korean troops. If the ROK army continues to reduce its troops according to [Military Reform Plan 2020], the total number of its troops will be 517,000 m 2020. If North Korea maintains the current status(l,190,000 troops), the number of the ROK troops will be 43.4% of the North Korean army. In terms of units, just before the Korean War, the number of the ROK army divisions and regiments was 80% and 44.8% of North Korean army. As of December 2008, North Korea maintains 86 divisions and 69 regiments. Compared to the North Korean army, the ROK army maintains 46 Divisions (53.4% of North Korean army) and 15 regiments (21.3% of North Korean army). If the ROK army continue to reduce the military units according to [Military Reform Plan 2020], the number of ROK army divisions will be 28(13 Active Division, 4 Mobilization Divisions and 11 Local Reserve Divisions), while that of the North Korean army will be 86 in 2020. In that case, the number of divisions of the ROK army will be 32.5% of North Korean army. During the Korean war, North Korea suddenly invaded the Republic of Korea and occupied its capital 3 days after the war began. At that time, the ROK army maintained 80% of army divisions, compared to the North Korean army. The lesson to be learned from this is that, if the ROK army is forced to disperse its divisions because of the simultaneous invasion of North Korea and attack of guerrillas in home front areas, the Republic of Korea can be in a serious military danger, even though it maintains 80% of military divisions of North Korea. If the ROK army promotes the plans in [Military Reform Plan 2020], the number of military units of the ROK army will be 32.5% of that of the North Korean army. This ratio is 2.4 times lower than that of the time when the Korean war began, and in this case, 90% of total military power should be placed in the DMZ area. If 90% of military power is placed in the DMZ area, few troops will be left for the defense of home front. In addition, if the ROK army continues to reduce the troops, it can allow North Korea to have asymmetrical superiority in military force and it will eventually exert negative influence on the stability and peace of the Korean peninsular. On the other hand, it should be reminded that, during the Korean War, the Republic of Korea was attacked by North Korea, though it kept 53.3% of troops, compared to North Korea. It should also be reminded that, as of 2008, the ROK army is defending its territory with the troops 55.04% of North Korea. Moreover, the national defense is assisted by 25,120 troops of the US Forces in Korea. In case the total number of the ROK troops falls below 43.4% of the North Korean army, it may cause social unrest about the national security and may lead North Korea's misjudgement. Besides, according to Lanchester strategy, the party with weaker military power (60% compared to the party with stronger military power) has the 4.1% of winning possibility. Therefore, if we consider the fact that the total number of the ROK army troops is 55.04% of that of the North Korean army, the winning possibility of the ROK army is not higher than 4.1%. If the total number of ROK troops is reduced to 43.4% of that of North Korea, the winning possibility will be lower and the military operations will be in critically difficult situation. [Military Reform Plan 2020] rums at the reduction of troops and units of the ground forces under the policy of 'select few'. However, the problem is that the financial support to achieve this goal is not secured. Therefore, the promotion of [Military Reform Plan 2020] may cause the weakening of military defence power in 2020. Some advanced countries such as Japan, UK, Germany, and France have promoted the policy of 'select few'. However, what is to be noted is that the national security situation of those countries is much different from that of Korea. With the collapse of the Soviet Unions and European communist countries, the military threat of those European advanced countries has almost disappeared. In addition, the threats those advanced countries are facing are not wars in national level, but terrorism in international level. To cope with the threats like terrorism, large scaled army trops would not be necessary. So those advanced European countries can promote the policy of 'select few'. In line with this, those European countries put their focuses on the development of military sections that deal with non-military operations and protection from unspecified enemies. That is, those countries are promoting the policy of 'select few', because they found that the policy is suitable for their national security environment. Moreover, since they are pursuing common interest under the European Union(EU) and they can form an allied force under NATO, it is natural that they are pursing the 'select few' policy. At present, NATO maintains the larger number of troops(2,446,000) than Russia(l,027,000) to prepare for the potential threat of Russia. The situation of japan is also much different from that of Korea. As a country composed of islands, its prime military focus is put on the maritime defense. Accordingly, the development of ground force is given secondary focus. The japanese government promotes the policy to develop technology-concentrated small size navy and air-forces, instead of maintaining large-scaled ground force. In addition, because of the 'Peace Constitution' that was enacted just after the end of World War II, japan cannot maintain troops more than 240,000. With the limited number of troops (240,000), japan has no choice but to promote the policy of 'select few'. However, the situation of Korea is much different from the situations of those countries. The Republic of Korea is facing the threat of the North Korean Army that aims at keeping a large-scale military force. In addition, the countries surrounding Korea are also super powers containing strong military forces. Therefore, to cope with the actual threat of present and unspecified threat of future, the importance of maintaining a carefully calculated large-scale military force cannot be denied. Furthermore, when considering the fact that Korea is in a peninsular, the Republic of Korea must take it into consideration the tradition of continental countries' to maintain large-scale military powers. Since the Korean War, the ROK army has developed the technology-force combined military system, maintaining proper number of troops and units and pursuing 'select few' policy at the same time. This has been promoted with the consideration of military situation in the Koran peninsular and the cooperation of ROK-US combined forces. This kind of unique military system that cannot be found in other countries can be said to be an insightful one for the preparation for the actual threat of North Korea and the conflicts between continental countries and maritime countries. In addition, this kind of technology-force combined military system has enabled us to keep peace in Korea. Therefore, it would be desirable to maintain this technology-force combined military system until the reunification of the Korean peninsular. Furthermore, it is to be pointed out that blindly following the 'select few' policy of advanced countries is not a good option, because it is ignoring the military strategic situation of the Korean peninsular. If the Republic of Korea pursues the reduction of troops and units radically without consideration of the threat of North Korea and surrounding countries, it could be a significant strategic mistake. In addition, the ROK army should keep an eye on the fact the European advanced countries and Japan that are not facing direct military threats are spending more defense expenditures than Korea. If the ROK army reduces military power without proper alternatives, it would exert a negative effect on the stable economic development of Korea and peaceful reunification of the Korean peninsular. Therefore, the desirable option would be to focus on the development of quality of forces, maintaining proper size and number of troops and units under the technology-force combined military system. The tableau above shows that the advanced countries like the UK, Germany, Italy, and Austria spend more defense expenditure per person than the Republic of Korea, although they do not face actual military threats, and that they keep achieving better economic progress than the countries that spend less defense expenditure. Therefore, it would be necessary to adopt the merits of the defense systems of those advanced countries. As we have examined, it would be desirable to maintain the current size and number of troops and units, to promote 'select few' policy with increased defense expenditure, and to strengthen the technology-force combined military system. On the basis of firm national security, the Republic of Korea can develop efficient policies for reunification and prosperity, and jump into the status of advanced countries. Therefore, the plans to reduce troops and units in [Military Reform Plan 2020] should be reexamined. If it is difficult for the ROK army to maintain its size of 655,000 troops because of low birth rate, the plans to establish the prompt mobilization force or to adopt drafting system should be considered for the maintenance of proper number of troops and units. From now on, the Republic of Korean government should develop plans to keep peace as well as to prepare unexpected changes in the Korean peninsular. For the achievement of these missions, some options can be considered. The first one is to maintain the same size of military troops and units as North Korea. The second one is to maintain the same level of military power as North Korea in terms of military force index. The third one is to maintain the same level of military power as North Korea, with the combination of the prompt mobilization force and the troops in active service under the system of technology-force combined military system. At present, it would be not possible for the ROK army to maintain such a large-size military force as North Korea (1,190,000 troops and 86 units). So it would be rational to maintain almost the same level of military force as North Korea with the combination of the troops on the active list and the prompt mobilization forces. In other words, with the combination of the troops in active service (60%) and the prompt mobilization force (40%), the ROK army should develop the strategies to harmonize technology and forces. The Korean government should also be prepared for the strategic flexibility of USFK, the possibility of American policy change about the location of foreign army, radical unexpected changes in North Korea, the emergence of potential threat, surrounding countries' demand for Korean force for the maintenance of regional stability, and demand for international cooperation against terrorism. For this, it is necessary to develop new approaches toward the proper number and size of troops and units. For instance, to prepare for radical unexpected political or military changes in North Korea, the Republic of Korea should have plans to protect a large number of refugees, to control arms and people, to maintain social security, and to keep orders in North Korea. From the experiences of other countries, it is estimated that 115,000 to 230,000 troops, plus ten thousands of police are required to stabilize the North Korean society, in the case radical unexpected military or political change happens in North Korea. In addition, if the Republic of Korea should perform the release of hostages, control of mass destruction weapons, and suppress the internal wars in North Korea, it should send 460,000 troops to North Korea. Moreover, if the Republic of Korea wants to stop the attack of North Korea and flow of refugees in DMZ area, at least 600,000 troops would be required. In sum, even if the ROK army maintains 600,000 troops, it may need additional 460,000 troops to prepare for unexpected radical changes in North Korea. For this, it is necessary to establish the prompt mobilization force whose size and number are almost the same as the troops in active service. In case the ROK army keeps 650,000 troops, the proper number of the prompt mobilization force would be 460,000 to 500,000.