The Pattern Analysis of Financial Distress for Non-audited Firms using Data Mining (데이터마이닝 기법을 활용한 비외감기업의 부실화 유형 분석)
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- Journal of Intelligence and Information Systems
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- v.21 no.4
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- pp.111-131
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- 2015
There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.
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.
As a results of researches on the cultivation processes and settlement developments on the Mangyoung river valley as a whole could be have four 'Space-Time Continuity' through a [Origin-Destination] theory model. On a initial phases of cultivation, the cultivation process has been begun at mountain slopes and tributory plains in upper part of river-basin from Koryo Dynasty to early Chosun Dynasty. At first, indigenous peasants burned forests on the mountain slopes for making 'dryfield' for a cereal crops. Following population increase more stable food supply is necessary facets of life inducing a change production method into a 'wetfield' in tributory plains matching the population increase. First sedentary agriculture maybe initiated at this mountain slopes and tributory plains on upper part of river basin through a burning cultivation methods. Mountain slopes and tributory plains are become a Origin area in cultivation processes. It expanded from up to down through the valleys with 'a bits of land' fashion in a steady pace like a terraced fields expanded with bit by bit of land to downward. They expanded their land to the middle part of river basin in mid period of Chosun Dynasty with dike construction techniques on the river bank. Lower part of river cultivated with embankment building techniques in 1920s and then naturally expanded to the tidal marshes on the estuaries and river inlets of coastal areas. 'Pioneer fringes' are consolidated at there in modern times. Changes in landscapes are appeared it's own characters with each periods of time. Followings are results of study through the Mangyoung river valley as a whole. (1) Mountain slopes and tributory plains on the upper part of river are cultivated 'dryfields' by indigenous peasants with Burning cultivation methods at first and developed sedentary settlements at the edges of mountain slopes and on the river terrace near the fields. They formed a kind of 'periphery-located cluster type' of settlement. This type of settlement are become a prominant type in upper part of river basin. 'Dryfields' has been changed into a 'wetfields' at the narrow tributory plains by increasing population pressure in later time. These wetfields are supplied water by Weir and Ponds Irrigation System(제언수리방법). Streams on the tributory plains has been attracted wetfields besides of it and formed a [water+land] complex on it. 'Wetfields' are expanded from up to downward with a terraced land pattern(adder like pattern, 붕전) according to the gradient of valley. These periphery located settlements are formed a intimate ecological linkage with several sets of surroundings. Inner villages are expanded to Outer villages according to the expansion of arable lands into downward. (2) Mountain slopes and tributory plains expanded its territory to the alluvial deposited plains on the middle part of river valley with a urgent need of new land by population increase. This part of alluvial plains are cultivated mainly in mid period of Chosun Dynasty. Irrigation methods are changed into a Dike Construction Irrigation method(천방수리방법) for the control of floods. It has a trend to change the subjectives of cultivation from community-oriented one who constructed Bochang along tributories making rice paddies to local government authorities who could be gather large sums of capitals, techniques and labours for the big dike construction affairs. Settlements are advanced in the midst of plains avoiding friction of distances and formed a 'Centrallocated cluster type' of settlements. There occured a hierarchical structures of settlements in ranks and sizes according merits of water supply and transportation convenience at the broad plains. Big towns are developed at there. It strengthened a more prominant [water+land] complex along the canals. Ecological linkages between settlements and surroundings are shaded out into a tiny one in this area. (3) It is very necessary to get a modern technology of flood control at the rivers that have a large volume of water and broad width. The alluvial plains are remained in a wilderness phase until a technical level reached a large artificial levee construction ability that could protect the arable land from flood. Until that time on most of alluvial land at the lower part of river are remained a wilderness of overgrown with reeds in lacks of techniques to build a large-scale artificial levee along the riverbank. Cultivation processes are progressed in a large scale one by Japanese agricultural companies with [River Rennovation Project] of central government in 1920s. Large scale artificial levees are constructed along the riverbank. Subjectives of cultivation are changed from Korean peasants to Japanese agricultural companies and Korean peasants fell down as a tenant in a colonial situation of that time in Korea. They could not have any voices in planning of spatial structure and decreased their role in planning. Newly cultivated lands are reflected company's intensions, objectives and perspectives for achieving their goals for the sake of colonial power. Newly cultivated lands are planned into a regular Rectangular Block settings of rice paddies and implanted a large scale Bureaucratic-oriented Irrigation System on the cultivated plains. Every settlements are located in the midst of rice paddies with a Central located Cluster type of settlements. [water+land] complex along the canal system are more strengthened. Cultivated space has a characters of [I-IT] landscapes. (4) Artificial levees are connected into a coastal emnankment for a reclamation of broad tidal marshes on the estuaries and inlets of rivers in the colonial times. Subjectives of reclamation are enlarged into a big agricultural companies that could be acted a role as a big cultivator. After that time on most of reclamation project of tidal marshes are controlled by these agricultural companies formed by mostly Japanese capitalists. Reclaimed lands on the estuaries and river inlets are under hands of agricultural companies and all the spatial structures are formed by their intensions, objectives and perspectives. They constructed a Unit Farming Area for the sake of companies. Spatial structures are planned in a regular one with broad arable land for the rice production of rectangular blocks, regular canal systems and tank reservoir for the irrigation water supply into reclaimed lands. There developed a 'Central-located linear type' of settlements in midst of reclaimed land. These settlements are settled in a detail program upon this newly reclaimed land at once with a master plan and they have planned patterns in their distribution, building materials, location, and form. Ecological linkage between Newly settled settlemrnts and its surroundings are lost its colours and became a more artificial one by human-centred environment. [I-IT] landscapes are become more prominant. This region is a destination area of [Origin-Destination] theory model and formed a 'Pioneer Fringe'. It is a kind of pioneer front that could advance or retreat discontinously by physical conditions and socio-cultural conditions of that region.