• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Applied Biological Chemistry
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• v.10
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• pp.69-100
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• 1968

1. In order to investigate on the microflora and enzyme activity of mold wheat 'Nuruk' , the major source of microorganisms for the brewing of Takju (a Korean Sake), two samples of Nuruk, one prepared at the College of Agriculture, Chung Nam University (S) and the other perchased at a market (T), were taken for the study. The molds, aerobic bacteria, lactic acid bacteria, and yeasts were examined and counted. The yeasts were classified by the treatment with TTC (2, 3, 5 triphenyltetrazolium chloride) agar that yields a varied shade of color. The amylase and protease activities of Nuruk were measured. The results were as the followings. a) In the Nuruk S found were: Aspergillus oryzae group, $204{\times}10^5$; Black Aspergilli, $163{\times}10^5$; Rhizogus, $20{\times}10^5$; Penicillia, $134{\times}10^5$; Areobic bacteria, $9{\times}10^6-2{\times}10^7$; Lactic acid bacteria, $3{\times}10^4$ In the Nuruk T found were: Aspergillus oryzae group, $836{\times}10^5$; Black Aspergilli, $286{\times}10^5$; Rhizopus, $623{\times}10^5$; Penicillia, $264{\times}10^5$; Aerobic bacteria, $5{\times}10^6-9{\times}10^6$; Lactic acid bacteria, $3{\times}10^4$ b) Eighty to ninety percent of the aerobic bacteria in Nuruk S appeared to belong to Bacillus subtilis while about 70% of those in Nuruk T seemed to be spherical bacteria. In both Nuruks about 80% of lactic acid bacteria were observed as spherical ones. c) The population of yeasts in 1g. of Nuruk S was about $6{\times}10^5$, 56.5% of which were TTC pink yeasts, 16% of which were TTC red pink yeasts, 8% of which were TTC red yeasts, 19.5% of which were TTC white yeasts. In Nuruk T(1g) the number of yeasts accounted for $14{\times}10^4$ and constituted of 42% TTC pink. 21% TTC red pink 28% TTC red and 9% TTC white. d) The enzyme activity of 1g Nuruk S was: Liquefying type Amylase, $D^{40}/_{30},=256$ W.V. Saccharifying type Amylase, 43.32 A.U. Acid protease, 181 C.F.U. Alkaline protease, 240C.F.U. The enzyme activity of 1g Nuruk T was: Liquefying type Amylase $D^{40}/_{30},=32$ W.V. Saccharifying type amylase $^{30}34.92$ A.U. Acid protease, 138 C.F.U. Alkaline protease 31 C.F.U. 2. During the fermentation of 'Takju' employing the Nuruks S and T the microflora and enzyme activity throughout the brewing were observed in 12 hour intervals. TTC pink and red yeasts considered to be the major yeasts were isolated and cultured. The strains ($1{\times}10^6/ml$) were added to the mashes S and T in which pH was adjusted to 4.2 and the change of microflora was examined during the fermentation. The results were: a) The molds disappeared from each sample plot since 2 to 3 days after mashing while the population of aerobic bacteria was found to be $10{\times}10^7-35{\times}10^7/ml$ inS plots and $8.2{\times}10^7-12{\times}10^7$ in plots. Among them the coccus propagated substantially until some 30 hours elasped in the S and T plots treated with lactic acid but decreased abruptly thereafter. In the plots of SP. SR. TP. and TR the coccus had not appeared from the beginning while the bacillus showed up and down changes in number and diminished by 1/5-1/10 the original at the end stage. b) The lactic acid bacteria observed in the S plot were about $7.4{\times}10^7$ in number per ml of the mash in 24 hours and increased up to around $2{\times}10^8$ until 3-4 days since. After this period the population decreased rapidly and reached about $4{\times}10^5$ at the end, In the plot T the lactic acid becteria found were about $3{\times}10^8$ at the period of 24 fours, about $3{\times}10$ in 3 days and about $2{\times}10^5$ at the end in number. In the plots SP. SR. TP, and TR the lactic acid bacteria observed were as less as $4{\times}10^5$ at the stage of 24 hours and after this period the organisms either remained unchanged in population or ceased to exist. c) The maiority of lactic acid bacteria found in each mash were spherical and the change in number displayed a tendency in accordance with the amount of lactic acid and alcohol produced in the mash. d) The yeasts had showed a marked propagation since the period of 24 hours when the number was about $2{\times}10^8$ ㎖ mash in the plot S. $4{\times}10^8$ in 48 hours and $5-7{\times}10^8$ in the end period were observed. In the plot T the number was $4{\times}10^8$ in 24 hours and thereafter changed up and down maintaining $2-5{\times}10^8$ in the range. e) Over 90% of the yeasts found in the mashes of S and T plots were TTC pink type while both TTC red pink and TTC red types held range of $2{\times}10-3{\times}10^7$ throughout the entire fermentation. f) The population of TTC pink yeasts in the plot SP was as $5{\times}10^8$ much as that is, twice of that of S plot at the period of 24 hours. The predominance in number continued until the middle and later stages but the order of number became about the same at the end. g) Total number of the yeasts observed in the plot SR showed little difference from that of the plot SP. The TTC red yeasts added appeared considerably in the early stage but days after the change in number was about the same as that of the plot S. In the plot TR the population of TTC red yeasts was predominant over the T plot in the early stage which there was no difference between two plots there after. For this reason even in the plot w hers TTC red yeasts were added TTC pink yeasts were predominant. TTC red yeasts observed in the present experiment showed continuing growth until the later stage but the rate was low. h) In the plot TP TTC pink yeasts were found to be about $5{\times}10^8$ in number at the period of 2 days and inclined to decrease thereafter. Compared with the plot T the number of TTC pink yeasts in the plot TP was predominant until the middle stage but became at the later stage. i) The productivity of alcohol in the mash was measured. The plot where TTC pink yeasts were added showed somewhat better yield in the earely stage but at and after the middle stage the difference between the yeast-added and the intact mashes was not recognizable. And the production of alcohol was not proportional to the total number of yeasts present. j) Activity of the liquefying amylase was the highest until 12 hours after mashing, somewhat lowered once after that, and again increased around 36-48 hours after mashing. Then the activity had decreased continuously. Activity of saccharifying amylase also decreased at the period of 24 hours and then increased until 48 hours when it reached the maximum. Since, the activity had gradually decreased until 72 hours and rapidly so did thereafter. k) Activity of alkaline protease during the fermentation of mash showed a tendency to decrease continusously although somewhat irregular. Activity of acid protease increased until hours at the maximum, then decreased rapidly, and again increased, the vigor of acid protease showed better shape than that of alkaline protease throughout. 3. TTC pink yeasts that were predominant in number, two strains of TTC red pink yeasts that appeared throughout the brewing, and TTC red yeasts were identified and the physiological characters examined. The results were as described below. a) TTC pinkyeasts (B-50P) and two strains of TTC red pink yeasts (B-54 RP & B-60 RP) w ere identified as the type of Saccharomyces cerevisiae and TTC pink red yeasts CB-53 R) were as the type of Hansenula subpelliculosa. b) The fermentability of four strains above mentioned were measured as follows. Two strains of TTC red pink yeasts were the highest, TTC pink yeasts were the lowest in the fermantability. The former three strains were active in the early stage of fermentation and found to be suitable for manufacturing 'Takju' TTC red yeasts were found to play an important role in Takju brewing due to its strong ability to produce esters although its fermentability was low. c) The tolerance against nitrous acid of strains of yeast was marked. That against lactic acid was only 3% in Koji extract, and TTC red yeasts showed somewhat stronger resistance. The tolerance against alcohol of TTC pink and red pink yeasts in the Hayduck solution was 7% while that in the malt extract was 13%. However, that of TTC red yeasts was much weaker than others. Liguefying activity of gelatin by those four strains of yeast was not recognized even in 40 days. 4. Fermentability during Takju brewing was shown in the first two days as much as 70-80% of total fermentation and around 90% of fermentation proceeded in 3-4 days. The main fermentation appeared to be completed during :his period. Productivity of alcohol during Takju brewing was found to be apporximately 65% of the total amount of starch put in mashing. 5. The reason that Saccharomyces coreanuss found be Saito in the mash of Takju was not detected in the present experiment is considered due to the facts that Aspergillus oryzae has been inoculated in the mold wheat (Nuruk) since around 1930 and also that Koji has been used in Takju brewing, consequently causing they complete change in microflora in the Takju brewing. This consideration will be supported by the fact that the original flavor and taste have now been remarkably changed.