Sanayi firmaları için mali tablo analizlerinden hareketle bir erken uyarı modelinin kurulması
Başlık çevirisi mevcut değil.
- Tez No: 46149
- Danışmanlar: PROF.DR. RAMAZAN EVREN
- Tez Türü: Yüksek Lisans
- Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 1995
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 77
Özet
ÖZET Çalışmanın amacı imalatçı firmaların mali tablolarından istifade ederek firmaların gelecekleriyle ilgili bir erken uyarı modelinin kurulmasıdır. Bu amaçla çalışmanın birinci bölümünde finansal oranlardan hareketle isletme başarısızlıklarının önceden tahminiyle ilgili yapılan çalışmalar anlatılmıştır. Bu bölümde Tek değişkenli ve Çok değişkenli istatistik yöntemleri kullanan çalışmalar hakkında bilgi verilmiştir. ikinci bölümde ise isletme başarısızlıklarının önceden tahmin edilmesiyle ilgili kullanılan teknik olan Diskriminant Analizi hakkında bilgi verilmiştir. Diskriminant bağıntısının oluşturulması, sınıflandırma sekli ve sınıflandırmanın güvenilirliğine ait testler üzerinde durulmuştur. Çalışmamızın üçüncü bölümü uygulama bölümüdür. Bu bölümde öncelikle Diskriminant analizinde kullanılacak başarılı ve başarısız firma grupları tesbit edilmiştir. Bu amaçla incelenen 1992, 1993 ve 1994 yıllarının en az birisinde zarar etmiş ve/veya net isletme sermayesi negatif gerçekleşmiş firmalar başarısız firmalar, incelenen üç yılın tamamında kâr eden ve isletme sermayesi pozitif gerçekleşmiş firmalar ise başarılı firmalar olarak tanımlanarak 26' sı başarılı, 11 'i başarısız olmak üzere 37 adet firma Diskriminant analizinde kullanılmak üzere seçilmiştir. Her bir firma için Diskriminant analizinde kullanılacak değişkenleri oluşturacak 40 adet anlamlı finansal oran hesaplanmıştır. 40 adet değişkenle Diskriminant analizi uygulamanın zorluğu sebebiyle oranlara“t istatistiği”uygulanmış ve başarılı ve başarısız firma grupları arasındaki farklılıkları maksimize eden 13 adet finansal oran Diskriminant analizinde kullanılmak üzere seçilmiştir. Diskriminant analizi neticesinde her bir oran için katsayı değerleri bulunmuştur. Oluşturulan diskriminant fonksiyonları ile her bir firma için skorlar elde edilmiştir. Bu skorlar hesaplanan kritik skor değerleri ile karşılaştırılmış ve istatistiki sınıflandırma ile tecrübi sınıflandırma arasındaki farklılıklar gözlemlenmiştir. Yapılan güvenilirlik testi ile istatistiki sınıflandırmanın daha güvenilir olduğu ortaya çıkmıştır. Dördüncü ve son bölümde çalışmamız genel hatlarıyla aktarılmış ve Diskriminant analizi ile kurmuş olduğumuz modelin kullanılması neticesinde imalatçı firmalar için yapılacak gelecek tahminlerinin tecrübi sınıflandırmadan daha objektif sonuçlar vereceği ortaya konmuştur.
Özet (Çeviri)
SUMMARY ESTABLISHMENT OF A PRE-ANNOIINCEMENT MODEL FOR INDUSTRIAL FIRMS/COMPANIES IN THE LIGHT OF FINANCIAL STATEMENT ANALYSIS In the first part of the analysis, we explained the One and Multy Variable Statisitcal Analysis methods in order to pre-estimate how successful 1 the companies in terms of their trading and manufacturing activities. One Variable Statistics Method“ OVSM ”was first developed by both Beaver's and Weibel's studies, Multy Variable Statistics Method“ MVSM ”by Tamari, Edward Altman, Meyer and Pifer, Daniel Martin and Sinkey's studies. In the second part of the analysis, we presented the One and Multy Variable Statistics Methods for predicting the possibilities of failure in future activities of the companies. In OVSM, the financial ratios of the financial statements of the companies are used in order to seperate the companies whether they are successful or unsuccessful. These OVSMs take the every ratio into account. Thus, the financial ratios, having the characteristics of seperating the groups of companies as successful or unsuccessful, are determined one by one. The reciprocal relations between the financial ratios determining the groups of companies according to their effectiveness criteria which can not be calculated by using the OVSM because this method can not compare and contrast the reciprocal relations between the financial ratios. In another words, the most distinctive financial ratios calculated by using the OVSM to determine the companies either they are successful or unsuccessful can not determine the seperation between these companies. Moreover the weight of OVSM can not be found out in the pre-determination of company failure. This inadequacy of OVSM could only be eliminated by using the MVSM. The MVSM takes into account the relationship between the variables and the reciprocal dependencies. In accordance with the MVSM the weight of variables in this model can be precisely valued and more rational and comprehensive information may be furnished. The Discriminant Analysis Method“DAM”one of the MVS methods, was used in this research. The DAM tries to determine the place of company within the pre-determined groups according to their success or failure based on an objective analysis. In this determination, the DAM uses the financial ratios of companies according to their year- end financial statements. The Discriminant function VIreached at the end of the financial analysis determines the relative importance of financial ratios used during these analysis. Therefore it becomes easy to make future estimations about these companies by means of financial ratios calculated in accordance with their relative importance in the pre-determined successful or unsuccessful groups of companies. In the third section of the analysis it is tried to develop a pre-announcement model to be utilized in predicting the inadequacy levels of the companies operating in manufacturing industry and for this purpose, the DAM was used. The analysis and its results are as follows. In this researche we studied on the 1992-93-94 year- end balance sheets and income statements of the companies operating in manufacturing industry. We choosed 37 firms according to their assets amounting to TL 100 and 250 billion in the year 1994. 26 of the 37 firms are the firms who realised certain amounts of profits and whose operating capital has positive value are classified as successful companies and other 11 firms are the firms who suffered loss at least in 1 of above mentioned three years and/or whose operating capital has negative value at least in two of last three years are accepted as unsuccessful compani es. After the pre-determined classification of the companies as successful and unsuccessful we calculated 40 financial ratios for each firm by an analysis programme prepared in LOTUS 123 programme taking into account the balance sheets and income statements of the companies. There are 9 financial ratios to compute the liquidity structure of the company, 13 ratios for operational analysis, 7 ratios for debt analysis, 4 ratios for cash flow tables and 7 ratios to calculate the profitability rate. In accordance with these financial ratios, we used the discriminant analysis method, one of MVS method, in order to establish a pre-announcement model for pre- estimating the possible inadequacies in future activies of companies. As it is hard to execute the Discriminant Analysis into such a too many financial ratios, we made a selection through these ratios to make a more comprehensive and accurate analysis. Therefore, we selected most trustworthy and reliable 13 ratios among these ratios by using the“t statistics method”, one of OVSM techniques. These 13 ratios are the most comprehensive and selective ratios to be able to seperate VIIthe companies -into successful or unsuccessful groups in a 95% reliable interval manner. These ratios are as follows; Ratio 2: (cash+bank+short terra receivables) /total assets Ratio 6: current assets/short term liabilities Ratio 9: (cash+bank+short term recei vebles+bonds+average monthly sales)/activity outcomes-depreciations Ratio 13 : (cash+bank+short term recei vabl es) /Net sales Ratio 23: Short-Term Liabilities/Total Assets Ratio 25: Total Liabilities/Total Assets Ratio 29: Long-Term Li abi 1 i ties/ (Long-Term Liabi li ties+Total Equity) Ratio 30: (Earning Before Tax+Depreciation)/Net Sales Ratio 31: (EBT+Depreciation) /Total Assets Ratio 32: (EBT+Depreciation) /Total Equity Ratio 33: (EBT+Depreci at ion) /Total Liabilities Ratio 38: EBT/Short-Term Liabilities Ratio 39: EBT/Total Fixed Assets In accordance with the above mentioned ratios a discriminant function was developed for each year by means of LOTUS 123, QPRO and EXCEL computer programmes, and then the discriminant function values was computed for each firm by analyzing their financial statements as of last three years. The most comprehensive and selective ratios in determining the categories of the companies according to their success or failure are given on the following. Ratio 2: (Cash+Banks+Short-Term Receivables) /Total Assets Ratio 13: (Cash+Banks+Short-Term Receivables) /Net Sales Ratio 23: Short-Term Liabilities/Total Assets Ratio 30: (EBT+Depreciation) /Net Sales Ratio 31: (EBT+Depreci at i on) /Total Assets VIIIRatio 32: (EBT-+Depreciat ion) /Total Equity According to the results of the understood that the ratio 2 is more liquidity analysis, the ratio 13 ratio 23 is in debt analysis, and more determinant in cash flow and the other financial ratios. analysis; it is determinant in is in current ratios, ratio 30, 31 and 32 are profitability rate than The discriminant function justified that; the 7 of pre-examined 26 successful firms in the years 1992 and 1993 and 4 firms in 1994 are following the trend of unsuccessful firms, and on the other hand, 2 of pre- examined and determined 11 unsuccessful companies in the year 1992, 1 in 1993 and 1 in 1994 are aiming to enter into the successful company trend. In another words, this analysis made it clear that there is an incorrect classification according to the results of pre-examined and determined categorization in accordance with the accrued profit/loss and net operating capital declared in the balance sheets of the successful companies. YEAR PERFORMANCE OF THE COMPANY DEVIATION (%) 1992 1993 1994 successful unsuccessful successful unsuccessful successful unsuccessful %27(7/26) %18(2/11) %27(7/26) %9(1/11) %15(4/26) %9(1/11) According to results of the discriminant function analysis the new and more accurate classf ication is presented below. YEAR 1992 1993 1994 PERFORMANCE OF THE COMPANY Successful Unsuccessful Successful Unsuccessful Successful Unsuccessful NUMBER OF FIRMS 21(19+2) 16(9+7) 20(19+1) 17(10+7) 23(22+1) 14(10+4) IXAs a result there are 13 financial ratios used in classification made by discriminant function and 6 of these ratios carry most determinant characteristics. These 6 ratios are (cash+banks+short term receivables) /Total Assets, (Cash+Banks+Short-Term Receivables) /Net Sales, Short-Term Liabilities/Total Assets, (EBT+Depreciation)/Net Sales, (EBT+Depreciation) /Total Assets, (EBT+Depreciation) /Total Equity. According to the values reached at the end of the analysis, the developed pre-announcement model will be helpful in forecasting the future activities of the companies. In another words, according to Discriminant analysis results, even the companies realized certain amounts of profits and their net operating capital has positive value, it would be better to evaluate those companies as unsuccessful companies within the framework of total financial ratio analysis and to act prudently about the future expectations of these companies. On the other hand, even these companies had suffered certain amounts of losses and/or their net operating capital were negative, it would be rational to evaluate those firms as successful by analyzing their financial ratios, and it would be wise to study the future expectations of those firms in a more tolerated way. Forecasting the future efficiency or inefficiency of the companies by using the discriminant functions will be very feasible for the credit suppliers and for the investors who will invest their money to the shares of these companies. Moreover, by means of the discriminant analysis, a model may be established of which will provide very rational data base to the credit extending institutions in the evaluation and review of the companies; to the managers in their internal controls and to the investors in their investment portfolio pref erances. It should be noted that in order to benefit from this kind of analysis and, to obtain the right judgement, the data in the financial statements should be precise, trustworthy and real figures. In abstract, The more accurate and healthy data in the financial statements; the more possibility to hit the right mark in the evaluatiation process of the companies as successful or unsuccessful.
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