Linear discriminant analysis (LDA) has found extensive application in predicting bankruptcy. In this article, we elucidate a novel modelling approach for LDA that can also aid in gaining useful insights regarding the relative importance and ranking of factors in the banking industry. The model steers away from the traditional computation of the variance/covariance matrix and employs an ensemble technique to assign records to classes. The efficacy of our model is tested using two datasets. Specifically, a large dataset from the banking industry was partitioned into the testing and training datasets, and an accuracy of 87.9% was achieved
AlbrightS. C., & WinstonW. L. (2009). Management Science Modeling, International Student Edition. South-Western.
2.
AltmanE. I. (1968). Discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.
3.
AltmanE. I., HaldemanR. G., & NarayananP. (1977). ZETA analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, 29–54.
4.
AltmanE. I. (1983). Corporate financial distress: A complete guide to predicting, avoiding and dealing with bankruptcy. Wiley.
5.
AltmanE. I. (1984). The success of business failure prediction models: An international survey. Journal of Banking and Finance, 8(2), 171–198.
6.
AltmanE. I. (1993). Corporate financial distress and bankruptcy: A complete guide to predicting & avoiding distress and profiting from bankruptcy (2nd ed.). Wiley.
7.
BunyaminuA., & IssahM. (2012). Predicting corporate failure of UK’s listed companies: Comparing multiple discriminant analysis and logistic regression. International Research Journal of Finance and Economics, 94, 6–22.
8.
CardwellP. M., McGregorC. C., & SynnW. J. (2003). Bankruptcy prediction in the textile industry. International Business & Economics Research Journal, 2(8), 31–40.
CramerJ. S. (2004). The early origins of the logit model. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Scienes, 35(4), 613–626.
11.
DavidD. E., LynneA. M., HanJ., & FoleyS. L. (2010). Evaluation of virulence factor profiling in the characterization of veterinary escherichia coli isolates. Applied and Environmental Microbiology, 76(22), 7509–7513.
12.
FardP. A., ShakoorjavanS., & AkbariS. (2018). The relationship between odour intensity and antibacterial durability of encapsulated thyme essential oil by PPI dendrimer on cotton fabrics. The Journal of the Textile Institute, 109(6), 832–841.
13.
FisherR. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
14.
GajaH., & LiouF. (2018). Defect classification of laser metal deposition using logistic regression and artificial neural networks for pattern recognition. The International Journal of Advanced Manufacturing Technology, 94(1–4), 315–326.
15.
GisselJ. L., GiacominoD., & AkersM. D. (2007). A review of bankruptcy prediction studies: 1930-present. Journal of Financial Education, 33, 1–42.
16.
HastieT., TibshiraniR., & FriedmanJ. (2008). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
17.
HotellingH. (1931). The generalization of Student’s ratio. Annals of Mathematical Statistics, 2(3), 360–378.
18.
HuseinM. F., & PambektiG. T. (2014). Precision of models of Altman, Springate, Zmijewski and Grover for predicting the financial distress. Journal of Economics, Business and Accountancy Ventura, 17(3), 405–416.
19.
JayadevanR., KolheS. R., PatilP. M., & PalU. (2011). Offline recognition of Devanagari script: A survey. IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews, 41(6), 782–796.
20.
JohnsonC. Y., HowardsP. P., StricklandM. J., WallerD. K., & FlandersW. D. (2018). Multiple bias analysis using logistic regression: An example from the national birth defects prevention study. Annals of Epidemiology, 28(8), 510–514.
21.
KingL. J. (1970). Discriminant analysis: A review of recent theoretical contributions and applications. Economic Geography, 46, 367–378.
22.
KiyakD., & LabanauskaiteD. (2012). Assessment of the practical application of corporate bankruptcy prediction models. Economics and Management, 17(3), 895–906.
23.
LiY., WangG., NieL., WangQ., & TanW. (2018). Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recognition, 75, 51–62.
24.
LiuQ., LuH., & MaS. (2004). Improving kernel Fisher discriminant analysis for face recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 42–49.
25.
MahalanobisP. C. (1936). On the generalized distance in statistics. Proceedings of the National Institute of Sciences in India, 2(1), 49–55.
26.
MarocoJ., SilvaD., RodriguesA., GuerreiroM., SantanaI., & de MendocaA. (2011). Data mining methods in the prediction of dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Research Notes, 4, 299.
27.
MohammedS. (2016). Bankruptcy prediction by using the Altman Z-score model in Oman: A case study of Raysut cement company SAOG and its subsidiaries. Australasian Accounting, Business and Finance Journal, 10(4), 70–80.
28.
MohammedN. N., KhaleelM. I., LatifM., & KhalidZ. (2018). Face recognition based on PCA with weighted and normalized Mahalanobis distance. 2018 International Conference on Intelligent Informatics and Biomedical, Sciences (ICIIBMS), Bangkok, Thailand.
29.
MoraisC. L. M., & LimaK. M. G. (2018). Principal component analysis with linear and quadratic discriminant analysis for identification of cancer samples based on mass spectrometry. Journal of the Brazilian Chemical Society, 29(3), 472–481.
30.
MorrisonD. F. (1976). Multivariate statistical analysis (2nd ed.). McGraw Hill.
31.
NunthapadP. (2000). The application of Altman’s and McGurr’s banktrptcy models to small retail firms: A comparative analysis (Dissertation). Nova Southeastern University, Proquest Dissertation Publishing.
32.
PantaloneC., & PlattM. B. (1987). Predicting failure of savings and loan associations. AREUEA, 15(2), 46–64.
33.
PeetsP., LeitoI., PeltJ., & VahurS. (2017). Identification and classification of textile fabric using ATR-FT-IR-spectroscopy with chemometric methods. Spectrochemica Acta Part A: Molecular and Biomolecular Specroscopy, 173, 175–181.
34.
PoharM., BlasM., & TurkS. (2004). Comparison of logistic regression and linear discriminant analysis: A simulation study. Metodoloski Zvezki, 1(1), 143–161.
35.
PreisnerO., GuiomarR., MachadoJ., MenezesJ. C., & LopesJ. A. (2010). Application of Fourier transform infrared spectroscopy and chemometrics for differentiation of salmonella enterica serovar enteridis phage types. Applied and Environmental Microbiology, 76(11), 3538–3544.
36.
RagsdaleC. T., & StamA. (1992). Introducing discriminant analysis to the business statistics curriculum. Decision Sciences, 23(3), 724–745.
37.
RaoC. R. (1948). The utilization of multiple measurements in problems of biological classification. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 159–203.
38.
ReadC. (2016). The early life of Ronald Aylmer Fisher. In ReadC. (Ed.), The econometricians. Great minds in finance (pp. 111–120). Palgrave Macmillan.
39.
RosenblattF. (1962). Principles of neurodynamics. Spartan Books.
40.
RumelhartD. E., HintonG. E., & WilliamsR. J. (1986). Learning internal representations by backpropagating errors. Nature, 323, 533–536.
41.
RumelhartD. E., HintonG. E., & WilliamsR. J. (1987). Learning internal representations by error propagation. Parallel Distributed Processing, 1, 318–362.
42.
SamarakoonL. P., & HasanT. (2003). Altman’s Z-score models of predicting corporate distress: Evidence from the emerging Sri Lankan stock market. Journal of the Academy of Finance, 1, 119–1258.
43.
SiddiqiM. H., AliR., KhanA. M., ParkY.-T., & LeeS. (2015). Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. IEEE Transactions on Image Processing, 24(4), 1386–1398.
44.
SpringateG. L. V. (1978). Predicting the possibility of failure in a Canadian firm. MBA Research Project, Simon Fraser University.
45.
UnsalM. G., & NazmanE. (2018). Investigating socio-economic ranking of cities in Turkey using data envelopment analysis (DEA) and linear discriminant analysis (LDA). Annals of Operations Research, 294, 281–295.
46.
VenkataRamanaN., AzashS. M., & RamakrishnaiahK. (2012). Financial performance and predicting risk of bankruptcy: A case of selected cement companies in India. International Journal of Public Administration and Management Research, 1(1), 40–56.
47.
VenkatesanC., KarthigaikumarP., PaulA., SatheeskumaranS., & KumarR. (2018). ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications. IEEE Access, 6, 9764–9773.
48.
VilaN., & KusterI. (2007). The importance of innovation in international textile firms. European Journal of Marketing, 41(1/2), 17–36.
49.
ViswanathanP. K., SrinivasanS., & HariharanN. (2020). Predicting financial health of banks for investor guidance using machine learning algorithms. Journal of Emerging Finance, 19(2), 226–261.
50.
WangH., ZhengB., YoonS. W., & KoH. S. (2018). A support vector machine-based ensemble algorithm for breast cancer diagnosis. European Journal of Operational Research, 267(2), 687–699.
51.
ZeinaD. A., & Al-AnziF. S. (2018). Employing Fisher discriminant analysis for Arabic text classification. Computers & Electrical Engineering, 66, 474–486.
