پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 61 |
| تعداد شمارهها | 2,226 |
| تعداد مقالات | 18,207 |
| تعداد مشاهده مقاله | 55,992,847 |
| تعداد دریافت فایل اصل مقاله | 29,031,943 |
Spectral graph embedding for dimension reduction in financial risk assessment | ||
| Journal of Mathematics and Modeling in Finance | ||
| دوره 1، شماره 2، اسفند 2021، صفحه 63-78 اصل مقاله (316.87 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2021.13841 | ||
| نویسندگان | ||
| Hossein Teimoori Faal* ؛ Meyssam Bagheri | ||
| Department of Mathematics and Computer Science, Allameh Tabataba'i University, Tehran, Iran | ||
| چکیده | ||
| The economic downturn in recent years has had a significant negative impact on corporates performance. In the last two years, as in the last years of 2010s, many companies have been influenced by the economic conditions and some have gone bankrupt. This has led to an increase in companies' financial risk. One of the significant branches of financial risk is the emph{company's credit risk}. Lenders and investors attach great importance to determining a company's credit risk when granting a credit facility. Credit risk means the possibility of default on repayment of facilities received by a company. There are various models for assessing credit risk using statistical models or machine learning. In this paper, we will investigate the machine learning task of the binary classification of firms into bankrupt and healthy based on the emph{spectral graph theory}. We first construct an emph{adjacency graph} from a list of firms with their corresponding emph{feature vectors}. Next, we first embed this graph into a one-dimensional Euclidean space and then into a two dimensional Euclidean space to obtain two lower-dimensional representations of the original data points. Finally, we apply the emph{support vector machine} and the emph{multi-layer perceptron} neural network techniques to proceed binary emph{node classification}. The results of the proposed method on the given dataset (selected firms of Tehran stock exchange market) show a comparative advantage over PCA method of emph{dimension reduction}. Finally, we conclude the paper with some discussions on further research directions. | ||
| کلیدواژهها | ||
| Spectral graph embedding؛ Principle component analysis؛ Financial risk assessment؛ Affinity matrix؛ Bankruptcy | ||
| مراجع | ||
|
[1] B. Ribeiro, N. Chen, A. Chen, Financial credit risk assessment: a recent review, Arti cial Intelligence Review, 45 (2016), 1-23. [2] K.Pearson, On lines and planes of closest t to systems of points in space, Philos. Phenomenol., 2(6) (1901), 559-572. [3] S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science, 290 (2000), 2323-2326. [4] D. A. Spielman, Graphs, vectors, and matrices, Bull. Amer. Math. Soc. 54(2017), 45-61. [5] L.K. Saul, S.T. Roweis, Y. Singer, Think globally, t locally: unsupervised learning of low dimensional manifolds, J. Machine Learn. Res., 4 (2003), 119-155. [6] E. W. Dijkstra, A note on two problems in connexion with graphs, Numer. Math., 1 (1959), 269-271. [7] Z. Zhang, H. Zha, Principal manifolds and nonlinear dimension reduction via local tangent space alignment, SIAM J. Sci. Comput., 26 (2002), 313-338. [8] E. Fix, J.L. Hodges, Discriminatory analysis. nonparametric discrimination: consistency properties, USAF school of aviation medicine, Randolph Field, Texas,1951. [9] M. Belkin P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comput., 15(6) (2003), 1373-1396. [10] Xiaofei He, Partha Niyogi, Locality preserving projections (LPP), Proceedings of the 16th international conference on neural information processing systems., (2003), 153-160. [11] Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han, Thomas Huang, Learning a spatially smooth subspace for face recognition, IEEE, (June 2007), 17-22. [12] Kouki M, Elkhaldi, A toward a predicting model of rm bankruptcy: evidence from the Tunisian context, Middle East Finance Economics., 14 (2011), 26-43. [13] Loredana Cultrera, Xavier Bredart, Bankruptcy prediction: the case of Belgian SMEs, Review of Accounting & Finance., 15 (2016), 101-119. [14] Bernardete Ribeiro, Ning Chen, Alexander Kovacec, Shaping graph pattern mining for nancial risk, Neurocomputing., 326327 (2019), 123-131. [15] A. Blanco, R. Pino-Mejias, J. Lara, S. Rayo, Credit scoring models for the micro nance industry using neural networks: evidence from Peru, Expert Syst Appl., 40(2013), 356-364. [16] FM. Rafiei, S. Manzari, S. Bostanian, Financial health prediction models using arti cial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence, Expert Syst Appl., 38 (2011), 10210-10217. | ||
|
آمار تعداد مشاهده مقاله: 712 تعداد دریافت فایل اصل مقاله: 671 |
||
Scholarly Journals Management System © 1991.05.01 by Allameh Tabatabaei University is
licensed under Attribution-NonCommercial 4.0 International.