A dolgozat áttekintést adott az alapvető tudatosság rendszeréről, jellemzőiről a pénz-ügyi instrumentumok szemszögéből. A dolgozat kísérletet tett az alapösszefüggések megtalálására, feltárására. A jelen dolgozatban taglalt kutatási terület széles kutatási lehetőséget nyújt a kutatók számára. Az elsődleges cél az Egyesített Mezőhöz való csat-lakozás feltérképezése, amellyel a gazdaság mélyebb aspektusait, összefüggéseit is meg-ismerhetjük.
Dolgozatom elsődlegesen a nem mély tanulást elősegítő neurális háló alkalmazására irányult, a modellek felismerésére és az összefüggések megjelenítésére. A modell még pontosabb működését mély tanulást elősegítő neurális háló alkalmazásával lehet erősí-teni, így az alapelvek szerint más független adatrendszerekben is adaptálhatóvá válhat.
A tudatosság általam felvázolt modelltípusa a legalapvetőbb összetevőket és azok összefüggéseit használja. A meghatározó, lényeges elemek figyelembevételével építet-tem fel a modellt. A rendszer tovább finomítható mesterséges intelligenciát használó programelemekkel.
A modell, illetve az alkalmazott szelektálási rendszer a gazdaság minden területén használható és továbbfejleszthető vállalati csoporttípustól függetlenül.
Irodalomjegyzék
Aerts, D. – Aerts, S. (1994): Applications of quantum statistics in psychological studies of decision processes. Foundations of Science, 1, 85–97. 1995/96. sz. https://doi.
org/10.1007/978-94-015-8816-4_11.
Ahn, B. S. – Cho, S. S. –Kim, C. Y. (2000): The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 2(18), 65–74. https://doi.org/10.1016/s0957-4174(99)00053-6.
Alam, P. – Booth, D. – Lee, K. – Thordarson, T. (2000): The use of fuzzy clustering algo-rithm and self-organizing neural networks for identifying potentially failing banks:
An experimental study. Expert Systems with Applications, 3(18), 185–199. https://
doi.org/10.1016/s0957-4174(99)00061-5.
Amania, F. A. – Fadlalla, A. M. (2017): Data mining applications in accounting:
A review of the literature and organizing framework. International Journal of Account-ing Information Systems, 24, 32–58. https://doi.org/10.1016/j.accinf.2016.12.004.
Anandarajan, M. – Anandarajan, A. (1999): A comparison of machine learning techniques with a qualitative response model for auditor’s going concern reporting.
Expert Systems with Applications, 4(16), 385–392. https://doi.org/10.1016/s0957-4174(99)00014-7.
Baaquie, B. E. (2007): Quantum Finance: Path Integrals and Hamiltonians for Options and Interest Rates. Cambridge University Press.
Back B. – Toivonen, J. – Vanharanta, H. – Visa, A. (2001): Comparing numerical data and text information from annual reports using self-organizing maps. International Journal of Accounting Information Systems, 4(2), 249–269. https://doi.org/10.1016/
s1467-0895(01)00018-5.
Ballarin, A. – Gervasi, S. – Cannat, V. – Liudaki, S. (1995): Company Financial Stra-tegic Analisys Using Neural Classifiers. Computational Intelligence for Financial Engi neering. IEEE. https://doi.org/10.1109/cifer.1995.495264.
Berry, R. H. – Nix, S. (1991): Regression analysis v. ratios in the cross-section analysis of financial statements. Accounting and Business Research, 21(82), 107–117. https://
doi.org/10.1080/00014788.1991.9729823.
Benhayoun, N. – Chairi, I. – El Gonnouni, A. – Lyhyaoui, A. (2013): Financial intel-li gence in prediction of firm’s creditworthiness risk: Evidence from support vector machine approach. Procedia Economics and Finance, 5, 103–112. https://doi.
org/10.1016/s2212-5671(13)00015-4.
Boyacioglu, M. A. – Kara, Y. – Baykan, O. K. (2009): Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 2(36), 3355–
3366. https://doi.org/10.1016/j.eswa.2008.01.003.
Calderon, T. G. – Cheh, J. J. (2002): A roadmap for future neural networks research in auditing and risk assessment. International Journal of Accounting Information Systems, 4(3), 203–236. https://doi.org/10.1016/s1467-0895(02)00068-4.
Callen, J. L. – Kwan, C. C. – Yip, P. C. – Yuan, Y. (1996): Neural network forecasting of quarterly accounting earnings. International Journal of Forecasting, 4(12), 475–482.
https://doi.org/10.1016/s0169-2070(96)00706-6.
Chakraborty, S. – Sharma, S. K. (2007): Prediction of corporate financial health by artificial neural network. International Journal of Electronic Finance, 4(1), 442–159.
https://doi.org/10.1504/ijef.2007.012898.
Chen, Y. – Zhang, L. – Zhang, L. (2013): Financial distress prediction for Chinese listed manufacturing companies. Procedia Computer Science, 17, 678–686. https://doi.
org/10.1016/j.procs.2013.05.088.
Chi, L. C – Tang, T. C. (2005): Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach. Expert Systems with Applications, 2(29), 244–255. https://doi.org/10.1016/j.eswa.2005.03.003.
Coakley, J. R. – Brown, C. E. (2000): Artificial neural networks in accounting and finance: Modeling issues. International Journal of Intelligent Systems in Account-ing, Finance & Management, 2(9), 119–144. https://doi.org/10.1002/1099-1174(200006)9:2<119::aid-isaf182>3.0.co;2-y.
Dattilo G. – Greco, S. Masciari, E. – Pontieri, L. (2000): A hybrid technique for data mining on balance-sheet data. In Kambayashi, Y. – Mohania, M. – Tjoa, A. M. (eds.):
Data Warehousing and Knowledge Discovery Second International Conference. Lon-don: Springer, 419–425. https://doi.org/10.1007/3-540-44466-1_42.
Doumpos, M. – Gaganis, C. – Pasiouras, F. (2005): Explaining qualifications in audit reports using a support vector machine methodology. Intelligent Systems in Account-ing, Finance and Management, 4(13), 197–215. https://doi.org/10.1002/isaf.268.
Eklund, T. – Back, B. – Vanharanta, H. – Visa, A. (2008): A face validation of a SOM-based financial benchmarking model. Journal of Emerging Technologies in Account-ing, 1(5), sz. 109–127. https://doi.org/10.2308/jeta.2008.5.1.109.
Etheridge, H. L. – Sriram, R. S. – Hsu, H. Y. (2000): A comparison of selected artificial neural networks that help auditors evaluate client financial viability. Decision Sciences, 2(31), 531–550. https://doi.org/10.1111/j.1540-5915.2000.tb01633.x.
Evans, J. R. (2013): Business Analytics: Methods, Models, and Decisions. Boston: Pren-tice-Hall.
Ezazi, M. E. – Ghotbi, F. S. – Ghotbi, S. F. (2013): Predicting earning management using RBF, ICA, and SVM in firms listed in Tehran security exchange. Asian Journal of Manage ment Research, 1(4), 208–220. https://doi.org/10.5958/2249-7307. 2014.00938.4.
Fayyad, U. P. – Shapiro, G. – Smyth, P. (1996): From data mining to knowledge discovery in databases. AI Magazine, 3(17), 37.
Foltin, C. – Garceau, L. (1996): Beyond expert systems: Neural networks in accounting.
National Public Accountant, 6(41), 26–32.
Gray, G. L. – Debreceny, R. S. (2014): A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information System, 4(15), 357–380.
Hagelin, J. S. (1987): Is consciousness the unified field? A field theorist’s perspective.
Modern Science and Vedic Science, 1, 29–87.
Han, J. – Kamber, M. (2006): Data Mining: Concepts and Techniques. Second Edition.
Elsevier, 67–68.
Haven, E. (2002): A discussion on embedding the Black–Scholes option pricing model in a quantum physics setting. Physica A: Statistical Mechanics and its Applications.
Physica A. 304. 507–524. https://doi.org/10.1016/j.accinf.2014.05.006.
Haven, E. E. – Khrennikov, A. (2013): Quantum Social Science. Cambridge University Press.
Helfert, E. A. (2001): Financial Analysis: Tools and Techniques. A Guide for Managers.
Mcgraw and Hill.
Hofmann, E. – Lampe, K. (2013): Financial statement analysis of logistics ser-vice providers: ways of enhancing performance. International Journal of Physical Dis tri bution and Logistics Management, 4(43), 4. https://doi.org/10.1108/
ijpdlm- 08-2012-0229.
Hoglund, H. (2012): Detecting earnings management with neural networks.
Expert Systems with Applications, 10(39), 9564–9570. https://doi.org/10.1016/j.
eswa. 2012.02.096.
Hoglund, H. (2013): Fuzzy linear regression-based detection of earnings management.
Expert Systems with Applications, 15(40), 6166–6172. https://doi.org/10.1016/j.
eswa.2013.05.046.
Huang, S. M. – Tsai, C. F. – Yen, D. C. – Cheng, Y. L. (2008): A hybrid financial analysis model for business failure prediction. Expert Systems with Applications, 3(35), 1034–
1040. https://doi.org/10.1016/j.eswa.2007.08.040.
Ittner, C. D. – Larcker, D. F. (2001): Assessing empirical research in managerial account-ing: A value-based management perspective. Journal of Accounting and Economics, 1(32), 349-410. https://doi.org/10.1016/s0165-4101(01)00026-x.
Kloptchenko, A. – Eklund, T. – Karlsson, J. – Back, B. – Vanharanta, H. – Visa, A.
(2004): Combining data and text mining techniques for analyzing financial reports.
Intel ligent Systems in Accounting, Finance and Management, 1(12), 29–41. https://
doi.org/10.1002/isaf.239.
Koh, H. C. – Tan, S. S. (1999): A neural network approach to the prediction of going concern status. Accounting and Business Research, 3(29), 211–216. https://doi.org/1 0.1080/00014788.1999.9729581.
Koh, H. C. – Low, C. K. (2004): Going concern prediction using data mining tech-niques. Managerial Auditing Journal, 3(19), 462–476. https://doi.org/ 10. 1108/
02686900410524436.
Koskivaara, E. (2004a): Artificial neural networks in analytical review pro-ce dures. Managerial Auditing Journal, 2(19), 191–223. https://doi.org/ 10.
1108/ 02686900410517821.
Kurzweil, R. (2005): How to Create a Mind. The Secret of Human Thought Revealed.
New York: Viking.
Kurzweil, R. (2013): A szingularitas küszöbén. Ad Astra Kiadó.
Kurzweil, R. – Steadman, R. (2012): Virtually Human. The Promise-and the Peril- of Digital Immorality. New York: St. Martins’s Press,
Laskai András (2018): Az üzleti terv pénzügyi instrumentumai nemzetközi perspek-tívában a tudatosság tükrében. PhD-értekezés. Soproni Egyetem. Lámfalussy János Közgazdaságtudományi Kar. Széchenyi István Gazdálkodás- és Szervezéstudomá-nyok Doktori Iskola.
Laskai, András (2019a): Consciousness and the International Business Planning.
International Journal of Science and Business, 1(3), 1–6. https://doi.org/10.5281/
zenodo.2528383.
Laskai, András (2019b): Consciousness system approaches and the financial instruments. International Journal of Science and Business, 1(3), 7–16. https://doi.
org/ 10.5281/zenodo.2528355.
Lenard, M. J. – Alam, P. – Madey, G. R. (1995): The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision.
Decission Sciences, 2(26), 209–227. https://doi.org/10.1111/j.1540-5915.1995.
tb01426.x.
Leon, C. (2017): Whose balance sheet is this? Neural networks for banks’ pattern recognition. Wilmott, 91, 34–47. https://doi.org/10.1002/wilm.10619.
Li, H. – Sun, J. – Li, J. C. – Yan, X. Y. (2013): Forecasting business failure using two-stage ensemble of multivariate discriminant analysis and logistic regression.
Expert Systems with Applications, 5(30), 385–397. https://doi.org/10.1111/j.1468-0394.2012.00642.x.
Magnusson, C. – Arppe, A. – Eklund, T. – Back, B. – Vanharanta, H. – Visa, A. (2005):
The language of quarterly reports as an indicator of change in the company’s financial status. Information and Management, 4(42), 561–574. https://doi.org/10.1016/j.
im.2004.02.008.
Martens, D. – Bruynseels, L. – Baesens, B. – Willekens, M. – Vanthienen, J. (2008):
Predicting going concern opinion with data mining. Decision Support Systems, 4(45). 765–777. https://doi.org/10.1016/j.dss.2008.01.003.
Neves, C. M. M. – Silva, V. R. M. – Barros, G. A. B. – Lopes, R. V. V. (2011): A variation of the genetic algorithm of Holland to support analysis of balance sheet and income statement for the fiscal year. Information Technology and Artificial Intelligence, 2, 331. https://doi.org/10.1109/itaic.2011.6030338.
Ngai, E. W. T. – Hu, Y. – Wong, Y. H. – Chen, Y. – Sun, X. (2011): The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decision Support Systems, 3(50), 559–569. https://
doi.org/10.1016/j.dss.2010.08.006.
Orrell, D. (2016): A quantum theory of money and value. Economic Thought, 2(5), 19–
28. https://doi.org/10.2139/ssrn.2624371.
Orrell, D. – Chlupatý, R. (2016): The Evolution of Money. Columbia University Press, 171–175.
Peel, M. J. (1989): The going-concern qualification debate: Some UK evidence. British Accounting Review, 4(21), 329–350. https://doi.org/10.1016/0890-8389(89) 90032-2.
Pujari, A. K. (2001): Data Mining Techniques. Universities Press.
Quek, C. – Zhou, R. W. – Lee, C. H. (2009): A novel fuzzy neural approach to data reconstruction and failure prediction. Intelligent Systems in Accounting, Finance and Management, 1/2(16), 165–187. https://doi.org/10.1002/isaf.299.
Salterio, S. (1996): The effects of precedents and client position on auditors’ financial accounting policy judgment. Accounting, Organizations & Society, 5(21), 467–486.
https://doi.org/10.1016/0361-3682(95)00041-0.
Shirata, C. Y. – Sakagami, M. (2008): An analysis of the Going Concern Assumption:
text mining from Japanese financial reports. Journal of Emerging Technologies in Accounting, 1(5), 1–16. https://doi.org/10.2308/jeta.2008.5.1.1.
Shubik, M. (1999): Quantum economics, uncertainty and the optimal grid size.
Economics Letters, 3(64), 277–278. https://doi.org/10.1016/s0165-1765(99)00095-6.
Song, D. B. – Lee, H. Y. – Cho, E. J. (2013): The association between earnings manage-ment and asset misappropriation. Managerial Auditing Journal, 6(28), 542–567.
https://doi.org/10.1108/02686901311329919.
Spathis, C. T. (2003): Audit qualification, firm litigation, and financial information: An empirical analysis in Greece. International Journal of Auditing, 1, 71–85. https://doi.
org/10.1111/1099-1123.00006.
Teese, C. (2017): Money and Quantum Physics. News Weekly.
Tsai, C.F.-Chiou, Y.J., (2009): Earnings management prediction: A pilot study of com-bining neural networks and decision trees. Expert Systems with Applications, 3(36), 7183–7191. https://doi.org/10.1016/j.eswa.2008.09.025.
Tsaih, R. H. – Lin, W. Y. – Huang, S. Y. (2009): Exploring fraudulent financial reporting with GHSOM. Proceeding of Pacific Asia Workshop (PAISI). Intelligence and Secu-rity Informatics, 31–41. https://doi.org/10.1007/978-3-642-01393-5_5.
Tung, W. L. – Quek, C. – Cheng, P. (2004): GenSo-EWS: A novel neural-fuzzy based early warning system for predicting bank failures. Neural Network, 4(17), 567–588.
https://doi.org/10.1016/j.neunet.2003.11.006.
Zhang, L. – Wang, L. – Zhang, J. (2011): A computer auditing model of the balance sheet parallel simulation based on data mining. In Modeling Risk Management for Resources and Environment in China. Springer-Verlag Berlin Heidelberg, 567–576.
https://doi.org/10.1007/978-3-642-18387-4_62.
Yang, J. G. S. (2006): Data mining techniques for auditing attest function and fraud detection. Journal of Forensic Investigative Accounting, 1(1), 4–10.
Yang, S. – Cogill, R. (2013): Balance Sheet Outlier Detection using a Graph Similarity Algorithm. 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics. 1–8. https://doi.org/10.1109/cifer.2013.6611709.
Yang, J. – Zhang,Y. – Au, K. – Xue, H. (2011): Prior experiences and social class as mode-rators om planning-performance relationship in China’s new business ventures. Natio-nal Science Foundation of China, 1–40. https://doi.org/10.1002/sej.1155.
Youn, H. – Gu, Z. (2010): Predicting Korean lodging firm failures: an artificial neural network model along with a logistic regression model. International Journal Hospital Management, 1(29), 120–127. https://doi.org/10.1016/j.ijhm.2009.06.007.
Virág Miklós – Kristóf Tamás – Fiáth Attila – Varsányi Judit (2013): Pénzügyi elemzés, csődelőrejelzés, válságkezelés. Kossuth Kiadó.
Wang, S. (2010): A comprehensive survey of data mining-based accounting-fraud detec tion research. Proceeding of the International IEEE Conference on Intelligent Com pu tation Technology and Automation. https://doi.org/10.1109/icicta.2010.831.