Qualitative Models of Bankruptcy Proceedings Integrating Psychological and Economic Aspects

AUTHOR(S):

TITLE

Qualitative Models of Bankruptcy Proceedings Integrating Psychological and Economic Aspects

ABSTRACT

There is a broad spectrum of BP (Bankruptcy Proceedings) models. They are unique, partially subjective, inconsistent, vague and multidimensional. BPs development suffers from IS (Information Shortage). IS often eliminates straightforward application of traditional statistical methods. It is therefore often prohibitively difficult to analyse them using numerical quantifiers. Oversimplified or highly specific BPs are sometimes obtained. Their practical applicability is therefore (very) limited. Artificial Intelligence has developed a number of tools to solve such problems. Qualitative reasoning is one of them. It is based on the least information intensive quantifiers i.e. trends. There are just three trend / qualitative values used to quantify variables and their derivatives: plus/increasing; zero/constant; negative/decreasing. There are qualitative BP knowledge items in equationless forms such as heuristics. For example – if standard of ensured justice is increasing then level of creditors bullying is decreasing. Such verbal knowledge item cannot be incorporated into a traditional numerical model. Qualitative models must be used. A qualitative model can be developed under conditions when the relevant quantitative model must be heavily simplified. The key information input into BPs is expert knowledge. The case study presents a model based on integration of equationless relations using 8 variables e.g. selling of assets, bullying of creditors or ensured justice. The result is represented by 11 scenarios. The paper is self-contained, no a prior knowledge of qualitative models is required. The result is represented by 11 scenarios. The paper is self-contained, no a prior knowledge of qualitative models is required.

KEYWORDS

Insolvency, qualitative variables, bankruptcy related knowledge, bankruptcy proceedings, decision making

REFERENCES

[1] Bredeweg, B., Bouwer, A., Jellema, J., Bertels, D., Linnebank, F. F., & Liem, J. (2007). Garp3. Proceedings of the 4th international conference on Knowledge capture - K-CAP 07. doi:10.1145/1298406.1298445

[2] Choueiry, B.Y., Iwasaki, Y., McIlraith, S., 2005. Towards a practical theory of reformulation for reasoning about physical systems. Artif. Intell., Reformulation 162, 145–204. doi:10.1016/j.artint.2004.01.004.

[3] Dočekalová, M.P., Kocmanová, A., 2016. Composite indicator for measuring corporate sustainability. Ecol. Indic. 61, Part 2, 612– 623. doi:10.1016/j.ecolind.2015.10.012

[4] Dohnal, M., 1985. Applications of a universal expert system in industry. Comput. Ind. 6, 115–121. doi:10.1016/0166-3615(85)90017- X

[5] Džeroski, S., Grbović, J., Walley, W.J., Kompare, B., 1997. Using machine learning techniques in the construction of models. II. Data analysis with rule induction. Ecol. Model. 95, 95–111. doi:10.1016/S0304- 3800(96)00029-4.

[6] Leoni, P. L. (n.d.). Psychological Determinants of Occurrence and Magnitude of Market Crashes. SSRN Electronic Journal. doi:10.2139/ssrn.1439314

[7] Lipmann O., Bogen H., 1996 Naive Physik. Arbeiten aus dem Instuut fur angewandte Psychologie in Berlin. Theoretische und experimentele Untersuchungen fuer die Feihigkeit zu intelligentem Handeln (Leipzig, Johann Ambrosius Barth)

[8] Sen, P.K., Singer, J.M., 1994. Large Sample Methods in Statistics: An Introduction with Applications. CRC Press.

[9] Orrell, D., McSharry, P., 2009. System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach. Int. J. Forecast., Special section: Decision making and planning under low levels of predictability 25, 734–743. doi:10.1016/j.ijforecast.2009.05.002

[10] Vicha, T., & Dohnal, M. (2008). Qualitative identification of chaotic systems behaviours. Chaos, Solitons & Fractals, 38(1), 70-78. doi:10.1016/j.chaos.2008.01.027

[11] Vicha, T., & Dohnal, M. (2008). Qualitative feature extractions of chaotic systems. Chaos, Solitons & Fractals, 38(2), 364-373. doi:10.1016/j.chaos.2008.01.008

[12] Delis, M. D., & Mylonidis, N. (2015). Trust, happiness, and households’ financial decisions. Journal of Financial Stability, 20, 82-92. doi:10.1016/j.jfs.2015.08.002

[13] Forbus, K. D. (1984). Qualitative Process Theory. Qualitative Reasoning About Physical Systems, 85-168. doi:10.1016/b978- 0-444-87670-6.50006-6

[14] Townsend, J. T. (2008). Mathematical psychology: Prospects for the 21st century: A guest editorial. Journal of Mathematical Psychology, 52(5), 269-280. doi:10.1016/j.jmp.2008.05.001

Cite this paper

Tomáš Polácek, Mirko Dohnal. (2017) Qualitative Models of Bankruptcy Proceedings Integrating Psychological and Economic Aspects. International Journal of Economics and Management Systems, 2, 198-205