Advertisement

Human-autonomous devices for characteristic analysis of pompeii trap in American finance

  • Han He
  • Yuanyuan Hong
  • Li Yin
  • Weiwei LiuEmail author
Original Research
  • 12 Downloads

Abstract

Effective prevention of traps in financial networks has become a focus of attention. Faced with the diversity of financial data and the complexity of financial traps, how to measure the risk value of investment behavior is very important. After analyzing the nature of risk, this paper puts forward an analysis of the characteristics of Pompeii trap in American finance based on human autonomous equipment. The feature analysis method based on human autonomous equipment is one of many risk measurement methods. On this basis, the information entropy risk function is established, and the validity of the model can be proved by experiments. The experimental results show that the model can help users avoid the so-called risk-free, high-yield, virtual investment projects, and avoid falling into the financial Ponzi scheme of borrowing new and repaying old.

Keywords

Information entropy Pompeii trap Financial risk Risk measurement Humanized computing Human-autonomous devices 

Notes

References

  1. Ahmed S, Elsholkami M, Elkamel A, Du J, Ydstie EB, Douglas PL (2014) Financial risk management for new technology integration in energy planning under uncertainty. Appl Energy 128:75–81CrossRefGoogle Scholar
  2. Cheng Y (2019) Mutual guarantee risk mechanism of animal husbandry and veterinary enterprises. Revista Científica 29(3):607–617Google Scholar
  3. Cueva C, Roberts RE, Spencer T, Rani N, Tempest M, Tobler PN, Rustichini A (2015) Cortisol and testosterone increase financial risk taking and may destabilize markets. Sci Rep 5:11206CrossRefGoogle Scholar
  4. Fisher PJ, Yao R (2017) Gender differences in financial risk tolerance. J Econ Psychol 61:191–202CrossRefGoogle Scholar
  5. Grisse C, Nitschka T (2015) On financial risk and the safe haven characteristics of Swiss franc exchange rates. J Empir Financ 32:153–164CrossRefGoogle Scholar
  6. Guizar-Mateos I (2013) Financial development, the dynamics of technology choices, and poverty traps. Doctoral dissertation, The Ohio State UniversityGoogle Scholar
  7. Haeufle DFB, Günther M, Wunner G, Schmitt S (2014) Quantifying control effort of biological and technical movements: an information-entropy-based approach. Phys Rev E 89(1):012716CrossRefGoogle Scholar
  8. Haldar SK, Chakrabarti B (2013) Dynamical features of Shannon information entropy of bosonic cloud in a tight trap. Int J Mod Phys B 27(13):1350048MathSciNetCrossRefGoogle Scholar
  9. Harte J, Newman EA (2014) Maximum information entropy: a foundation for ecological theory. Trends Ecol Evol 29(7):384–389CrossRefGoogle Scholar
  10. Kandasamy N, Hardy B, Page L, Schaffner M, Graggaber J, Powlson AS, Coates J (2014) Cortisol shifts financial risk preferences. Proc Natl Acad Sci 111(9):3608–3613CrossRefGoogle Scholar
  11. Kou G, Peng Y, Wang G (2014) Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf Sci 275:1–12CrossRefGoogle Scholar
  12. Matros P, Vilsmeier J (2014) The multivariate option iPoD framework: assessing systemic financial riskGoogle Scholar
  13. Nedeltchev S, Shaikh A (2013) A new method for identification of the main transition velocities in multiphase reactors based on information entropy theory. Chem Eng Sci 100:2–14CrossRefGoogle Scholar
  14. Saksena P, Hsu J, Evans DB (2014) Financial risk protection and universal health coverage: evIDence and measurement challenges. PLoS Med 11(9):e1001701CrossRefGoogle Scholar
  15. Su J, Furman E (2017) A form of multivariate Pareto distribution with applications to financial risk measurement. ASTIN Bull J IAA 47(1):331–357MathSciNetCrossRefGoogle Scholar
  16. Toma A, Dedu S (2014) Quantitative techniques for financial risk assessment: a comparative approach using different risk measures and estimation methods. Procedia Econ Financ 8:712–719CrossRefGoogle Scholar
  17. Wang S, Xu R, Liu B, Gui L, Zhou Y (2014) Financial named entity recognition based on conditional random fields and information entropy. In: IEEE international conference on machine learning and cybernetics, vol 2, pp 838–843Google Scholar
  18. Weller S, O’Neill P (2014) De-industrialisation, financialisation and Australia’s macro-economic trap. Camb J Reg Econ Soc 7(3):509–526CrossRefGoogle Scholar
  19. Yang Y (2013) The study of Kunming's financial risk early warning based on the perspective of financial ecological environment. In: IEEE 6th international conference on information management, innovation management and industrial engineering, vol 3, pp 623–626Google Scholar
  20. Yang ZB, Zhou RX, Zhang Q, Yu M (2013) A portfolio optimization model based on information entropy and fuzzy time series. In: IEEE sixth international conference on business intelligence and financial engineering, pp 166–170Google Scholar
  21. Yiping H, Qin G, Xun W (2014) Financial liberalization and the mIDdle-income trap: what can China learn from the cross-country experience? China Econ Rev 31:426–440CrossRefGoogle Scholar
  22. Zeng K, Li HQ, Zeng MJ, Liu PQ (2014) Power system risk security assessment based on maximum information entropy principle. In: IEEE international conference on power system technology, pp 421–426Google Scholar
  23. Zhang X, Mei C, Chen D, Li J (2016) Feature selection in mixed data: a method using a novel fuzzy rough set-based information entropy. Pattern Recogn 56:1–15CrossRefGoogle Scholar
  24. Zhenhai Z, Shining L, Zhigang L, Hao C (2013) Multi-label feature selection algorithm based on information entropy. J Comput Res Dev 50(6):1177–1184Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Rongzhi College of Chongqing Technology and Business UniversityChongqingChina
  2. 2.School of Public Health and ManagementChongqing Medical UniversityChongqingChina

Personalised recommendations