DOI: 10.14704/nq.2018.16.5.1397

Risk Decision of Corporate Internet Financial Reporting Based on Brain Evoked Potential Testing Technology

Lingyan Ou

Abstract


Corporate decision makers will weigh and balance before making any decisions at different points in time. Different time and process of decision-making will lead to different degrees of risk to the financial status of the company. According to different inter-temporal decision-making financial risks, corporate decision makers will show different behavioral responses and neural changes. Based on the brain evoked potential testing technology, this study tests the behavioral performance and brain mechanism responses of the subjects under the frameworks of financial risk and zero financial risk, and explores the brain evoked potential and brain network mechanism of inter-temporal decision-making on financial risks. The experimental results show that subjects are more willing to choose the options that are nearer in time and smaller in number under the framework of risk conditions. Under the two frame conditions, the decision type has significant main effect, while the electrode has no main effect, and there is no interaction effect between the decision type and the electrode. The degree distribution, clustering coefficient and shortest path length under the two frame conditions are different, that is, the function and efficiency of brain network are different. Analysis of the key nodes by degree distribution also shows that the brain mechanism is different under the two conditions.

Keywords


Intertemporal decision making; Risk; Brain evoked potential testing technology; Brain network mechanism; Corporate internet financial reporting.

Full Text:

PDF

References


Cruz JM, Nagurney A, & Wakolbinger T. Financial engineering of the integration of global supply chain networks and social networks with risk management. Naval Research Logistics 2006; 53(7): 674-96.

Dastkhan H, & Gharneh NS. How the ownership structures cause epidemics in financial markets: a network-based simulation model. Physica A Statistical Mechanics & Its Applications 2018; 492: 324-42.

Groth SS, & Muntermann J. An intraday market risk management approach based on textual analysis. Decision Support Systems 2011; 50(4): 680-91.

Guo L, Wang Y, Yu H, Yin N, & Li Y. Study of brain functional network based on sample entropy of eeg under magnetic stimulation at pc6 acupoint. Bio-medical materials and engineering 2014; 24(1): 1063-69.

Hashem SQ, & Giudici P. Systemic risk of conventional and islamic banks: comparison with graphical network models. Applied Mathematics 2016; 7(17): 2079-96.

Ishizaki F, Harada T, Aoi S, Ikeda H, & Chikamura C. Clarification of recovery mechanism from chronic brain dysfunction and application to treatment – eeg spectrum analysis of brain function. Clinical Neurophysiology 2010; 121(1): S311-S311.

Miocinovic S, Miller A, Swann NC, Ostrem JL, & Starr PA. Chronic deep brain stimulation normalizes scalp eeg activity in isolated dystonia. Clinical Neurophysiology Official Journal of the International Federation of Clinical Neurophysiology 2017; 129(2): 368-76.

Nagurneya A. Financial networks with intermediation: risk management with variable weights. European Journal of Operational Research 2006; 172(1): 40-63.

Papenbrock J, & Schwendner P. Handling risk-on/risk-off dynamics with correlation regimes and correlation networks. Financial Markets & Portfolio Management 2015; 29(2): 125-47.

Pascualleone A, Freitas C, Oberman L, Horvath, JC, Halko M, & Eldaief M. Characterizing brain cortical plasticity and network dynamics across the age-span in health and disease with tms-eeg and tms-fmri. Brain Topography 2011; 24(3-4): 302-15.

Pavão LV, Pozo C, Costa CBB, & Jiménez L. Financial risks management of heat exchanger networks under uncertain utility costs via multi-objective optimization. Energy 2017; 139: 98-117.

Poledna S, Bochmann O, & Thurner S. Basel iii capital surcharges for g-sibs are far less effective in managing systemic risk in comparison to network-based, systemic risk-dependent financial transaction taxes. Journal of Economic Dynamics & Control 2017; 77: 230-46.

Sgouras KI, Dimitrelos DI, Bakirtzis AG, & Labridis DP. Quantitative risk management by demand response in distribution networks. IEEE Transactions on Power Systems 2017; 33(2): 1496-506.

Solorzano-Margain JP, Martinez-Jaramillo S, & Lopez-Gallo F. Financial contagion: extending the exposures network of the mexican financial system. Computational Management Science 2013; 10(2-3): 125-55.

Wessel JR. Testing multiple psychological processes for common neural mechanisms using EEG and independent component analysis. Brain Topography 2016; 31(1): 90-100.

Xia L, You D, Jiang X, & Guo Q. Comparison between global financial crisis and local stock disaster on top of chinese stock network. Physica A Statistical Mechanics & Its Applications (2017); 490: 222-30.

Zahedi A, Stuermer B, Hatami J, Rostami R, & Sommer W. Eliminating stroop effects with post-hypnotic instructions: brain mechanisms inferred from eeg. Neuropsychologia 2017; 96: 70-77.

Zaccone R, Sacile R, Fossa M. Energy modelling and decision support algorithm for the exploitation of biomass resources in industrial districts, International Journal of Heat and Technology 2017; 35(S1), S322-29.


Supporting Agencies





| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2017