Acta Univ. Agric. Silvic. Mendelianae Brun. 2019, 67(5), 1269-1283 | DOI: 10.11118/actaun201967051269

Utilization of Artificial Intelligence for Sensitivity Analysis in the Stock Market

Zuzana Janková, Petr Dostál
Institute of Informatics, Faculty of Business and Management, Brno University of Technology, Kolejní 2906/4, Královo Pole, 612 00 Brno, Czech Republic

The main contribution of this paper is to perform sensitivity analysis using artificial intelligence methods on the US stock market using alternative psychological indicators. The Takagi-Sugeno fuzzy model applies investor sentiment represented by VIX index and monitors the impact of economic optimism, political stability and control of the corruption index on the S&P 500 stock index. Alternative psychological indicators have been chosen that have not been explored in the context of stock index performance sensitivity. Investors primarily use fundamental and technical analysis as a source to determine when and what to buy into an investment portfolio. However, psychological factors that may indicate the strength of reaction to the market are often neglected. Fuzzy rules are determined and tested using a neuro-fuzzy inference system and then the rules are reduced by fuzzy clustering to improve performance of ANFIS. The membership function is defined as a Gaussian function because it has the least RMSE value. The sensitivity analysis confirmed that there is a significant impact of the political stability index and the economic optimism index on the S&P 500 performance. Conversely, the sensitivity analysis, unlike the previous study, did not confirm the strong impact of VIX on equity index performance. Results indicate that incorporating psychological indicators in macroeconomic models leads to better supervision and control of the financial markets.

Keywords: artificial intelligence, fuzzy approach, fuzzy logic, sensitivity analysis, sentiment, soft computing, stock market
Grants and funding:

This paper was supported by project No. FP-J-19-5814 'The Use of Artificial Intelligence in Business III' from the Internal Grant Agency at Brno University of Technology.

Received: May 14, 2019; Accepted: September 30, 2019; Published: October 31, 2019  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Janková, Z., & Dostál, P. (2019). Utilization of Artificial Intelligence for Sensitivity Analysis in the Stock Market. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis67(5), 1269-1283. doi: 10.11118/actaun201967051269
Download citation

References

  1. ALAM, S. and NADEEM, M. 2015. Impact of macroeconomic factors on capital market of Pakistan: An empirical study. Journal of Business Strategies, 9(2): 47-58. Go to original source...
  2. ALJAZAERLI, M. A., SIROP, R. and MOUSELLI, S. 2016. Corruption and Stock Market Development: New Evidence from GCC Countries. Verslas: Teorija ir Praktika, 17(2): 117-127. Go to original source...
  3. BABU, A. S. and KUMAR, R. R. 2015. The Impact of Sentiments on Stock Market: A Fuzzy Approach. IUP Journal of Applied Finance, 21(2): 22-33.
  4. BOLGORIAN, M. 2011. Corruption and stock market development: A quantitative approach. Physica A: Statistical Mechanics and its Applications, 390(23-24): 4514-4521. DOI: 10.1016/j.physa.2011.07.024 Go to original source...
  5. BOYACIOGLU, M. A. and AVCI, D. 2010. An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Systems with Applications, 37(12): 7908-7912. DOI: 10.1016/j.eswa.2010.04.045 Go to original source...
  6. CASTILLO, O., MELIN, P., KACPRZYK, J. and PEDRYCZ, W. 2007. Type-2 Fuzzy Logic: Theory and Applications. In: IEEE International Conference on Granular Computing (GRC). November, Fremont, CA, USA: IEEE, pp. 145-145. Go to original source...
  7. CHANG, P. C., FAN, C. Y. and LIN, J. L. 2011. Trend discovery in financial time series data using a case based fuzzy decision tree. Expert Systems with Applications, 38(1): 6070-6080. DOI: 10.1016/j.eswa.2010.11.006 Go to original source...
  8. CHAU, F., DEESOMSAK, R. and WANG, J. 2014. Political uncertainty and stock market volatility in the Middle East and North African (MENA) countries. Journal of International Financial Markets, Institutions and Money, 28: 1-19. DOI: 10.1016/j.intfin.2013.10.008 Go to original source...
  9. DASZYNSKA-ZYGADLO, K., SZPULAK, A. and SZYSZKA, A. 2014. Investor sentiment, optimism and excess stock market returns. Evidence from emerging markets. Business and Economic Horizons, 10(4): 362-373. DOI: 10.15208/beh.2014.27 Go to original source...
  10. DHAOUI, A. and KHRAIEF, N. 2014. Sensitivity of trading intensity to optimistic and pessimistic beliefs: Evidence from the French stock market. Arab Economic and Business Journal, 9(2): 115-132. DOI: 10.1016/j.aebj.2014.05.008 Go to original source...
  11. DOSTÁL, P. and KRULJACOVÁ, A. 2018. Evaluation of University Quality via Fuzzy Logic. In: Inovation Management and Education Excellence through Vision 2020. 15-16 November, Sevilla, Spain: IBIMA, pp. 1368-1375.
  12. DOSTÁL, P. 2011. Advanced Decision Making in Business and Public Services. Brno: Academic Publishing House CERM.
  13. DOSTÁL, P., RAIS, K. and SOJKA, Z. 2005. Advanced methods of managerial decision-making: concrete examples of using methods in practice [in Czech: Pokročilé metody manažerského rozhodování: konkrétní příklady využití metod v praxi]. Praha: Grada. Expert (Grada).
  14. ESFAHANIPOUR, A. and AGHAMIRI, W. 2010. Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis. Expert Systems with Applications, 37(7): 4742-4748. DOI: 10.1016/j.eswa.2009.11.020 Go to original source...
  15. FERNANDES, N. and GONENC, H. 2016. Multinationals and cash holdings. Journal of Corporate Finance, 39: 139-154. DOI: 10.1016/j.jcorpfin.2016.06.003 Go to original source...
  16. FINANCE YAHOO. 2019. S&P 500 index. Yahoo Finance. [Online]. Available at: https://finance.yahoo.com/quote/%5EGSPC?p=^GSPC [Accessed: 2019, April 13].
  17. GARCÍA, F., GUIJARRO, F., OLIVER, J. and TAMOŠIŪNIENĖ, R. 2018. Hybrid Fuzzy Neural Network to Predict Price Direction in the German DAX-30 Index. Technological and Economic Development of Economy, 24(6): 2161-2178. DOI: 10.3846/tede.2018.6394 Go to original source...
  18. GHANI, U., BAJWA, I. and ASHFAQ, A. 2018. A Fuzzy Logic Based Intelligent System for Measuring Customer Loyalty and Decision Making. Symmetry, 10(12): 761. DOI: 10.3390/sym10120761 Go to original source...
  19. HAMMER, M., JANDA, O. and ERTL, J. 2012. Selected Soft-Computing Methods in Power Oil Transformer Diagnostics - part 1. [in Czech: Využití vybraných soft-computingových metod v diagnostice výkonových olejových transformátorů - 1. Část]. Elektrorevue, 14(3): 33.
  20. JEFFERSON, C., LIU, H. and COCEA, M. 2017. Fuzzy Approach for Sentiment Analysis. In: IEEE International Conference on Fuzzy Systems. 9. July. Naples, Italy: IEEE, pp. 1-6. Go to original source...
  21. KLIGER, D. and KUDRYAVTSEV, A. 2013. Volatility expectations and the reaction to analyst recommendations. Journal of Economic Psychology, 37(C): 1-6. DOI: 10.1016/j.joep.2013.04.003 Go to original source...
  22. KLIGER, D. and LEVY, O. 2003. Mood-induced variation in risk preferences. Journal of Economic Behaviour and Organization, 52: 573-584. DOI: 10.1016/S0167-2681(03)00069-6 Go to original source...
  23. LAU, C. K. M., DEMIR, E. and BILGIN, M. H. 2013. Experience-based corporate corruption and stock market volatility: Evidence from emerging markets. Emerging Markets Review, 17: 1-13. DOI: 10.1016/j.ememar.2013.07.002 Go to original source...
  24. LIU, H. and COCEA, H. 2017. Fuzzy Rule Based Systems for Interpretable Sentiment Analysis. In: 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI). 4-6 February, Doha, Qatar: IEEE, pp. 129-136. Go to original source...
  25. LOEWENSTEIN, G. F., HSEE, C. K., WEBER, E. V. and WELCH, N. 2001. Risk as feelings. Psychological Bulletin, 127(2): 267-286. DOI: 10.1037/0033-2909.127.2.267 Go to original source...
  26. MADHUSUDHANAN, S. and MOORTHI, M. 2018. Optimized fuzzy technique for enhancing sentiment analysis. Cluster Computing, (online only): doi.org/10.1007/s10586-017-1514-z. DOI: 10.1007/s10586-017-1514-z Go to original source...
  27. NAMOURI, H., JAWADI, F., FTITI, Z. and HACHICHA, N. 2017. Threshold effect in the relationship between investor sentiment and stock market returns: a PSTR specification. Applied Economics, 50(5): 559-573. DOI: 10.1080/00036846.2017.1335387 Go to original source...
  28. NOVÁK, V. 2000. Basics of fuzzy modeling [in Czech: Základy fuzzy modelování]. Praha: BEN.
  29. OMODERO, C. O. 2018. Corruption and stock market performance in Nigeria. Annals of Spiru Haret University. Economic Series, 18(4): 23-40. DOI: 10.26458/1841 Go to original source...
  30. PEROTTI, E. C. and OIJEN, P. V. 2001. Privatization, political risk and stock market development in emerging economies. J. Int. Money Finance, 20(1): 43-69. DOI: 10.1016/S0261-5606(00)00032-2 Go to original source...
  31. RUAN, L. 2018. Research on Sustainable Development of the Stock Market Based on VIX Index. Sustainability, 10(11): 2-12. Go to original source...
  32. SALLEH, M. N. M., TALPUR, N. and TALPUR, K. H. 2018. A Modified Neuro-Fuzzy System Using Metaheuristic Approaches for Data Classification. In: ACEVES-FERNANDEZ, M. A. Artificial Intelligence - Emerging Trends and Applications. IntechOpen. Go to original source...
  33. SARWAR, G. 2014. U.S. stock market uncertainty and cross-market European stock returns. Journal of Multinational Financial Management, 28(C): 1-14. DOI: 10.1016/j.mulfin.2014.07.001 Go to original source...
  34. SHRIVASTAVA, H. and SRIDHARAN, S. 2013. Conception of data preprocessing and partitioning procedure for machine learning algorithm. International Journal of Recent Advances in Engineering & Technology, 1(3): 160-164.
  35. TAKAGI, T. and SUGENO, M. 1985. Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man. Cybernet., 15(1): 116-132. DOI: 10.1109/TSMC.1985.6313399 Go to original source...
  36. TALPUR, N., SALLEH, M. N. M. and HUSSAIN, K. 2017. An investigation of membership functions on performance of ANFIS for solving classification problems. In: IOP Conference Series: Materials Science and Engineering. 6-7 May. Melaka, Malaysia: IOP, pp. 1-7. Go to original source...
  37. TASKIN, A. and KUMBASAR, T. 2015. An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems. In: IEEE Symposium Series on Computational Intelligence. 7-10 December. Cape Town, South Africa: IEEE, pp. 1561-1568. Go to original source...
  38. TRABELSI, M. A. 2017. Political uncertainty and behaviour of Tunisian stock market cycles: Structural unobserved components time series models. Research in International Business and Finance, 39: 206-214. DOI: 10.1016/j.ribaf.2016.07.029 Go to original source...
  39. TUNG, K. T. and LE, M. H. 2017. An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem. JOIV: International Journal on Informatics Visualization, 1(2): 40-49. DOI: 10.30630/joiv.1.2.20 Go to original source...
  40. WANG, J. and WANG, J. 2015. Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing, 156(1): 68-78. DOI: 10.1016/j.neucom.2014.12.084 Go to original source...
  41. WORLD BANK. 2019. The Worldwide Governance Indicators (WGI) project. [Online]. Available at: http://info.worldbank.org/governance/wgi/#home [Accessed: 2019, April 13].
  42. WRIGHT, W. F. and BOWER, G. H. 1992. Mood effects on subjective probability assessment. Organizational Behaviour and Human Decision Processes, 52(2): 276-291. DOI: 10.1016/0749-5978(92)90039-A Go to original source...
  43. ZADEH, L. A. 1965. Fuzzy sets. Information and Control, 8(3): 338-353. DOI: 10.1016/S0019-9958(65)90241-X Go to original source...
  44. ZITTA, R. and PALATOVÁ, M. 2005. Methods for generating fuzzy rules from data [in Czech: Metody pro generování fuzzy pravidel z dat]. In: Konference Matlab 2005. Praha: Matlab, pp. 1-8.

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY NC ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.