Stochastic informative expert system for investment
Abstract
The stochastic nature of investment process implies that it should be treated not unambiguously. Instead of concentrating only on possible return, it is worth analysing three parameters when we discuss the future investment results. These parameters are return possibility, reliability of this possibility, and the riskiness. The stochastic informative expert system for investment allows to analyse the behaviour of financial markets, forecasting the dynamics of stock prices and, along with that, rationally allocating investment resources. The proposed system is based on the adequate portfolio model, previously developed by the authors. Considering the real-time characteristics of financial markets, the system can be useful for individual investors, as well as for institutional investors, such as investment funds. Also, the authors propose the original risk tolerance determination methodology, which divides investors into three categories according their risk tolerance. The system can be applicable not only to capital markets, but also to other business or macroeconomic processes. As an example, a portfolio of the interaction of macroeconomic indicators with USA, UK, and Lithuanian data is developed. Such results could be useful for economists and governments in order to attain the higher value added in a particular country.
Keyword : stochastic expert system, investment, portfolio, stochastic optimization, risk tolerance, macroeconomic indicators
How to Cite
Rutkauskas, A. V., & Stasytytė, V. (2020). Stochastic informative expert system for investment. Journal of Business Economics and Management, 21(1), 136-156. https://doi.org/10.3846/jbem.2020.11768
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Ahuja, A., & Rödder, W. (2003). Project risk management by a probabilistic expert system. In U. Leopold-Wildburger, F. Rendl, & G. Wäscher (Eds.), Operations Research Proceedings 2002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55537-4_53
Buračas, A., Rutkauskas, A. V., & Joshi, L. (2014). Metaeconomics: stochastics & nanotech: New approaches to contemporary reality. LAP LAMBERT Academic Publishing.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational intelligence and financial markets: a survey and future directions. Expert Systems with Applications, 55, 194–211. https://doi.org/10.1016/j.eswa.2016.02.006
Chojnacki, E., Plumecocq, W., & Audouin, L. (2019). An expert system based on a Bayesian network for fire safety analysis in nuclear area. Fire Safety Journal, 105, 28–40. https://doi.org/10.1016/j.firesaf.2019.02.007
Chrysafi, A., Cope, J. M., & Kuparinen, A. (2019). Eliciting expert knowledge to inform stock status for data-limited stock assessments. Marine Policy, 101, 167–176. https://doi.org/10.1016/j.marpol.2017.11.012
Fasanghari, M., & Montazer, G. A. (2010). Design and implementation of fuzzy expert system for Tehran stock exchange portfolio recommendation. Expert Systems with Applications, 37(9), 6138–6147. https://doi.org/10.1016/j.eswa.2010.02.114
García, F., González-Bueno, J., Oliver, J., & Riley, N. (2019b). Selecting socially responsible portfolios: a fuzzy multicriteria approach. Sustainability, 11(9), 2496. https://doi.org/10.3390/su11092496
García, F., González-Bueno, J., Oliver, J., & Tamošiūnienė, R. (2019a). A credibilistic mean-semivariance-PER portfolio selection model for Latin America. Journal of Business Economics and Management, 20(2), 225–243. https://doi.org/10.3846/jbem.2019.8317
García, F., Guijarro, F., Oliver, J., & 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. https://doi.org/10.3846/tede.2018.6394
Gottschlich, J., & Hinz, O. (2014). A decision support system for stock investment recommendations using collective wisdom. Decision Support Systems, 59, 52–62. https://doi.org/10.1016/j.dss.2013.10.005
Hurtado, S. M. (2010). Modeling of operative risk using fuzzy expert systems. In M. Glykas (Ed.), Fuzzy Cognitive Maps: Vol. 247. Studies in Fuzziness and Soft Computing (pp. 135–159). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03220-2_6
Yazdi, M., Hafezi, P., & Abbassi, R. (2019). A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment. Journal of Loss Prevention in the Process Industries, 58, 51–29. https://doi.org/10.1016/j.jlp.2019.02.001
Icen, D., & Gunay, S. (2019). Design and implementation of the fuzzy expert system in Monte Carlo methods for fuzzy linear regression. Applied Soft Computing, 77, 399–411. https://doi.org/10.1016/j.asoc.2019.01.029
Young, C. C., & Taib, S. M. (2009). Designing a decision support system model for stock investment strategy. In Proceedings of the World Congress on Engineering and Computer Science 2009 I, San Francisco, USA.
Jalota, H., Thakur, M., & Mittal, G. (2017) Credibilistic decision support system for portfolio optimization. Applied Soft Computing, 59, 512–528. https://doi.org/10.1016/j.asoc.2017.05.054
Kim, C., & Won, C. (2004). A knowledge-based framework for incorporating investor’s preference into portfolio decision-making. Intelligent Systems in Accounting, Finance and Management, 12(2), 121–138. https://doi.org/10.1002/isaf.248
Liang, T.-P., & Liu, Y.-H. (2018). Research Landscape of Business Intelligence and Big Data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2–10. https://doi.org/10.1016/j.eswa.2018.05.018
Liu, Y.-H., & Jiang, I.-M. (2019). Optimal proportion decision-making for two stages investment. North American Journal of Economics and Finance, 48, 776–785. https://doi.org/10.1016/j.najef.2018.08.002
Macebo, L. L., Godinho, P., & Alves, M. J. (2017). Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules, Expert Systems with Applications, 79, 33–43. https://doi.org/10.1016/j.eswa.2017.02.033
Mezei, J., & Sarlin, P. 2016. Aggregating expert knowledge for the measurement of systemic risk. Decision Support Systems, 88, 38–50. https://doi.org/10.1016/j.dss.2016.05.007
Montes, G. A., & Goertzel, B. (2019). Distributed, decentralized, and democratized artificial intelligence. Technological Forecasting & Social Change, 141, 254–358. https://doi.org/10.1016/j.techfore.2018.11.010
Ortner, J., Velthuis, L., & Wollscheid, D. (2017). Incentive systems for risky investment decisions under unknown preferences. Management Accounting Research, 36, 43–50. https://doi.org/10.1016/j.mar.2016.09.001
Pourdarab, S., Nosratabadi, H. E., & Nadali, A. (2011). Risk assessment of information technology projects using fuzzy expert system. In H. Cherifi, J. M. Zain, & E. El-Qawasmeh (Eds.), Digital Information and Communication Technology and Its Applications. DICTAP 2011: Vol 166. Communications in Computer and Information Science (pp. 563–576). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_47
Qiu, S., Sallak, M., Schon, W., & Ming, H. X. G. (2018). A valuation-based system approach for risk assessment of belief rule-based expert systems. Information Sciences, 466, 323–336. https://doi.org/10.1016/j.ins.2018.04.039
Rutkauskas, A. V. (2000). Formation of adequate investment portfolio for stochasticity of profit possibilities. Property Management, 4(2), 100–115.
Rutkauskas, A. V. (2006). Adequate investment portfolio anatomy and decisions applying imitative technologies. Economics: Research Papers, 75, 52–76.
Rutkauskas, A. V., & Ostapenko, A. (2016). Return, reliability and risk as a proactive set of concepts in developing an efficient integration strategy of companies. Journal of Business Economics and Management, 17(2), 201–214. https://doi.org/10.3846/16111699.2016.1150876
Rutkauskas, A. V., & Stasytytė, V. (2011). Optimal portfolio search using efficient surface and threedimensional utility function. Technological and Economic Development of Economy, 17(2), 305–326. https://doi.org/10.3846/20294913.2011.580589
Rutkauskas, A. V., Stasytytė, V., & Rutkauskas, A. (2017, May 11–12). Reliability as main factor for future value creation. In 5th International Scientific Conference Contemporary Issues in Business, Management and Education’2017, Vilnius, Lithuania (pp. 1–11). VGTU Press. https://doi.org/10.3846/cbme.2017.075
Savage, J., Rosenblueth, D. A., Matamoros, M., Negrete, M., Contreras, L., Cruz, J., Martell, R., Estrada, H., & Okada, H. (2019). Semantic reasoning in service robots using expert systems. Robotics and Autonomous Systems, 114, 77–92. https://doi.org/10.1016/j.robot.2019.01.007
Sultana, S., Zulkifli, N., & Zainal, D. (2018). Environmental, Social and Governance (ESG) and investment decision in Bangladesh. Sustainability, 10(6), 1831. https://doi.org/10.3390/su10061831
Tam, K., Bierstaker, J. L., & Seol, I. (2006). Understanding investment expertise and factors that influence the information processing and performance of investment experts. In V. Arnold, B. D. Clinton, P. Luckett, R. Roberts, C. Wolfe, & S. Wright (Eds.), Advances in Accounting Behavioral Research (Vol. 9, pp. 113–156). Emerald Group Publishing Limited, Bingley. https://doi.org/10.1016/S1475-1488(06)09005-3
Tian, D., Yang, B., Chen, J., & Zhao, Y. (2018). A multi-experts and multi-criteria risk assessment model for safety risks in oil and gas industry integrating risk attitudes. Knowledge-Based Systems, 156, 62–73. https://doi.org/10.1016/j.knosys.2018.05.018
Wall, L. D. (2018). Some financial regulatory implications of artificial intelligence. Journal of Economics and Business, 100, 55–63. https://doi.org/10.1016/j.jeconbus.2018.05.003
Xidonas, P., Mavrotas, G., Zopounidis, C., & Psarras, J. (2011). IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection. European Journal of Operational Research, 210(2), 398–409. https://doi.org/10.1016/j.ejor.2010.08.028
Buračas, A., Rutkauskas, A. V., & Joshi, L. (2014). Metaeconomics: stochastics & nanotech: New approaches to contemporary reality. LAP LAMBERT Academic Publishing.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational intelligence and financial markets: a survey and future directions. Expert Systems with Applications, 55, 194–211. https://doi.org/10.1016/j.eswa.2016.02.006
Chojnacki, E., Plumecocq, W., & Audouin, L. (2019). An expert system based on a Bayesian network for fire safety analysis in nuclear area. Fire Safety Journal, 105, 28–40. https://doi.org/10.1016/j.firesaf.2019.02.007
Chrysafi, A., Cope, J. M., & Kuparinen, A. (2019). Eliciting expert knowledge to inform stock status for data-limited stock assessments. Marine Policy, 101, 167–176. https://doi.org/10.1016/j.marpol.2017.11.012
Fasanghari, M., & Montazer, G. A. (2010). Design and implementation of fuzzy expert system for Tehran stock exchange portfolio recommendation. Expert Systems with Applications, 37(9), 6138–6147. https://doi.org/10.1016/j.eswa.2010.02.114
García, F., González-Bueno, J., Oliver, J., & Riley, N. (2019b). Selecting socially responsible portfolios: a fuzzy multicriteria approach. Sustainability, 11(9), 2496. https://doi.org/10.3390/su11092496
García, F., González-Bueno, J., Oliver, J., & Tamošiūnienė, R. (2019a). A credibilistic mean-semivariance-PER portfolio selection model for Latin America. Journal of Business Economics and Management, 20(2), 225–243. https://doi.org/10.3846/jbem.2019.8317
García, F., Guijarro, F., Oliver, J., & 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. https://doi.org/10.3846/tede.2018.6394
Gottschlich, J., & Hinz, O. (2014). A decision support system for stock investment recommendations using collective wisdom. Decision Support Systems, 59, 52–62. https://doi.org/10.1016/j.dss.2013.10.005
Hurtado, S. M. (2010). Modeling of operative risk using fuzzy expert systems. In M. Glykas (Ed.), Fuzzy Cognitive Maps: Vol. 247. Studies in Fuzziness and Soft Computing (pp. 135–159). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03220-2_6
Yazdi, M., Hafezi, P., & Abbassi, R. (2019). A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment. Journal of Loss Prevention in the Process Industries, 58, 51–29. https://doi.org/10.1016/j.jlp.2019.02.001
Icen, D., & Gunay, S. (2019). Design and implementation of the fuzzy expert system in Monte Carlo methods for fuzzy linear regression. Applied Soft Computing, 77, 399–411. https://doi.org/10.1016/j.asoc.2019.01.029
Young, C. C., & Taib, S. M. (2009). Designing a decision support system model for stock investment strategy. In Proceedings of the World Congress on Engineering and Computer Science 2009 I, San Francisco, USA.
Jalota, H., Thakur, M., & Mittal, G. (2017) Credibilistic decision support system for portfolio optimization. Applied Soft Computing, 59, 512–528. https://doi.org/10.1016/j.asoc.2017.05.054
Kim, C., & Won, C. (2004). A knowledge-based framework for incorporating investor’s preference into portfolio decision-making. Intelligent Systems in Accounting, Finance and Management, 12(2), 121–138. https://doi.org/10.1002/isaf.248
Liang, T.-P., & Liu, Y.-H. (2018). Research Landscape of Business Intelligence and Big Data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2–10. https://doi.org/10.1016/j.eswa.2018.05.018
Liu, Y.-H., & Jiang, I.-M. (2019). Optimal proportion decision-making for two stages investment. North American Journal of Economics and Finance, 48, 776–785. https://doi.org/10.1016/j.najef.2018.08.002
Macebo, L. L., Godinho, P., & Alves, M. J. (2017). Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules, Expert Systems with Applications, 79, 33–43. https://doi.org/10.1016/j.eswa.2017.02.033
Mezei, J., & Sarlin, P. 2016. Aggregating expert knowledge for the measurement of systemic risk. Decision Support Systems, 88, 38–50. https://doi.org/10.1016/j.dss.2016.05.007
Montes, G. A., & Goertzel, B. (2019). Distributed, decentralized, and democratized artificial intelligence. Technological Forecasting & Social Change, 141, 254–358. https://doi.org/10.1016/j.techfore.2018.11.010
Ortner, J., Velthuis, L., & Wollscheid, D. (2017). Incentive systems for risky investment decisions under unknown preferences. Management Accounting Research, 36, 43–50. https://doi.org/10.1016/j.mar.2016.09.001
Pourdarab, S., Nosratabadi, H. E., & Nadali, A. (2011). Risk assessment of information technology projects using fuzzy expert system. In H. Cherifi, J. M. Zain, & E. El-Qawasmeh (Eds.), Digital Information and Communication Technology and Its Applications. DICTAP 2011: Vol 166. Communications in Computer and Information Science (pp. 563–576). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_47
Qiu, S., Sallak, M., Schon, W., & Ming, H. X. G. (2018). A valuation-based system approach for risk assessment of belief rule-based expert systems. Information Sciences, 466, 323–336. https://doi.org/10.1016/j.ins.2018.04.039
Rutkauskas, A. V. (2000). Formation of adequate investment portfolio for stochasticity of profit possibilities. Property Management, 4(2), 100–115.
Rutkauskas, A. V. (2006). Adequate investment portfolio anatomy and decisions applying imitative technologies. Economics: Research Papers, 75, 52–76.
Rutkauskas, A. V., & Ostapenko, A. (2016). Return, reliability and risk as a proactive set of concepts in developing an efficient integration strategy of companies. Journal of Business Economics and Management, 17(2), 201–214. https://doi.org/10.3846/16111699.2016.1150876
Rutkauskas, A. V., & Stasytytė, V. (2011). Optimal portfolio search using efficient surface and threedimensional utility function. Technological and Economic Development of Economy, 17(2), 305–326. https://doi.org/10.3846/20294913.2011.580589
Rutkauskas, A. V., Stasytytė, V., & Rutkauskas, A. (2017, May 11–12). Reliability as main factor for future value creation. In 5th International Scientific Conference Contemporary Issues in Business, Management and Education’2017, Vilnius, Lithuania (pp. 1–11). VGTU Press. https://doi.org/10.3846/cbme.2017.075
Savage, J., Rosenblueth, D. A., Matamoros, M., Negrete, M., Contreras, L., Cruz, J., Martell, R., Estrada, H., & Okada, H. (2019). Semantic reasoning in service robots using expert systems. Robotics and Autonomous Systems, 114, 77–92. https://doi.org/10.1016/j.robot.2019.01.007
Sultana, S., Zulkifli, N., & Zainal, D. (2018). Environmental, Social and Governance (ESG) and investment decision in Bangladesh. Sustainability, 10(6), 1831. https://doi.org/10.3390/su10061831
Tam, K., Bierstaker, J. L., & Seol, I. (2006). Understanding investment expertise and factors that influence the information processing and performance of investment experts. In V. Arnold, B. D. Clinton, P. Luckett, R. Roberts, C. Wolfe, & S. Wright (Eds.), Advances in Accounting Behavioral Research (Vol. 9, pp. 113–156). Emerald Group Publishing Limited, Bingley. https://doi.org/10.1016/S1475-1488(06)09005-3
Tian, D., Yang, B., Chen, J., & Zhao, Y. (2018). A multi-experts and multi-criteria risk assessment model for safety risks in oil and gas industry integrating risk attitudes. Knowledge-Based Systems, 156, 62–73. https://doi.org/10.1016/j.knosys.2018.05.018
Wall, L. D. (2018). Some financial regulatory implications of artificial intelligence. Journal of Economics and Business, 100, 55–63. https://doi.org/10.1016/j.jeconbus.2018.05.003
Xidonas, P., Mavrotas, G., Zopounidis, C., & Psarras, J. (2011). IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection. European Journal of Operational Research, 210(2), 398–409. https://doi.org/10.1016/j.ejor.2010.08.028