A Neural-Network Approach to the Modeling of the Impact of Market Volatility on Investment

Subject Areas : Financial Mathematics

Mohammad Azim Khodayari ¹ , Ahmad Yaghobnezhad ² , Khalili Eraghi Khalili Eraghi ³

1 - Department of financial management Science and Research branch, Islamic Azad University, Tehran, Iran
2 - Department of Economic And Accounting, Islamic Azad University of Central Tehran Branch, Tehran, Iran
3 - Department of Economic And Management Science and Research branch, Islamic Azad University, Tehran, Iran

Received: 2020-08-23 Accepted : 2020-08-23 Published : 2020-10-01

Keywords:

References:

[1] AraúJo, R., Ferreira, T., A morphological-rank-linear evolutionary method for stock market prediction, Inform. Sci.2013, 237, P.3–17.

 

[2] Barunik, J., Krehlik, T., Vacha, L., Modeling and forecasting exchange rate volatility in time-frequency domain, European Journal of Operational Research, 2016, 251, P.329-340.

 

[3] Bishop, C.M., Neural Networks for Pattern Recognition, Oxford University press, 1996.

 

[4] Boyacioglu, M., Avci, D., An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange, Expert Syst. Appl, 2010, 37, P.7908–7912.

 

[5] Charles, A., The day-of-the-week effects on the volatility: The role of the asymmetry, European Journal of Operational Research, 2010, 20, P.143-152.

 

[6] Dargahi, H., Ansari, R., Improvement of the modeling of neural networks in predicting exchange rate by applying turbulence indexes, Quarterly Journal of Economic Research, 2008, 85, P.144-117. (in Persian).

 

[7] Degiannakis, S., Filis, G., Hassani, H., forecasting global stock market implied volatility indices, Journal of Empirical Finance, march 2018, 46, P.111-129, Doi: 10.1016j.jempfin.2017.12.008

[8] Davalu, M., Heydari, T., Projection of the stock index using artificial neural network and ultra-fast modeling of harmonic search and genetic algorithm, Journal of Financial Economics, Islamic Azad University, Tehran University, Tehran, 2017, 11(40), P.1-24. (in Persian).

 

[9] Ghasemi, A., Nazari, S., investigating the role of financial deepening on macroeconomic turbulence, Journal of Financial Economics, Islamic Azad University, Tehran Branch, 2016, 10(35), P.27-44. (in Persian).

 

[10] Hakkio, CS., Keeton, WR., Financial Stress: What Is It, How Can It Be Measured, and Why Does It Matter? , 2009, Available: http://ideas.repec.org/a/fip/fedker/y2009iqiip5-50nv.94no.2.html.

 

[11] Illing, M., Liu, Y., Measuring Financial Stress in a Developed Country: An Application to Canada, Journal of Financial Stability, 2006, 2(3), P.243-265.

 

[12] Izadikhah, M., Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model, Advances in Mathematical Finance and Applications, 2018, 3(2), P.27-41.

 Doi: 10.22034/amfa.2018.540829

 

[13] Kalyani, D., Causal Modeling of Stock Market Prices using Neural Networks and Multiple Regression: A Comparison Report, Finance India, 2007, 21(3), P.923-930.

 

[14] Kristjanpoller, W., Minutolo, C., Expert system with applications: gold price volatility: A Fore casting approach using the artificial neural network-gharch model, 42(20),15 november 2015, P.7245-7251

 Doi: 10.1016j.eswa.2015.04.058

 

[15] Mittnik, S., robinzonov, N., Spindler, M., stock market volatility: identifying major drivers and the nature of their impact Journal of banking & Finance, 2015, 58, P.1-14, Doi: 10.1016j.jbankfin.2015.04.003

 

[16] NikooMaram, H., PourZamanni, Z., Dehghan, A., Influence of turbulence in Iranian capital market, Quarterly Journal of Investment Knowledge, 2014, 11, P.179-199. (in Persian).

 

[17] Oet, M.V., Eiben, R., Bianco, T., Gramlich, D., Ong, S.J., The Financial Stress Index: Identification of Systemic Risk Conditions, Federal Reserve Bank of Cleveland, 2011, Working Paper 11-30.

 

[18] Perez. E., Alonso-Gonzalez, P., Nunez-velaquez J.Expert systems with applications: Forecasting volatility with a stacked model based on a hybridized artificial neural network, 1 september 2019, 129, P.1-9.

Doi: 10.1016j.eswa.2019.03.046

 

[19] Ripley, B.D., Pattern Recognition and Neural Networks, Cambridge University Press, 1996.

 

[20] Roye, BV. Financial Stress and Economic Activity in Germany and the Euro Area, 2011.

 

[21] Roye, BV. Financial Stress and Economic Activity in Germany, 2012.

 

[22] Sharda, R., Patil, R., Neural Networks as forecasting experts: an empirical test, Proceedings of the 1990 International Joint Conference on Neural Networks, 1960, 1, P.491-494, Washington DC, USA.

 

[23] Tang, A., Fishwick, S., Time series forecasting using neural networks vs. Box-Jenkins methodology, November 1991, P.303- 310.

 

[24] Tung, W.L., Quek.C. Expert system with applications: Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach, 2011, 38(5), P. 4668-4688.

Doi: 10.1016/j.eswa.2015.04.058

 

[25] Ghazi Fini, S.R., Panahian, H., Modelling and Investigating the Differences and Similarities inthe Volatility of the Stocks Return in Tehran Stock Exchange Using the Hybrid Model PANEL-GARCH, Advances in Mathematical Finance and Applications, 2018, 3(2), P. 13-26. Doi: 10.22034/amfa.2018.540828

 

[26] Nasr, N., Farhadi Sartangi, M., Madahi, Z. A Fuzzy Random Walk Technique to Forecasting Volatility of Iran Stock Exchange Index, Advances in Mathematical Finance and Applications, 2019, 4(1), P.15-30.

Doi: 10.22034/amfa. 2019.583911.1172

 

[27] Sarlak, A., Mohamadtalebi, M., Mohammadtalebi, B., The Effect of Liquidity and Credit Risk on the Relationship between Business Activities and Fluctuations in the Price of all Companies Listed on the Tehran Stock Exchange, Advances in Mathematical Finance and Applications, 2017, 2(1), P. 69-81.

 Doi: 10.22034/amfa. 2017.529062