Evaluating the Asymmetric Effects of Parallel Financial Markets Shocks on Financial and Commercial Risk as well as Cash Returns
الموضوعات :
Farzin Axon
1
(
Department Of Accounting, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
)
Seyed Hossein Nasl Mousavi
2
(
Department Of Accounting, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
)
Abbas Ali Pour Aghajan
3
(
Department Of Accounting, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
)
الکلمات المفتاحية: Keywords: Gold Market, Foreign Exchange Market, Asymmetric Effects, Price Shocks , Financial Risk , Trade Risk , Stock Returns,
ملخص المقالة :
Listed companies are always affected by shocks and instabilities in parallel financial markets such as exchange rates and gold. Knowledge of how these impacts are useful for managing companies and investors to make optimal decisions regarding risk management, financing and investment. Therefore, in this study, the effect of investigating the asymmetric effects of parallel financial markets shock on stock returns and financial and commercial risk of 262 companies listed on the Tehran Stock Exchange during the period 2009-2010 using the Generalized Torque (GMM) approach. Been investigated. The results show that the negative and positive shocks of the exchange rate and the price of gold have an asymmetric effect on trade risk, finance and stock returns. These asymmetric effects apply in terms of size, sign and significance. Positive gold price shocks also have a negative effect on trade risk and a positive effect on financial risk, but these shocks do not have a significant effect on stock returns. In contrast, the impact of negative gold price shocks on financial risk is negative and market returns are positive (the impact of negative shock on trade risk is not statistically significant). Based on the above results, it can be stated that corporate operating costs and financing costs are affected by price shocks in the gold and foreign exchange markets.
[1] Alba E, Chicano JF. Training neural networks with GA hybrid algorithms. InGenetic and Evolutionary Computation Conference 2004 Jun 26 (pp. 852-863). Berlin, Heidelberg: Springer Berlin Heidelberg.Doi: 10.1007.978-3-540-24854-5_87
[2] Aljarah I, Faris H, Mirjalili S. Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing. 2018; 22:1-5. Doi:10.1007.s00500-016-2442-1
[3] Chen JF, Do QH, Hsieh HN. Training artificial neural networks by a hybrid PSO-CS algorithm. Algorithms. 2015 Jun 11;8(2):292-308.Doi: 10.3390.a8020292
[4] Familian, Moloud., Identifying the price bubble of Tehran Stock Exchange with systems dynamics approach, Third National Conference on Accounting and Management, Tehran, Narkish Information Institute،https:..civilica.com.doc.343172 (in Persian).
[5] Wan C, Xiao Z. Idiosyncratic volatility, expected windfall, and the cross-section of stock returns. InEssays in Honor of Peter CB Phillips 2014 Nov 21 (Vol. 33, pp. 713-749). Emerald Group Publishing Limited.Doi: 10.1108.S0731-905320140000033020
[6] Kongsilp W, Mateus C. Volatility risk and stock return predictability on global financial crises. China Finance Review International. 2017 Feb 20;7(1):33-66. Doi: 10.1108.CFRI-04-2016-0021
[7] Faris H, Aljarah I, Al-Madi N, Mirjalili S. Optimizing the learning process of feedforward neural networks using lightning search algorithm. International Journal on Artificial Intelligence Tools. 2016 Dec 28;25(06): 1650033. Doi: 10.1142.S0218213016500330(in Persian).
[8 Hsiao, Cheng. Analysis of panel data. No. 64. Cambridge university press, 2022.
[9] Im KS, Pesaran MH, Shin Y. Testing for unit roots in heterogeneous panels. Journal of econometrics. 2003 Jul 1;115(1):53-74. Doi: 10.1016.S0304-4076(03)00092-7
[10] Heidari AA, Faris H, Aljarah I, Mirjalili S. An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Computing. 2019 Sep 1; 23: 7941-58. Doi: 10.1007.s00500-018-3424-2
[11] Heidari AA, Faris H, Mirjalili S, Aljarah I, Mafarja M. Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. Nature-Inspired Optimizers: Theories, Literature Reviews and Applications. 2020:23-46. Doi: 10.1007.978-3-030-12127-3_3(in Persian).
[12] Khodayari MA, Yaghobnezhad A, Khalili Eraghi KE. A Neural-Network Approach to the Modeling of the Impact of Market Volatility on Investment. Advances in Mathematical Finance and Applications. 2020 Oct 1;5(4):569-81. Doi: 10.22034.amfa.2020.674953(in Persian).
[13] Kumar G, Jain S, Singh UP. Stock market forecasting using computational intelligence: A survey. Archives of computational methods in engineering. 2021 May; 28: 1069-101.Doi: 10.1007.s11831-020-09413-5
[14] Li WX, Chen CC, French JJ. Toward an early warning system of financial crises: What can index futures and options tell us? The Quarterly Review of Economics and Finance. 2015 Feb 1; 55: 87-99. Doi: 10.1016.j.qref.2014.07.004
[15] Ferderer JP. Oil price volatility and the macroeconomy. Journal of macroeconomics. 1996 Dec 1;18(1):1-26. Doi: 10.1016.S0164-0704(96)80001-2
[16] Chen J, Hong H, Stein JC. Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices. Journal of financial Economics. 2001 Sep 1;61(3):345-81. Doi: 10.1016.S0304-405X(01)00066-6
[17] Saremi S, Mirjalili S, Lewis A. Grasshopper optimisation algorithm: theory and application. Advances in engineering software. 2017 Mar 1; 105: 30-47. Doi: 10.1016.j.advengsoft.2017.01.004(in Persian).
[18] Amores VJ, Benítez JM, Montáns FJ. Average-chain behavior of isotropic incompressible polymers obtained from macroscopic experimental data. A simple structure-based WYPiWYG model in Julia language. Advances in Engineering Software. 2019 Apr 1; 130:41-57. Doi: 10.1016.j.advengsoft.2019.01.004
[19] Tone K, Toloo M, Izadikhah M. A modified slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research. 2020 Dec 1;287(2):560-71. Doi: 10.1016.j.ejor.2020.04.019
[20] Al Rahahleh N, Adeinat I, Bhatti I. On ethnicity of idiosyncratic risk and stock returns puzzle. Humanomics. 2016 Feb 8;32(1):48-68. Doi: 10.1108.H-06-2015-0043
[21] Faris H, Aljarah I, Al-Madi N, Mirjalili S. Optimizing the learning process of feedforward neural networks using lightning search algorithm. International Journal on Artificial Intelligence Tools. 2016 Dec 28;25(06):1650033. Doi: 10.1142.S0218213016500330. (in Persian).
[22] AfruozianAzar A, Rezaei N, Hajiha Z, Pakmaram A. Optimal Banking Performance Model based on ERM. Advances in Mathematical Finance and Applications. 2023 Jan 1;8(1):273-85.Doi: 10.22034.AMFA.2020.1900625.1435(in Persian).
[23] Morovat S, Baghfalaki A. The Relationship between Risk and Return on Financial Assets (The Panel Vector Auto-Regression and Panel Cointegration Ap-proaches). Advances in Mathematical Finance and Applications. 2022 Jul 1;7(3):695-714.Doi:10.22034.AMFA.2020.1885620.1343.
[24] Zhang Y, Zheng X. A study of herd behavior based on the Chinese stock market. Journal of Applied Management and Investments. 2016;5(2):131-5. Doi: journl:v:5:y:2016:i:2:p:131-135
