ORIGINAL_ARTICLE
The Effects of Monetary and Fiscal Policies on the Systemic Risk of Iran's Financial Markets (SURE Approach in Panel Data)
The mutual relationship between monetary and fiscal policies and value at risk is one of the most important topics in the financial economics literature and accounts for the vast majority of empirical studies. Therefore, the main objective of this paper is to investigate the effects of monetary and fiscal policies on conditional value at risk in the financial sectors of the stock exchange, bank and insurance during the years 1995-2017. For this purpose, by quantile regression method and in the form of Adrian and Brunnermeier approach, the conditional value at risk of these three financial sectors is estimated and then by using the seemingly unrelated regression equation approach in panel data evaluated the effect of liquidity money variables. The interest rate on facility payments, the real exchange rate, the government's budget deficit, real GDP growth, and the degree of economic openness are subject to conditional risk. The results of the model estimation indicate the significance of the effect of liquidity money, interest rate on facility payments and real exchange rate variables on conditional value at risk in each of three relevant equations, and real GDP growth variable in the model, Exposure to the conditional value at risk of the insurance sector has a negative and significant effect. Also, the degree of openness of the economy in any of the three estimated equations has no significant effect on the conditional value at risk.
https://www.ijfifsa.ir/article_113332_8704c54d1c83a33e437c0f18f4161d95.pdf
2020-01-01
1
24
10.22034/ijf.2020.230256.1123
Monetary and Fiscal Policies
systemic risk
Financial Markets
Iran
SURE in Panel Data Approach
Neda
Ranjandish
neda.ranjandish@gmail.com
1
Ph.D. Candidate, Department of Economics, Central branch of Tehran, Islamic Azad University, Tehran, Iran.
AUTHOR
Marjan
Damankeshideh
m.damankeshideh@yahoo.com
2
Assistant Prof., Department of Economics, Central branch of Tehran, Islamic Azad University, Tehran, Iran.
LEAD_AUTHOR
Houshang
Momeni Vesalian
hoshang.momeni@gmail.com
3
Assistant Prof., Department of Economics, Central branch of Tehran, Islamic Azad University, Tehran, Iran.
AUTHOR
Majid
Afsharirad
majidfeshari@gmail.com
4
Associate Prof., Department of Economics, Kharazmi University, Tehran, Iran.
AUTHOR
Abu Nouri Ismail, Saeed Karimi Potanlar and Mohammad Reza Mardani (2008), The effect of financial policy on macroeconomic variables in Iran.No.3,pp.117-143.
1
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AlOmran and AlOmran Roya (2012), Tehran Stock Exchange fluctuation trend Quarterly Journal of Financial Knowledge Securities Analysis. No. 14, pp. 132-119.
4
Azeri Qara Lor, A. (2016), Comparison of Systematic Risk Measurement Approaches in Tehran Stock Exchange Companies. Master Thesis in Financial Engineering. Faculty of Finance, Kharazmi University.
5
Bernal, O., Gnabo, J., & Guilmin, G. (2017). Assessing the contribution of banks, insurance and other financial services to systemic risk. Journal of Banking and Finance, 47(10): 270–287.
6
Brownlees, C. T., Engle, R. (2012). Volatility, correlation and tails for systemic risk measurement. Available at: SSRN 1611229. 1-55.
7
Brunnermeier, M., Adrian, T. (2011). CoVaR. NBER Working Paper No. w17454. Available at SSRN: https://ssrn.com/abstract=1939717H .;
8
Danesh Jafari, d. (2016), Ranking of Banks in Resistance to Systemic Risk in Resistance Financial Financial System (Quantile Regression Method and dynamic Conditional Solidarity. Basij Strategic Studies, 72 (19), 99-79.
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Ebrahimi, S. B., Seyed Hosseini, S. M. (2015). Robust M-estimation of multivariate FIGARCH models for handling volatility transmission: A case study of Iran, United Arab Emirates and the global oil price index. Scientia Iranica. Transaction E, Industrial Engineering, 22 (3), 12–18.
10
Elyasiani, E., Kalotychou, E., Staikouras, S. K., Zhao, G. (2015). Return and Volatility Spillover among Banks and Insurers: Evidence from Pre-Crisis and Crisis Periods. Journal of Financial Services Research, 48 (1), 21–52.
11
Farzinoush, A, Elahi, Nasser, Gilanipoor, Javad and Mahdavi, Ghadir (2017), Systematic risk assessment in the Iranian banking network by the criterion of changes in value under conditional risk, Quarterly Journal of Financial Engineering and Securities Management, 8 ( 33): 281-265
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16
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17
Liow, K. H. (2015). Volatility spillover dynamics and relationship across G7 financial markets. The North American Journal of Economics and Finance, 33, 328–365.
18
Mahdavi Kaleshmi Ghadir, Elahi Nasser, Farzin Vash Asadaleh, Gilanipour Javad (2017), System Risk Assessment in Iran Banking Network by Value Change Criteria Risk of Financial Engineering Magazine and Securities Management No. 33 / Winter 2017
19
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20
Mohammadi Aghdam Saeed, Ghavam Mohammad Hossein, Mirfeiz Fallah Shams Mirfeiz (2017), Systematic Risk Assessment due to Currency Shock in Iran's Financial Markets Financial Research, Faculty of Management, University of Tehran, Volume 19, Number 3, Fall 2017 504-475
21
Mouna, A., Anis, J. (2016). Market, interest rate, and exchange rate risk effects on financial stock returns during the financial crisis: AGARCH-M approach. Cogent Economics & Finance, 4 (1), 1-16.
22
Muradmand Jalali, M. (2015), Evaluation of the share of banks, insurance and investment companies in systemic risk. Master Thesis in Financial Engineering. Faculty of Industrial Engineering, Khatam University.
23
Noor Ali Dokht, S. (2016), Resistance to Delayed Transmission in Financial Networks. Master Thesis. School of Mathematics and Computer. Zanjan University of Basic Sciences
24
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25
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26
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27
Salmani Bishak Mohammad Reza, Barghi Eskouei Mohammad Mehdi, Lak Soda (2015), The Impact of Monetary and Financial Policy Shocks on the Iranian Stock Market Quarterly Journal of Economic Modeling Research No. 22 Winter 94
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33
ORIGINAL_ARTICLE
Identifying path of Global Financial Crisis Contagion Direction on Industries of Iran Stock Market
Simultaneous understanding of volatilities and changes in financial markets is very important to optimize the portfolio and risk management methods. The 2008 financial crisis led into devaluation of most assets, increased volatilities and endangered several institutional investors' survival. When the stock market' correlation is highly enhanced, risk and return management with the classic portfolio theory becomes severely challenging. In this study, to manage systematic and non-systematic risks by investors and policymakers in case of similar financial crises, the Effect of global financial crisis contagion is examined through the path of S&P500 global index, and DFM regional index of different industries of Iran Stock Market is examined using DFGM contagion test and stochastic Ornstein Uhlenbech process. The results show that Dubai Stock Market has an important role in crisis expansion into different sectors of Iran Stock Markets so that the fundamental contagion effects are channelled via this direction. Also, according to the results, the starting point of the global financial crisis contagion was the basic metals industry, and the contagin happened in metal ores and petroleum products sectors with different rates. Finally, the global financial crisis is spread into different industries of Iran Stock Market via financial links and not trough commercial ones. Identifying the direction of contagion of financial crisis provides an opportunity for investors to apply hedging and asset allocation strategies optimally.
https://www.ijfifsa.ir/article_113333_06f81083ad7db0069601cf289d3f5c7c.pdf
2020-01-01
25
54
10.22034/ijf.2020.234212.1134
Stock market
Global Financial Crisis
DFGM contagion test
Ornstein- Uhlenbeck process
Mojtaba
Karimi
tabakarimi622@gmail.com
1
PhD Candidate, Department of Finance, South Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Fatemeh
Sarraf
aznyobe@yahoo.com
2
Assistant Prof., Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Ghodratollah
Emamverdi
ghemamverdi@iauctb.ac.ir
3
Assistant Prof., Department of Economics, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
LEAD_AUTHOR
Ali
Baghani
ali.baghni.58@gmail.com
4
Assistant Prof., Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Abed's Moghabere, Sepehr; Keshavarz Haddad, Gholamreza, (2012), "Did the Global Financial Crisis Spread to the Tehran Stock Market?", Journal of Economic Research, No. 2, 2013.
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58
ORIGINAL_ARTICLE
Auditors’ Behavioral Intention: the Interaction Effect of Individual, Audit Firm and Audit Team Factors
Breakdown of reporting detected misstatements can cause serious problems because it reflects poor audit quality and can lead audit firm to failures. Due to the magnitude of the quality of auditors’ work, many studies have attempted to identify influencing factors on auditors’ intention to act ethically. This study ascertains how external auditors decide to report the detected misstatements in terms of their individual characteristics, ethical culture and team norms according to the theory of planned behavior. Data are collected using 257 survey questionnaires which are distributed among audit seniors. Statistical analyses indicate that ethical culture and team norm moderate the influence of individual factors on auditors’ intention of reporting misstatements. In fact, the association between locus of control, personality type and auditors’ work quality moderate by audit firm ethical culture and team norms.
https://www.ijfifsa.ir/article_113331_2d5a37b02e04bf002f04edee99a7c02e.pdf
2020-01-01
55
80
10.22034/ijf.2020.222342.1113
Behavioral Intention
Ethical Culture
Locus of control
Personality type
Reporting Misstatements
Team Norm
Theory of Planned Behavior
Arezoo
Aghaei Chadegani
arezooaghaei@phu.iaun.ac.ir
1
Assistant Prof., Department of Accounting, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
LEAD_AUTHOR
Khadijeh
Ebrahimi Kahrizsangi
ebrahimi641@yahoo.com
2
Assistant Prof., Department of Accounting, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
AUTHOR
Ab Ghani, N., J. Galbreath, and R. Evans. 2011. “Predicting Whistle-Blowing Intention among Supervisors in Malaysia”, Journal of Global Management, 3, 1, 1-18.
1
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2
Ahmad, Z., and D. Taylor. 2009. “Commitment to independence by internal auditors: The effects of role ambiguity and role conflict”, Managerial Auditing Journal, 24, 9, 899-925.
3
Ahmar Ahmad, S., Smith, M. and Z. Ismail. 2013. “Internal whistle blowing intentions by internal auditors: A prosocial behaviour perspective”. Malaysian Accounting Review, 12, 1, 145-181.
4
Ajzen, I. 1991. “The Theory of Planned Behaviour”. Organisational Behaviour and Human Decision Processes, 50, 179-211.
5
Ajzen, I., and M. Fishbein. 1980. “Understanding attitudes and predicting social behaviour”. London: Englewood Cliffs, Prentice-Hall.
6
Akpotu, C., and O. Israel. 2013. “External Auditors’ Unethical Behaviour and Corporate Business”. International Journal of Business and Management Invention, 2, 4, 12-18.
7
Alaniz-Bouqayes, N., Chiu, S., Gupta, G. and S. Slom. 2012. “The Role of Professional Scepticism, National Culture, and National Legal Enforcement on Accountant’s Intentions to Report Fraudulent Behaviour”. American Accounting Association Annual Meeting and Conference on Teaching and Learning Accounting.
8
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9
Alleyne, P. A. 2010. “The influence of individual, team and contextual factors on external auditors' whistle-blowing intentions in Barbados: towards the development of a conceptual model of external auditors' whistle-blowing intentions”. PhD Dissertation, University of Bradford, Barbados.
10
Alleyne, P. A., M. Hudaib, and R. Haniffa. 2012. “A Model of Whistle-blowing Intentions among External Auditors”. Accounting & Finance Association of Australia and New Zealand conference.
11
Alleyne, P. A., M. Hudaib, and R. Pike. 2013. “Towards A Conceptual Model of Whistle-blowing Intentions among External Auditors”. The British Accounting Review, 36, 4, 345-368.
12
Alleyne, P. and M. Lavine. 2013. “Factors influencing accountants' behavioural intentions to use and actual usage of enterprise resource planning systems in a global development agency”. Journal of Financial Reporting and Accounting, 11, 2, 179-200.
13
Ampofo, A., B. Mujtaba, F. Cavico, and L. Tindall. 2004. “Organisational Ethical Culture: A Significance Determinant of Ethical Behaviour”. Retrieved on July 24 from http://www.aaahq.org/AM2004/cpe/ Ethics/Forum_2001.pdf.
14
Ampofo, A. A. 2004. “An empirical investigation into the relationship of organisational ethical culture to ethical decision making by accounting/finance professionals in the insurance industry in the U.S.A”. P.h.D Dissertation, Nova Southeastern University.
15
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17
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18
Baker, R. L., W. Bealing, D. Nelson, and A. B. Staley. 2006. “An institutional perspective of the Sarbanes-Oxley Act”. Managerial Auditing Journal, 21, 1, 23-33.
19
Bansal, H. S., and S. F. Taylor. 2002. “Investigating interactive effects in the theory of planned behaviour in a service-provider switching context”. Psychology and Marketing, 19, 407-425.
20
Baotham, S. 2009. “Audit independence, quality, and credibility: effects on reputation and sustainable success of CPAs in Thailand”. International Journal of Business Research, 9, 1.
21
Beck, L., and I. Ajzen. 1991. “Predicting Dishonest Actions using the Theory of Planned Behaviour”. Journal of Research in Personality, 25, 285-301.
22
Bettenhausen, K. L. and J. K. Murnighan. 1991. “The development of an intragroup norm and the effects of interpersonal and structural challenges”. Administrative Science Quarterly, 36, 20-35.
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DeAngelo, L. E. 1981. “Auditor size and audit quality”. Journal of Accounting and Economics, 3, 3, 183-199.
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29
Dowling, C. 2007. “The compensatory and complementary influence of auditor, audit team and firm-level factors on how audit support systems are used”. PhD Dissertation, Department of Accounting and Business Information Systems, The University of Melbourne.
30
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77
ORIGINAL_ARTICLE
Risk disclosure, stability and the economic consequences in the banking system
Shareholders in the capital market always demand Reporting and disclosure and based on information that disclosure; they change their expectations of risk and returns. Disclosure has an economic consequence and the risk disclosure, in addition to economic consequences, has an effect on financial and banking stability. In this paper, we survey the risk disclosure of economic consequences and its effect on banking stability. We count the number of the risk disclosures in Iranian banks' financial statements by using the quantitative content analysis methodology and indexation of Iran's risk disclosure regulation. According to the estimation of panel data from 18 banks to period 2011-2016, we find that risk disclosure has a negative and significant relationship with stability and a positive and significant relationship with the cost of capital.
https://www.ijfifsa.ir/article_113329_1152250f4ddb99a8ec48e300c2f132e3.pdf
2020-01-01
81
104
10.22034/ijf.2020.179085.1019
Risk disclosure
Stability
Economics consequences
content analysis
The Banking system
Ali
Rahmani
rahmani@alzahra.ac.ir
1
Prof., Faculty of Social Sciences and Economics, University of Alzahra, Tehran, Iran.
AUTHOR
Gholamreza
Solimani
soleymani@alzahra.ac.ir
2
Associate Prof., Faculty of Social Sciences and Economics, University of Alzahra, Tehran, Iran.
AUTHOR
Mandana
Taheri
taherim66@yahoo.com
3
Assistant Prof., Faculty of Management and Accounting, University of Allameh Tabatabai, Tehran, Iran.
LEAD_AUTHOR
Abraham, S., & Cox, P. (2007). Analysing the determinants of narrative risk information in UK FTSE 100 annual reports. The British Accounting Review, 39(3), 227-248.
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4
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6
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56
ORIGINAL_ARTICLE
Designing and Investigating the Profitability of Fuzzy Inference Trading System based on Technical Signals and Corrective Property
Technical analysis is constituted as an approach in the market analysis which is based on the study of pricing behavior and shares size in the past and price determination and its procedure in the future. Algorithmic transactions are growing rapidly in order to automate business strategies, given the arrival of computer-based technologies and the rapid processing of bulky information. Trading systems combine input information and ultimately identify the time of purchase and sale by forming one signal. In this paper, the training system is a kind of fuzzy inference system that combines fuzzified RSI and SO signals from technical analysis. The system’s trade rules database (selling, buying, and holding) would be calculated based on an optimization process using PSO. This optimization process should be repeated at certain intervals to keep the system up to date. This process is called the corrective property of systems. The findings on the overall index in the period 2001/3/21-2019/3/20 indicate that the system having optimized training on training data has an average daily return of /0027, risk-taking of /0065 and the daily sharp ratio of /42. Concerning the index of return and sharp ratio, the findings reveal that the system outperforms the signals and the market performance.
https://www.ijfifsa.ir/article_113330_48f2101282d8bc96633f6d5a4139ea85.pdf
2020-01-01
105
123
10.22034/ijf.2020.214880.1103
Corrective property
Fuzzy inference system
Oscillators
PSO
CharaghAli
Bakhtiyariasl
bakhtiyaricharaghali@gmail.com
1
PhD Candidate of Financial Engineering, Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.
AUTHOR
Sayyed Mohammad Reza
Davoodi
smrdavoodi@dehaghan.ac.ir
2
Assistant Prof., Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.
LEAD_AUTHOR
Abdolmajid
Abdolbaghi Ataabadi
abdolbaghi@shahroodut.ac.ir
3
Assistant Prof of Finance, Department of Management, Shahrood University of Technology, Shahrood, Iran.
AUTHOR
Alejandro, E., Juliano, M. & Sebastian, M. (2013). A Technical Analysis Indicator Based on Fuzzy Logic. Journal of Electronic Notes on Theoretical Computer Science, 8(3): 27-29.
1
Abbasi, I., Akefi, H. & Adibmehr, S. (2015). Parameter setting of technical analysis indicators using multi-objective particle swarm optimization and adaptive fuzzy inference system. Journal of Investment Knowledge, 4(15): 111-134. (in Persian)
2
Brown, C. (2018). The Composite Index: A Divergence Analysis Study. IFTA Journal, 15(1): 25-34.
3
Fallahpour, S. & Hakimian, H. (2017). Evaluating the Performance of a Pairs Trading System in Tehran Stock Exchange: the Cointegration Approach and Sortino Ratio Analysis. Journal of Financial Engineering and Securities Management, 8(30): 1-17. (in Persian)
4
Ijegwa, A. D., Rebecca, V. O., Olusegun, F., & Isaac, O. O. (2014). A predictive stock market technical analysis using fuzzy logic. Journal of Computer and information science, 7(3): 1-17.
5
Hirabayashi, A., Aranha, Cl. & Iba, H. (2009). Optimization of the trading rule in foreign exchange using a genetic algorithm. Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009. 1529-1536. 10.1145/1569901.1570106.
6
Lim, S., Yanyali, S. & Savidge, J.(2016).Do Ichimoku Cloud Figures Work and Do They Work Better in Japan?. IFTA Journal, 13(1):1-7.
7
Magda B.Fayek., Hatem M.El-Boghdad.& Sherin M.Omran.(2013).Multi-Objective Optimization of technical stock market indicators using GAS. International Journal of Computer Applications, 68(20):41-48.
8
Molaee, B., Nikokar, S., Nikokar, F. & Khosravani, F (2015). Evaluation of Price Momentum Strategy in Tehran Stock Exchange. Paper presented at the International Conference on Management, Economics and Industrial Engineering, Tehran.
9
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin.
10
Nabavi Chashami, S. A., Ayatollah, H. (2011). Investigation of MA Index Efficiency in Technical Analysis in Stock Price Forecasting. Journal of Financial Knowledge of Securities Analysis 4(10): 83-106. (in Persian)
11
Naranjo, R. & Santos, M. (2017). Fuzzy Candlesticks Forecasting Using Pattern Recognition for Stock Markets. Journal of Intelligent Systems and Computing, 527(2): 323-333.
12
Nasrollah, K. S. K., Samadi R. S. & Prez Barzani, M.(2013). Evaluating the usefulness of Japanese candle patterns in Tehran Stock Exchange. Journal of Financial Accounting Research, 3(17): 59-72. (in Persian)
13
Simutis, R. (2000). Fuzzy logic based stock trading system. Paper presented at the Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr)(Cat. No. 00TH8520).
14
Sherbini, A.(2018).Time cycle oscillators.IFTA Journal, 15(1): 66-84.
15
Theodorus, Z. & Dimitrus, K. (2013). Short Term Prediction of Foreign Exchange Rates with a Neural-Network Based Ensemble of Financial Technical Indicators. International Journal on Artificial Intelligence Tools, 22(3): 220-241.
16
Tadi, M., Abkar, M. & Motahari Nia, V. (2018). Evaluation of paired trading strategy with a distance approach in Tehran Stock Exchange. Journal of Investment Knowledge, 7(26): 99-112. (in Persian)
17
Volna, E., Kotyrba, M. & Jarusek, R. (2013). Multi-classifier based on Elliott wave’s recognition. Journal of Computers & Mathematics with Applications, 66(1): 213–225.
18
Wang, F., Yu, P. & Cheung, D. (2016). Combining technical trading rules using particle swarm optimization. Journal of Expert Systems with Applications, 41(6): 3016-3026.
19
Zhou, X. S. & Don, M. (2004). Can fuzzy logic make technical analysis 20/20? Financial analyst journal, 60(4): 54-73.
20
ORIGINAL_ARTICLE
Dynamic relationships between financial conditions index and stock returns
Stock return predictability has been extensively considered as a stylized reality. Theories indicate that returns should change along the time, and various studies have presented evidence on this point. On the other hand, there is an optimal portfolio in each regime, and one cannot claim that a specific portfolio can minimize risk and returns in each regime. On the other hand, the financial conditions index (FCI) is an important index to specify monetary policy conditions. Regarding the importance of the issue, this research aims to present a comprehensive index, including all monetary transmission mechanisms. In this regard, it is attempted to improve the efficiency of stock return predictability in Iran's economy by incorporating an FCI and identifying relationships between FCI and stock returns using the TVP-DMA model, which can resolve shortcomings of traditional models. The study is applied research in terms of purpose. Seasonal data over the period of April 1991 to July 2019 is used. The results based on TPV, DMS, and DMA models indicate that liquidity growth rate, economic growth rate, unemployment rate, exchange rate, financial condition index, oil revenues, misery index, and budget deficit, has significantly affected factors of stock returns in 30, 50, 11, 49, 66, 54, 7, and 84 periods of 104 periods, respectively. Accordingly, budget deficit, financial condition index, oil revenues, and economic growth are the most effective factors of stock returns predictability in Iran. Further, the incorporation of flexibility in coefficients of the financial development index leads to higher forecast accuracy.
https://www.ijfifsa.ir/article_113334_ac4dad0386aa749aff59d6990c1cb91a.pdf
2020-01-01
124
145
10.22034/ijf.2020.234560.1137
Financial development index
Monetary conditions index
Stock Returns
TVP-DMA
Amin
Sadat
aminsadat1363@gmail.com
1
Ph.D. Candidate of Department of Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
AUTHOR
Ebrahim
Abbasi
abbasiebrahim2000@alzahra.ac.ir
2
Prof., Department of Finance and Insurance, Faculty of Management, Alzahra University, Tehran, Iran.
LEAD_AUTHOR
Hasan
Ghalibaf Asl
h.ghalibafasl@alzahra.ac.ir
3
Prof., Department of Finance and Insurance, Faculty of Management, Alzahra University, Tehran, Iran.
AUTHOR
Atrkar roshan Sedigheh, Mahboobi Motahareh (2016), Extracting Financial Condition Index for Iran, Journal of Economic Modeling Research,147-174.
1
Barberis, Nicholas, Thaler, Richard. (2003). “A Survey of Behavioral Finance”, Handbook of the Economics of Finance, Elsevier Science B.V.
2
Bovin J.; Kiely, M. and Mishkin, F. (2010); How Has the Monetary Transmission Mechanism Evolved?, Finance and Economics Discussion Series, Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington, D. C.
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D.j. Poirier. Intermediate Statistics and Econometrics, MIT Press, Cambridge 1995.
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