ORIGINAL_ARTICLE
Modeling the prediction of the Financial Behavior in Iranian Stock Market Investors with an Interpretive Structural Approach
Nowadays, predicting the financial behavior of investors plays a crucial role in decision-making and the financial policy-making process. This study is aimed at providing a paradigm to predict the financial behavior of investors in Iran’s stock market. 24 experts were interviewed to identify the variables, and 24 variables were identified. The interpretive structural paradigming was carried out using a self-interaction matrix based on the experts’ opinions. The MICMAC analysis has been used to identify the types of the variables. As findings of the study, a five-level paradigm was determined, in which environmental factors and the background of financial behavior on the fifth level were the most influential variables and also arbitrage, bias, and the perceptual mistake were the most impressible variables of the paradigm on the first level. MICMAC analysis of this study suggested that the variable of environmental factors had low dependence and high efficacy. Furthermore, psychological projection, perceptual mistake, arbitrage, and bias are dependent variables with high dependence and low efficacy. Other variables are mediator variables with high dependence and effectiveness.
https://www.ijfifsa.ir/article_101106_bbe35ea51b815c3e3b747fa9765e0f07.pdf
1999-12-01
1
26
10.22034/ijf.2019.201765.1069
prediction
financial behavior
Investors
Iran’s Stock Market
Decision-making
Fatemeh
Ahmadi
fatemehahmady60@yahoo.com
1
Ph.D. Candidate, Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
AUTHOR
Mehrdad
Ghanbari
ghanbari@iauksh.ac.ir
2
Assistant Prof., Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
LEAD_AUTHOR
Babak
Jamshidi Navid
jamshidinavid@iauksh.ac.ir
3
Assistant Prof., Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
AUTHOR
Shahram
Mami
shahram.mami@yahoo.com
4
Assistant Prof., Department of Psychology, Ilam Branch, Islamic Azad University, Ilam, Iran.
AUTHOR
Ameli, Ahmad, and Ramazani, Malihe (2015). Stock Price Forecasting Using Genetic Algorithm Based on Fuzzy Neural Network and Comparison with Fuzzy Neural Network, Journal of Economic Paradigming Research, No. 22, pp. 61-91.
1
Barasinska, Nataliya, (2017), Essys on determinants of financial behavior of individuals, Phd thesis Freie University of Berlin.
2
Charan, P., Shankar, R. and Baisya, R.K., (2008). Analysis of interactions among the variables of supply chain performance measurement system implementation. Business Process Management Journal,14(4), pp.512-529.
3
Da Costa Jr., N., Goulart, M., Cupertino, C., Macedo Jr., J., Da Silva, S., (2013), “The disposition effect and investor experience”, Journal of Banking & Finance, Volume 37, Issue 5, Pages 1669-1675
4
Darabi, Roya, and Chenari Bouqet, Hassan (2015). Dimensions and Approaches of Financial Behavioral Theory, Journal of Humanities, Allameh Tabataba'i University, No. 46, pp. 78-103.
5
DeWeaver, M.A., Shannon, R., (2010), “Waning vigilance and the disposition effect: Evidence from Thailand on individual investor decision making”, The Journal of Socio-Economics, Volume 39, Issue 1, Pages 18-23.
6
Eslami Bidgoli, Gholamreza, Kordlouie, Hamidreza, (2010). Behavioral Finance, A Transition from Standard Finance to Neurofinance, Journal of Financial Engineering and Portfolio Management, Vol. 1, No. 1, pp. 19-36.
7
Fadaeinejad, Mohammad Esmaeil, Miley, Mohammad Reza, and Imam Doost, Mostafa, (2015), The Influence of Behavioral and Reflective Factors on Delayed Orientation in Investor Transaction Behavior, Accounting and Auditing Studies, Fourth Year, No. 14, pp. 5-26.
8
Govindan, K., Palaniappan, M., Zhu, Q. and Kannan, D., (2012). Analysis of third party reverse logistics r using interpretive structural paradigming. International Journal of Production Economics, 140(1), pp.204-211.
9
Hassanzadeh, Ali, Ahmadian, Azam, (2015), The Impact of Stock Market Development on Economic Growth, Quarterly Journal of Money and Economics, No. 12, pp. 31-52.
10
Hosseini Chegini, Elham, Haghgoo, Behnaz, Rahmani Nejad, Leila, (2014), Investigation of Investor Behavioral Investments in Tehran Stock Exchange Based on Structural Equation Paradigming, Journal of Financial Management Strategy, Second Year, No. 7, pp. 113-133.
11
Lao P., Singh H., (2012), “Herding behavior in the Chinese and Indian stock markets”, Journal of Asian Economics, Volume 22, Issue 6, Pages 495-506
12
Malekouti, Neda, (2014), Improving Forecasting in Financial and Investment Markets, Future Payam Magazine, No. 7, pp. 36-38.
13
Martinez L.F., Zeelenberg M., Rijsman J.B., (2011), “Regret, disappointment and the endowment effect”, Journal of Economic Psychology, Volume 32, Issue 6, Pages 962-968.
14
Pour Zamani, Zahra, (2014), Comparing the Efficiency of Internal and Comparative Data Analysis Techniques in Nonlinear Genetic Algorithm to Predict Corporate Profitability, Financial Accounting, and Auditing Research, No. 23, pp. 117-131.
15
Rahnama Roodposhti, Fereydoon, Salehi, E Karam, (2010). Schools and Theories of Finance and Accounting, First Edition, Islamic Azad University, Central Tehran Branch.
16
Rahnama Roodposhti, Fereydoon, Zandieh, Vahid, (2012). Behavioral and Neural Finance (The New Financial Paradigm) From Theory to Practice, First Edition, Islamic Azad University, Central Tehran Branch.
17
Reb, J., (2008), “Regret aversion and decision process quality: Effects of regret salience on decision process carefulness”, Organizational Behavior and Human Decision Processes, Volume 105, Issue 2, Pages 169-182
18
Salmani, Mohammad, Kazemi Sani, Nasrin, Badri, Seyedali, Mattof, Sharif, (2016), identifying and analyzing the effect of variables and tolerance indexes, journal of space anaiysis of environmental dangers, number 2, pp 1-22.
19
Seuntjens, Terri, Van de van, Niels, Zeelenberg, Marcel and Van der Schors, Anna, (2016), Problems of financial Behavior, Journal of Economic Psychology,Vol 57, pp 1-12.
20
Shefrin, H., (2007), “Behavioral Corporate Finance: Decision that Create Value”, McGraw Hill Press.
21
Shleifer, A., (2000), “Inefficient Market: An Introduction to Behavioral Finance”. New York: Oxford University Press.
22
Statman, M., (1999), “Behavioral Finance: Past Battles and Future Engagments”. Financial Analysts Journal, November/December, Pp: 18-27
23
Stromback, Camilla, Lind, Therese, Skagerlund, Kenny & Vastfjall, Daniel (2017). does self-control predict financial behavior and financial well-being? Journal of behavioral and exoerimental finance, Vol14, 30-38pp.
24
Tang, Ning and Baker, Andrew, (2016), Self-esteem financial knowledge and financial behavior, journal of Economic Psychology, vol 54,164-176pp.
25
Villatoro, F., (2009), “The delegated Portfolio Management Problem: Reputation and Herding”, Elsevier B.V., Journal of Banking & Finance
26
Yao J., Ma C., Peng He W., (2014), “Investor herding behavior of Chinese stock market”, international Review of Economics & Finance, Volume 29, Pages 12-29.
27
Zarei, Ali, Darabi, Roya, (2018), The Influence of Investor Emotional Attitudes on Voluntary Disclosure in the Iranian Capital Market, Financial Accounting and Auditing Research, No. 37, pp. 131-157.
28
ORIGINAL_ARTICLE
Analyzing the Causal Relations between Trading Volume and Stock Returns and between Trading Volume and Return Volatility in Tehran Stock Exchange
Identifying the causal relations between trading volume and stock returns and between trading volume and return volatility plays a vital role in identifying profitable investment opportunities. In this study, the Granger causality test was conducted to analyze the causal relationships between the mentioned variables in Tehran Stock Exchange. Consequently, the Vector Auto Regression (VAR) model was employed to determine the conditional mean equations of returns and volume. Moreover, the bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was used to model the conditional variance equation, stating the relationship between volume and return volatility. According to the results, no bilateral causal relationship can be ascertained between returns, volume, and return volatility. In other words, return and return volatility could barely predict volume; therefore, volume cannot be the Granger causality of the other two variables. However, stock returns were found to have an important role in determining the volume. Likewise, return volatility can be used to predict volume accurately. In fact, stock returns and the return volatility were both the Granger causalities of the volume.
https://www.ijfifsa.ir/article_101108_380655f903f5e36f4a9964ff285c7e10.pdf
1999-12-01
27
40
10.22034/ijf.2019.101108
Stock Returns
Volume of trade
Return Volatility
Causal Relationships
Vector Auto Regression model
Granger causality
GARCH
Mohammad Reza
Rostami
m.rostami@alzahra.ac.ir
1
Assistant Prof., Department of Finance, Faculty of management, Alzahra University, Tehran, Iran.
AUTHOR
Peyman
Alipour
payman.alipoor@ut.ac.ir
2
MSc., Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran.
LEAD_AUTHOR
Adel
Behzadi
adel.bh145@gmail.com
3
Ph.D. Candidate, Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran.
AUTHOR
Esmaeeli, Shahpour (2006). The Relationship between Dividend Quality and Stock Returns, Master’s Thesis, Allameh Tabataba’i University
1
Omid Ghaemi, Mustafa (2000). Analyzing the Relationship between Volume and Stock Prices at Companies Listed in Tehran Stock Exchange, Master’s Thesis, Shahid Beheshti University
2
Jahankhani, Ali; Parsaeeyan, Ali; Financial Management, First and Second Volumes, SAMT Publications
3
Rahnama Roudposhti, Fereydoun; Mir Ghafari, Seyed Reza. Portfolio Performance Evaluation at Tehran Stock Exchange: The Use of Value at Risk, Journal of Financial Engineering and Stock Securities Management, Autumn, 2011
4
Rahnama Roudposthi, F. et al. (2013). Strategic Financial Management (Value Creation)
5
Ziodar, Mahdi (2005). Analyzing the Empirical Relationship between Volume, Stock Returns, and Return Volatility at Tehran Stock Exchange. Master’s Thesis, Tarbiat Modares University
6
Muhammad Ali Zadeh, Saeed (2002). Analyzing the Dynamic Relationship between the Transactions of Natural-Legal Investors and Stock Returns at Tehran Stock Exchange, Master’s Thesis, University of Tehran
7
Baklaci, H. and Kasman, A. (2008). An Empirical Analysis of Trading Volume and Return Volatility Relationship in the Turkish Stock Market.http://eab.ege.edu.tr/pdf/6_2/C6-S2-M11.pdf.
8
Blume, L., Easley, D., & O'Hara, M. (1994). Market statistics and technical analysis: The role of volume. Journal of Finance, 49, 153–181.
9
Campbell, J. Y., Grossman, S. J., & Wang, J. (1993). Trading volume and serial correlation in stock returns. Quarterly Journal of Economics, 108, 905–939.
10
Copeland, T. E. (1976). A model of asset trading under the assumption of sequential information arrival. Journal of Finance, 31,1149–1168.
11
Darrat, A. F., Rahman, S., & Zhong, M. (2003). Intraday trading volume and return volatility of the DJIA stocks: A note. Journal of Banking and Finance, 27, 2035–2043.
12
Gervais, S., & Odean, T. (2001). Learning to be overconfident. Review of Financial Studies, 14, 1–27.
13
Ghysels, E., Gourieá, C., & Jasiak, J. (2000). Causality between returns and traded volumes. Annales d'Economie et d. Statistique, 60, 189–206.
14
Griffin, J. M., Nardari, F., & Stulz, R. M. (2007). Do investors trade more when stocks have performed well? Evidence from 46 countries. Review of Financial Studies, 20, 905–951.
15
Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price–volume relation. Journal of Finance, 49, 1639–1664.
16
Karpoff, J. M. (1988). Costly short sales and the correlation of returns with volume. Journal of Financial Research, 11, 173–188.
17
Lamoureux, C. G., & Lastrapes, W. D. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. Journal of Finance,45, 221–229.
18
Lee, C. M. C., & Swaminathan, B. (2000). Price momentum and trading volume. Journal of Finance, 55, 2017–2069.
19
Malliaris, A. G., & Urrutia, J. L. (1998). Volume and price relationships: Hypotheses and testing for agricultural futures. Journal of Futures Markets, 18, 53–72.
20
Pascual, L., Romo, J. and Ruiz, E. (2006). Bootstrap Prediction for Returns and Volatilities in GARCH Models. Computational Statistics & Data Analysis. 50, 2293 – 2312.
21
R. S. Tsay, Analysis of financial time series vol. 543: Wiley. com, 2005.
22
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. Journal of Finance, 40, 777–791.
23
Smirlock, M., & Starks, L. (1985). A further examination of stock price changes and transaction volume. Journal of Financial Research, 8, 217–225.
24
Wang, J. (1994). A model of competitive stock trading volume. Journal of Political Economy, 102, 127–168.
25
W.-I. Chuang, H.-H. Liu, and R. Susmel, "The bivariate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility," Global Finance Journal, vol. 23, pp. 1-15, 2012.
26
ORIGINAL_ARTICLE
Measurement and assessment of systematic risk of selected industries in stock exchange using wavelet approach
Investment is an essential factor in a country’s economic development. Meanwhile, return and risk have been effective factors in investment. Today, many financial economists have accepted Risk or Beta as a standard tool for assessing the risk involved in certain actions. This paper has been conducted to find a way to obtain the risk of industries in different timescales included in the short-term and long-term. The statistical population includes a daily index of selected industries (including banks and the food, and car industries) from 2009 to 2014. The present study has measured the risk in different timescales using the wavelet analysis, and consequently, the risk time series have been expressed using a State- Space model. The direct relation between the risk of the selected industries and the market have been eventuated in which, an increase in return of the market would lead to an increase in return of industries and this has also been proven when there is a reduction in return.
https://www.ijfifsa.ir/article_101116_611c5a678719215fe9cd5155dd8ebdf7.pdf
1999-12-01
64
77
10.22034/ijf.2019.101116
Systematic risk
Stat Space Model
Beta Coefficient
Wavelet
Ghodratollah
Emamverdi
ghemamverdi@iauctb.ac.ir
1
Assistant Prof., Department of Economics, Islamic Azad University central Tehran branch, Tehran, Iran.
LEAD_AUTHOR
Mojtaba
Karimi
tabakarimi622@gmail.com
2
Ph.D. Candidate, Department of Finance, Islamic Azad University South Tehran Branch, Tehran, Iran.
AUTHOR
Cifter A., A. Ozun A, 2008, Signal Processing Model for Time Series Analysis: The Effect of International F/X Markets on Domestic Currencies Using Wavelet Networks, International Review of Electrical Engineering No. 3, Pp. 580-591
1
CifterA. , A. Ozun, 2007, multi scale Systematic Risk: An Application on ISE 30. MPRA Paper 2484, University Library of Munich, Germany
2
Conlon T, Crane M., Ruskin H. J., 2008, Wavelet Multi scale Analysis for Hedge Funds: Scaling and Strategies, Physical No. 387, Pp. 5197-5204
3
Drabi, Roya, Saeedi, Atiye, 2009, assessment of relationship between operating leverage and systematic risk in Tehran Stock Exchange, Journal of Financial Accounting and Auditory, No. 2, Pp. 141-162
4
Hejazi, Mehran, Gholamhosseini, Mehri, 2010, assessment of use of Capital Asset Pricing Model in Tehran Stock Exchange, Journal of Financial Accounting and Auditory, No. 34, Pp. 65-92
5
Lynch & Zumbach, 2003, Lynch, P. and Zumbach,m G. (2003). Market heterogeneities and the causal structure of volatility. Quantitative Finance, 3:320-331.
6
Mohammadi, Shapoor et al, 2007, assessment of different estimation methods of Beta in Tehran Stock Exchange, Auditory and Accounting assessments, No. 14, Pp. 3-38
7
Raee, Reza, Khosravi, Amir Reza, 2007, specification of Capital Asset Pricing Model with emphasize on undesirable risk in Tehran Stock Exchange, Journal of Social and Human science, No. 7, Pp. 45-62
8
Sadeghi, Hossein, Zolfaghari, Mehdi, 2010, basis of prediction models in economics, Noore Elm Pub. First addition, Tehran
9
Samadi, Saeed, Nasrollahi, Zahra, Zahedmeh, Amin, 2007, efficiency test of existence bubble in Tehran Stock Exchange using filter rule and CAPM, Economic Journal of Quantitative assessments, No. 4, Pp. 91-113
10
Zolfaghari mahdi, Sadeghi hossein, Aram Rahman, 2012, Modeling and Forcasting of Urban Water short – run Demand, Journal of Economic Studies and Policies (Economic Policies), winte2012, Volume 7(17), Number 2(87); Page (s) 159 to 172.
11
ORIGINAL_ARTICLE
Financial Integration between Iran, OPEC and the Shanghai Organization
This article investigates the financial convergence between Iran, OPEC & the Shanghai Organization trade groups, of which Iran is a member. The analysis covers the period of 2005 to 2017.In order to examine the convergence dynamics of these financial markets; we have employed the Philips and Sul (2007) methodology, which uses a nonlinear time-varying factor model. This paper provides a comprehensive picture of the financial systems within Iran and its convergence clubs by testing the convergence of their money market with domestic credit to private sector by banks (% of GDP), deposit and lending interest rate, real interest rate, and capital market with Stocks traded, total value (% of GDP). The empirical findings show that money and stock markets of OPEC and the Shanghai group do not form a homogenous convergence club. Results show that Iran has convergence with some countries in OPEC and the Shanghai group in money and stock markets, which can be explained by their similar economic indicators in both markets. Furthermore, the convergence speed between Iran and the Shanghai countries is higher than that of Iran and OPEC countries, which proves that joint trade agreements are stronger reasons for convergence than the oil factor. Iran should implement further structural reforms in order to achieve greater financial convergence with its joined groups.
https://www.ijfifsa.ir/article_101118_2ef5e4b05bebf9ef1b513a798af9225b.pdf
1999-12-01
78
105
10.22034/ijf.2019.199622.1059
Stock markets
Banking sector
Opec and The Shanghai Organization
Panel Convergence Methodology
Saghar
Nikpour
saghar.nikpour@gmail.com
1
Ph.D. Candidate, Department of Economics, Faculty of Management and Economics, Shahid Bahonar university of kerman, Kerman, Iran.
LEAD_AUTHOR
Mojtaba
bahmani
mbahmani@uk.ac.ir
2
Assistant Prof., Department of Economics, Faculty of Management and Economics, Shahid Bahonar university of kerman, Kerman, Iran.
AUTHOR
sayyed Abdolmajid
Jalaee
jalaee@uk.ac.ir
3
Prof., Department of Economics, Faculty of Management and Economics, Shahid Bahonar university of kerman, Kerman, Iran.
AUTHOR
Mehdi
Nejati
mehdi.nejati@gmail.com
4
Assistant Prof. of Economics, Faculty of Management and Economics, Shahid Bahonar university of kerman, Kerman, Iran.
AUTHOR
Arnold, I.J., van Ewijk, S.E. (2014). A state space approach to measuring the impact of sovereign and credit risk on interest rate convergence in the euro area. J. Int. Money Finance 49 (B), 340–357.
1
Afonso, A., Furceri, D., Gomes, P. (2012). Sovereign credit ratings and financial markets linkages: application to European data. J. Int. Money Finance 31 (3), 606–638.
2
Baele, L., Ferrando, A., Hördahl, P., Krylova, E., Monnet, C. (2004). Measuring financial integration in the euro area. ECB Occasional Paper Series No. 14. European Central Bank.
3
Bartkowska, M., Riedl, A. (2012). Regional convergence clubs in Europe: identification and conditioning factors. Econ. Modell. 29 (1), 22–31.
4
Baumöhl, E. (2014). Risk-return convergence in CEE stock markets: structural breaks and market volatility. Finance aúver-Czech J. Econ. Finance 64 (5), 352–373.
5
Bernard, A.B., Durlauf, S.N. (1996). Interpreting tests of the convergence hypothesis. J. Econom. 71 (1), 161–173. Borsi, M.T., Metiu, N., 2015. The evolution of economic convergence in the European Union. Empir. Econ. 48, 657–681.
6
Buch, C.M., Pierdzioch, C. (2005). The integration of imperfect financial markets: implications for business cycle volatility. J. Policy Model. 27 (7), 789–804.
7
Byström, H., 2005. Credit default swaps and equity prices: the iTraxx CDS index market. Working Papers No. 24. Lund University.
8
Caporale, G.M., Erdogan, B., Kuzin, V. (2015). Testing stock market convergence: a non-linear factor approach. Empirica 42, 481–498. De Jong, R., Sakarya, N., 2016. The econometrics of the Hodrick-Prescott filter. Rev. Econ. Stat. 98, 310–317.
9
Cecchetti, S. G., & Schoenholtz, K. L. (2011). (The 3rd Edition) New York, NY: McGraw-Hill Education, ISBN 978-0-07-122068-2, 673.
10
Charles Morris, 1998. The Law of Financial Services Groups.Oxford university Press.
11
Durlauf, S.N., Quah, D.T. (1999). The new empirics of economic growth. In: Taylor, J.B., Woodford, M. (Eds.), Handbook of Macroeconomics, vol. 1A. Elsevier Science, North-Holland. J. Bank. Finance 34, 856–870.
12
Luc Eyraud, Diva Singh, and Bennett Sutton (2017). Benefits of Global and Regional Financial Integration in Latin America.IMF working paper.WP/17/1.
13
Färe, R., Grosskopf, S., Margaritis, D. (2006). Productivity growth and convergence in the European Union. J. Product. Anal. 25 (1-2), 111–141.
14
Fernández de Guevara, J., Maudos, J., Pérez, F. (2007). Integration and competition in the European financial markets. J. Int. Money Finance 26 (1), 26–45.
15
Fischer, C., 2012. Price convergence in the EMU? Evidence from micro data. Eur. Econ. Rev. 56 (4), 757–776.
16
Fritsche, U., Kuzin, V. (2011). Analysing convergence in Europe using the non-linear single factor model. Empir. Econ. 41 (2), 343–369.
17
Fung, M.K. (2009). Financial development and economic growth: convergence or divergence? J. Int. Money Finance 28 (1), 56–67.
18
Robert J. HOdrick Edward C. Prescott Postwar (1997-1980) U.S. Business Cycles: An Empirical Investigation Journal of Money, Credit and Banking, Vol. 29, No. 1 (Feb., 1997), pp. 1-16 Published by: Blackwell Publishing
19
IMF – International Monetary Fund (2014). Republic of Slovenia, January 2014. IMF Country Report no. 14/11.
20
Islam, N. (2003). What have we learnt from the convergence debate? J. Econ. Surv. 17 (3), 309–362. M. NiÛoi, M.M. Pochea / Economic Systems 40 (2016) 323–334 333.
21
Lyócsa, Š., Baumöhl, E. (2015). Similarity of emerging markets returns under changing market conditions: markets in the ASEAN-4, Latin America Middle East and BRICS. Econ. Syst. 39 (2), 253–268.
22
Monfort, M., Cuestas, J.C., Ordóñez, J. (2013). Real convergence in Europe: a cluster analysis. Econ. Modell. 33, 689–694. OECD – Organisation for Economic Co-operation and Development, 2013. OECD Economic Surveys Slovenia, April 2013.
23
Mckinnon and Shaw (1973). Finance and Growth: A Survey of the Theoretical and Empirical Literature. Tinbergen Institute Discussion Paper TI 2004-039/2.
24
Opec bulletin 2017.JMMCheld in St Petersburg.
25
Phillips, P.C., Sul, D. (2007). Transition modeling and econometric convergence tests. Econometrica 75 (6), 1771–1855.
26
Prasad, E., Rogoff, K., Wei, S.-J., Kose, M.A. (2003). The effects of financial globalization on developing countries: some empirical evidence. IMF Occasional Paper No. 220. International Monetary Fund.
27
Pungulescu, C. (2013). Measuring financial market integration in the European Union: EU15 vs. new member states. Emerg. Markets Rev. 17, 106–124.
28
Ravn, M., Uhlig, H. (2002). On adjusting the Hodrick-Prescott filter for the frequency of observations. Rev. Econ. Stat. 84, 371–376.
29
Rughoo, A., Sarantis, N. (2012). Integration in European retail banking: evidence from savings and lending rates to non-financial corporations. J. Int. Finan. Markets Inst. Money 22 (5), 1307–1327.
30
Rughoo, A., Sarantis, N. (2014). The global financial crisis and integration in European retail banking. J. Bank. Finance 40, 28–41.
31
ORIGINAL_ARTICLE
Investigating the Relationship between Managerial Entrenchment and Internal Control Weakness (Operant Conditioning Behavior Theory Test)
Purpose: In the shadow of separation of the ownership from the control and the problem of representation arising from it in the modern business world, there is a need to pay attention to the CEOs' approaches toward takeover as decision makers in this area. Managerial entrenchment is considered as one of the consequences of separation of ownership from control that explains the difference between the incentives in the corporate management and causes a disturbance in internal control as a communication mechanism between the corporate's performance and the capital market. Paying attention to operant conditioning behavior like tournament incentives as a functional behavioral stimulus in CEOs reduces the profit-seeking attitudes among them and increases the effectiveness of the corporates' performance mechanisms in disclosing financial reporting. The purpose of this research is to study the relationship between the managerial entrenchment and the internal control weakness by operant conditioning behavior theory test. Design/methodology/approach: In this research, 95 companies listed in Tehran Stock Exchange were evaluated between 2013 and 2018. Considering the duality of internal control assessment, logistic regression in SPSS software was used in this research. Findings: Results of the research showed that there is positive and significant relationship between the managerial entrenchment and the internal control weakness. But there is a negative and significant relationship between the tournament incentive and the internal control weakness. Moreover, it was found that the negative relationship between the managerial entrenchment and the corporates' internal control is mediated by the tournament incentive. Originality/value: Considering that little attention has been paid to motivational issues of the CEOs under the representation theory over the past few years, the present research attempts to investigate managerial entrenchment approach with the effectiveness of internal controls through analysis of the operant conditioning behavior theory to provide more reliable experimental results for the investors and the shareholders.
https://www.ijfifsa.ir/article_101119_2ca3ff744ea7bca0d435b43cb74ccadf.pdf
1999-12-01
106
132
10.22034/ijf.2019.194923.1051
Managerial Entrenchment
Tournament Incentive
internal control weakness
Mostafa
Maskani
mmaskani2000@gmail.com
1
PhD. Candidate, Department of Accounting, Islamic Azad University, Shahrood Branch, Shahrood, Iran.
AUTHOR
Mohammadreza
Abdoli
mrab830@yahoo.com
2
Associate Prof., Department of Accounting, Islamic Azad University, Shahrood Branch, Shahrood, Iran.
LEAD_AUTHOR
Abor, J., Godfred A, B. (2010). Investment opportunities, corporate finance, and dividend payout policy: Evidence from emerging markets, Studies in Economics and Finance, Vol. 27 Issue: 3, pp.180-194, https://doi.org/10.1108/10867371011060018
1
Aggarwal, R. K., Samwick, A. A. (2003). Performance Incentives within Firms: The Effect of Managerial Responsibility. Journal of Finance, 58 (4): 1613-1650.
2
Ambrose, B., Megginson, W., 1992.The role of as set structure, ownership structure, and takeover defenses in determining acquisition likelihood. J. Finan. Quant. Anal. 27 (4), 575–589.
3
Bebchuk, L., Cohen, A., Ferrell, A. (2009). What matters in corporate governance? Rev. Finan. Stud. 22 (2): 783–827.
4
Beng, W. G., Li, D. (2011). Internal Controls and Conditional Conservatism, Accounting Review, 86(3): 975-1005.
5
Berger, P. G., Ofek, E., & Yermack, D. L. (1997). Managerial entrenchment and capital structure decisions. Journal of Finance, 52(3): 1411–1438.
6
Bloom, M., & Michel, J. G. (2002).The relationships among organizational context, pay dispersion, and among managerial turnover. Academy of Management Journal, 45(1), 33–42.
7
Bognanno, M. L. (2001). Corporate tournaments. J. Labor. Econ. 19, 290–315.
8
Bonnier, K., Bruner, R. (1989). An analysis of stock price reaction to management change in distressed firms, Journal of Accounting and Economics 11(11): 95-106.
9
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