ORIGINAL_ARTICLE
Earning Quality and Investment Efficiency; Do Board Characteristics Matter? Evidence from Tehran Stock Exchange
This study postulates the relationships between earning quality and investment efficiency among Tehran Stock Exchange-listed companies with an emphasis on the moderating role of board characteristics including independence, the duality of executives and the financial expertise of members. The research is applied in terms of purpose and takes a correlative-descriptive approach. The statistical population is comprised of TSE listed companies from 2008 to 2018 and, the final sample consisting of 78 companies was selected using systematic (purposeful) elimination. To test the hypotheses, two regression models were estimated using Ordinary Least Squares method through Eviews software. The empirical results revealed a positive and significant relationship between the quality of earning and investment efficiency in TSE publicly-traded companies. As well as, the board members' independence and financial background can significantly exaggerate such a relationship. Based on our findings, capital market legislators, regulators, and policymakers may reinforce the governance role of the board of directors in monitoring the behavior of firms, and as a result, increase the efficiency of allocating capital among companies listed in TSE and also in macroeconomic levels. The findings can persuade corporate shareholders to pay more attention to the degree of independence and expertise of their board of directors to gain more return on their investment opportunities.
https://www.ijfifsa.ir/article_101364_aa7897f1cdc8d6567e631692b968b1fb.pdf
2020-01-12
1
23
10.22034/ijf.2020.208476.1086
Board Independence
Earning Quality
Executive Duality
Financial expertise
investment efficiency
Mahmoud
Karimi
m.karimi3313@gmail.com
1
Ph.D. Candidate, Department of Finance, Faculty of Management, Islamic Azad University, Science Research & Technology Branch, Tehran, Iran.
LEAD_AUTHOR
Ali
Eshaghzadeh
ali_eshaghzade@yahoo.com
2
MSc., Department of Finance, Faculty of Petroleum, Petroleum University of Technology, Tehran, Iran.
AUTHOR
Hadi
Poursina
afin2012@yahoo.com
3
B.S., Department of Accounting, Faculty of Petroleum, Petroleum University of Technology, Tehran, Iran.
AUTHOR
Aghaei, M. A. A. & Etemadi, Hussein.(2009). Characteristics of corporate governance and the information content of earnings in Tehran Stock Exchange, with emphasis on the role of earnings management. Journal of Management Sciences, (16), 53-27 (in Persian).
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50
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51
ORIGINAL_ARTICLE
Cash flow forecasting by using simple and sophisticated models in Iranian companies
Cash flow is one of the critical resources in the economic unit and the balance between available cash and cash needs is the most important factor in economic health. Since judgments of many stakeholders such as investors and shareholders about the position of the economic unit are based on liquidity situation, so predicting future cash flow is crucial. In this research, the impact of cash and accrual items on cash flow forecasts has been studied. Providing a proper model to predict operating cash flows and review some important characteristics of cash flow forecasting regression models, using a multilayer perceptron and determining the best model by using accrual regression model variables for predicting cash flows. For this purpose, 287 firms listed in Tehran Stock Exchange during 2008 to 2017 were studied; Linear and nonlinear regression, correlation coefficient and artificial neural network statistical methods have been used for data analysis and predictive power of powers was compared by using the sum of squared prediction error and coefficient of determination. Results showed that the accrual regression model can predict future cash flows better than other tested models and among corporate characteristics, the highest correlation belongs to sales volatility and firm size with accrual regression models. On the other hand, results of fitting different neural network models indicate that two structures with 8 and 11 hidden nodes are the best models to predict cash flows.
https://www.ijfifsa.ir/article_101365_05a81abcd84ec895d67c87d932b6496f.pdf
2020-01-12
24
52
10.22034/ijf.2020.202650.1071
Predicting cash flows
Future cash flows prediction models
Accruals
Artificial Neural Network
Fatemeh
Sarraf
aznyobe@yahoo.com
1
Assistant Prof., Department Of Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran.
LEAD_AUTHOR
AL-Attar. A. and Hussain. S. (2004). Corporate data and future cash flow. Journal of Business Finance and Accounting, Vol. 31. Nos7-8. PP. 861-903.
1
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4
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37
Sarraf, Fatemeh. (2013). "Designing a model for predicting cash flow in Iranian companies". Ph.D. Thesis. AllamehTabataba'i University.
38
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39
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41
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45
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46
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47
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48
ORIGINAL_ARTICLE
The Role of Machiavellianism, Emotional Manipulation and Moral Foundations in Tax Avoidance
Tax avoidance is making use of legal loop holes to display an individual's financial situation as if it were lower than what it is in order to decrease the amount of income tax owed. Behavioral economics and taxation literature indicate that psychological factors can provide further insight on accountants' financial decisions. The literature claim that tax compliance can be influenced by an individual’s personality and beliefs. Therefore, in this research, the effects of psychological variables including Machiavellianism, emotional manipulation and moral foundations are examined on tax avoidance in accounting and finance professions. The aim of this study is to investigate the role of Machiavellianism and emotional manipulation as two negative attributes of human beings and moral foundations in tax avoidance in listed and unlisted firms. For this purpose, a sample consisting of 500 accountants and financial managers of listed and unlisted companies of Tehran stock exchange was selected. This study is an applied and descriptive survey. The hypotheses of the research have been analyzed by structural equation modeling using Lisrel software. The evidence of this study show that Machiavellianism has a positive and moral foundations have a negative effect on tax avoidance. But, this study doesn’t confirm any significant relation between emotional manipulation and tax avoidance. This paper also states that social and psychological variables would explain the tax avoidance phenomenon.
https://www.ijfifsa.ir/article_101366_6a764d52e2bfce4a8a99b859245247ff.pdf
2019-01-01
53
72
10.22034/ijf.2019.208496.1088
machiavellianism
Emotional Manipulation
Moral Foundations and Tax Avoidance
Hossein
Esmaeili Komar Olia
h.esmaili.k@gmail.com
1
PhD. Candidate, Department Of Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
AUTHOR
Bahman
Banimahd
dr.banimahd@gmail.com
2
Associate Prof., Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.
LEAD_AUTHOR
Sina
Kheradyar
kheradyar@iaurasht.ac.ir
3
Assistant Prof., Department of Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran.
AUTHOR
Aghaei, H., Husseini, H; Bagheri, H. (2018) The Role of Management ability to Avoid Company Tax: Evidence from Tehran Stock Exchange, Empirical Studies of Financial Accounting, Year 14, pp. 57 - 47, (in Persian).
1
Akers, M. D. and G. L. Porter. (2003). Your EQ skills: Got what it takes? Journal of Accountancy 195 (3):65–70.
2
Amiri, M (2017) behavioral economics and tax evasion, Journal of Economic Bulletin, 16, 130 - 130.and intentional noncompliance: An experimental investigation, (in Persian).
3
Austin, E. J. Farrelly, D. Black, C. & Moore, H. (2007). Emotional intelligence, Machiavellianism and emotional manipulation: Does EI have a dark side? Personality and Individual Differences, 43(1), 179–189
4
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5
Chen, S. (2010) Relations of Machiavellianism with emotional blackmail orientation of salespeople, Procedia Social and Behavioral Sciences. 5: 294–298
6
Christian, R.C. Alm, J. (2014) Empathy, sympathy, and tax compliance, Journal of Economic Psychology, 40, 62-82
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Christie, R. & Geis, F. (1970) Studies in Machiavellianism (pp. 339–358). New York: Academic Press.
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Coricelli, G. Rusconi, E. and Villeval, M. C. (2014). Tax evasion and emotions: An empirical test of reintegrative shaming theory. Journal of Economic Psychology, 40, 49–61.
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29
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30
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31
Manoj, M. Gopal. M. (2019) Consequences and Evidence of Tax Evasion and Avoidance" International Journal of Business and Management Invention (IJBMI), vol. 08, no. 01, 2019, pp 72-76
32
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33
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36
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50
ORIGINAL_ARTICLE
Analysis of Iran Banking Sector by Multi-Layer Approach
Networks are useful tools for presenting the relationships between financial institutions. During the previous years, many scholars have found that using single-layer networks cannot properly characterize and explain complex systems. The purpose of this research is to introduce a multiplex network in order to analyze, as accurately as possible, all aspects of communication between banks in capital market of Iran. In this article, each bank represents a node and three layers of return, trading volume and market Cap have been presented for analyzing the idea of multiplex networks. We have used the Granger causality method to determine the direction between nodes. For understanding the topology structure of these layers, different concepts have been used. The research findings show that the value layer topology has a significant similarity with the trading volume layer. Also according to the measure of centrality it can be seen that the centrality varies in different layers.
https://www.ijfifsa.ir/article_101367_44f4271f64d305bd62fc523fdc930fe3.pdf
2019-01-01
73
89
10.22034/ijf.2019.101367
Banking sector
centrality
Complex system
Granger causality
Multiplex Network
Ali
Namaki
alinamaki@ut.ac.ir
1
Assistant Prof., Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran.
LEAD_AUTHOR
Reza
Raei
raei@ut.ac.ir
2
Prof., Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran.
AUTHOR
Nazanin
Asadi
n.asadi@ut.ac.ir
3
MSc., Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran.
AUTHOR
Ahmad
Hajihasani
a.hajihasani@ut.ac.ir
4
MSc., Department of Finance, Faculty of Management, University of Tehran, Tehran, Iran.
AUTHOR
Aldasoro, I&Alves, I (2018). Multiplex interbank networks and systemic importance: An application to european data. Journal of Financial Stability, 35, 17-37.
1
Aleta, A & Moreno, Y (2018). Multilayer networks in a nutshell. Annual Review of Condensed Matter Physics, 10 (3), 45-62.
2
Andrew, W; Wang, J (2000). Trading volume: definitions, data analysis, and implications of portfolio theory. The Review of Financial studies, 13, 257-300.
3
Bargigli, L; di Iasio, G; Infante, L; Lillo, F & Pierobon, F (2014). The multiplex structure of interbank networks. Quantitative Finance, 15, 673-691.
4
Bargigli, L; di Iasio, G; Infante, L; Lillo, F & Pierobon, F (2016) Interbank markets and multiplex networks: centrality measures and statistical null models. Interconnected Networks. 9, 179-194.
5
Battiston, S; Gatti, D; Gallegati, M; Greenwald, B & Stiglitz, J (2012). Increasing connectivity, risk sharing, and systemic risk. Journal of Economic Dynamics and Control, 36, 1121-1141.
6
Boccaletti, S (2014). The structure and dynamics of multilayer networks. Physics Reports, 544, 1-122.
7
Boldi, P &Vigna, S (2014). Axioms for centrality. Internet Mathematics, 10, 222-262.
8
Bonacich, P (2007). Some unique properties of eigenvector centrality. Social networks, 29 (4), 555-564.
9
Borboa, M; Jaramillo, M; Gallo, F& van der Leij, M (2015). A multiplex network analysis of the mexican banking system: link persistence, overlap and waiting times. Journal of Network Theory in Finance,1, 99 -138.
10
Caraiani, P (2013). Using complex networks to characterize international business cycles. PLoS one, 8 (3), e58109.
11
Chen, G; Firth, M & Rui, O (2001).The dynamic relation between stock returns, trading volume and volatility. Financial Review, 36, 153-174.
12
Cont, R; Moussa, A & Santos, E (2010). Network structure and systemic risk in banking systems. SSRN eLibrary, 1- 41.
13
Faghani, M & Nguyen, U (2013). A study of xss worm propagation and detection mechanisms in online social networks. IEEE transactions on information forensics and security, 8 (11), 1815-1826.
14
Fagiolo, G (2007). Clustering in complex directed networks. Phys. Rev. E, 76, 026107.
15
Gai, P & Kapadia, S (2010). Contagion in financial networks. Proceedings of the Royal Society A, 466(2120), 2401-2423.
16
Granger, C (1969). Investigating causal relations by econometric models and cross spectral methods. Econometrica: Journal of the Econometric Society, 37 (3), 424-438.
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Junker, B & Schreiber, F (2008). Analysis of biological networks. Wiley-Interscience, 2, 31-59.
19
Kermarrec, A; Merrer, E; Sericola, B & Tredan, G (2011). Second order centrality: Distributed assessment of nodes criticity in complex networks. Computer Communications, 34, 619-628.
20
Landherr, A; Friedl, B & Heidemann, D (2010). A critical review of centrality measures in social networks. Business and Information Systems Engineering, 2, 371-385.
21
Liu, Z; Jiang, C; Wang, J & Yu, H (2015). The node importance in actual complex networks based on a multi-attribute ranking method. Knowledge-Based Systems, 84, 56-66.
22
Namaki, A; Koohi Lai, Z; Jafari, GR; Raei, R & Tehrani, R (2013). Comparing emerging and mature markets during times of crises: A non-extensive statistical approach. Physica A: Statistical Mechanics and its Applications, 392, 3039-3044.
23
Namaki, A; Shirazi, AH; Raei, R & Jafari, GR (2011). Network analysis of financial market based on genuine correlation and threshold method. Physica A: Statistical Mechanics and its Applications, 390, 3835-3841.
24
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25
Shirazi, A; Namaki, A; Roohi, A & Jafari, GR (2013). Transparency effect in emergence of monopolies in social networks. arXiv preprint arXiv, 1301.4634.
26
Soramaki, K; Bech, M; Arnold, J; Glass, R & Beyeler, W (2007). The topology of interbank payment ows. Physica A: Statistical Mechanics and its Applications, 379, 317-333.
27
Tang, Y (2019). How do the global stock markets inuence one another evidence from finance big data and granger directed network. International Journal of Electronic Commerce, 23, 85-109.
28
Yu, Y & Fan, S (2015). Node importance measurement based on the degree and closeness centrality. Journal of Information and Commputational Science, 12, 1281-1291.
29
Zheng, B; Li, D; Chen, G; Du, W & Wang, J (2012). Ranking the importance of nodes of complex networks by the equivalence classes approach. ARxIV PREPRINT, 1211-5484.
30
ORIGINAL_ARTICLE
Comparison of Some Data Mining Models in Forecast of Performance of Banks Accepted in Tehran Stock Exchange Market
In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics. The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naïve Bayes algorithm. According to the findings, the Naïve Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.
https://www.ijfifsa.ir/article_101368_4b86dfde094118b213a22d82699a8f6b.pdf
2019-01-01
90
109
10.22034/ijf.2019.195386.1047
Bank Performance
Data mining
Financial Ratios
Tehran Stock Exchange
Elham
Adakh
eladel20081@gmail.com
1
PH. D Candidate, Department of Finance, faculty of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran.
AUTHOR
Arefeh
Fadavi Asghari
are.fadavi_asghari@iauctb.ac.ir
2
Assistant Prof., Department of Finance, Faculty of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran.
LEAD_AUTHOR
Mohammad Ebrahim
Mohammad Pourzarandi
moh.mohammadpour_zarandi@iauctb.ac.ir
3
Prof., Department of Finance, Faculty of Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran.
AUTHOR
Al-Osaimy, M. H. (1995). A Neural Networks System for Predicting Islamic Banks Performance. JKAU: Econ. & Adm., Vol. 11, p. 33-46.
1
Azar,A.Faraji,H,.(2010).Fuzzy management science,Mehraban publishing.(Book)
2
Bay vo,Bac Le,Thang N.Nguyen,.(2011).Mining frequent Itemsets from multi dimensional Data base.ACIDS '11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I ,p. 177-186.
3
Becerra, V. M., Galvao, R. K. H., & Abou-Seads, M. (2005). Neural and wavelet network models for financial distress classification. Data Mining and Knowledge Discovery, 11, p.35–55.
4
Cheng, C. H. & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy modification of Delphi. (Book)
5
Ehteshami, S., Hamidian, M., Hajiha, Z. and Shokrollahi,S.(2018). Forecasting Stock Trend by Data Mining Algorithm. Journal of Advances in mathematical finance & applications,3 (1),p. 97-105.
6
Esmaeli, Mehdi,.(2014). Concepts and techniques of data mining, Niaz Danesh publishing. (Book)
7
Ghazanfari,M.,Alizadeh,S., &Teimorpour,B.(2016).Data mining and knowledge discovery .(Book)
8
Gorunescu,F,.(2011). Concepts , Models and Techniques of data mining,Springer-Verlag Berlin Heidelberg.
9
Neelamadhab, P., Dr.Pragnyaban, M. and Rasmita, P.(2012). The survey of data mining applications and feature scope, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2(3),p.43-58.
10
Odom,M.D.,&Sharda,R.(1990). A neural network model for bankruptcy prediction,. Neural Networks (IJCNN), International Joint Conference on.
11
Pino-Mejías.R, Cubiles.M.D-de-la-Vega, Anaya-Romero.M, Pascual-Acosta.A, Jordán-López.A, and Bellinfante-Crocci.N.(2010). Predicting the potential habitat of oaks with data mining models and the R system, Journal of Environmental Modelling & Software.25(37),p.826-836.
12
Porzanb, M., Cristina, A. and Danescu, T.(2012). The role of the risk management and of the activities of internal control in supplying usefu information through the accounting and fiscal reports, Journal of Procedia and Finance, 3, P.1099-1106.
13
Ravi Kumar, P., & Ravi, V. (2006). Bankruptcy prediction in banks by an ensemble classifier. In Proceedings of IEEE international conference on industrial technology, Mumbai, India.p. 2032–2036.
14
Ravi Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques-a review. European Journal of Operational Research, 180,p. 1–28.
15
Saberi, M., Rostami,M., Hamidian,M. & Aghimi,N. (2016). Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network.Journal of Advances in mathematical finance & applications.1 (2), p.95-104.
16
Sangjae,L.,& Wu Sung.C.(2013). A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis.International Journal of Expert systems of applications,40(8), p.2941-2946.
17
ORIGINAL_ARTICLE
Which Investment method is selected by companies in each stage of their Life Cycle? (Investing in operating assets or non-operational assets)
One of the main causes of firms’ ineffectiveness is the absence or insufficiency of appropriate investment methods. This deficiency could also be attributed to an unfortunate selecting of an inappropriate investment methods which may ultimately endanger the firms’ prospect of survival. According to the firm life cycle theory, various firms demonstrate diverse behavior when provided with an investment opportunity. These responses are largely in accordance with the stage of the life cycle in which the firm resides in at that moment. In this research, the selection of the investment method appropriate for a firm has been studied following the premises of the life cycle theory. The target populations of this study were companies admitted to Tehran Stock Exchange. Systematic removal method was adopted to recruit a sample of 118 firms. The study period was 8 years (2011-2018). Findings suggest that firms choose to invest in operational properties when they are at the stage of growth, maturity and decline. In other words, the capital under the companies’ authority and control were employed for the firms’ mainstream activities. However, such a link was not found at the introduction stage of their life cycle. This relation has been illustrated in various industries.
https://www.ijfifsa.ir/article_101369_0d40875d845e63b99362da6d0010112b.pdf
2019-01-01
110
129
10.22034/ijf.2019.183450.1021
Firm Life Cycle theory
Capital Investment Choice
Industrial type
Ali
Khamaki
khamaki@aliabadiau.ac.ir
1
Ph.D. Candidate, Department of Accounting, Islamic Azad University, Aliabad Katoul branch, Aliabad Katoul, Iran.
AUTHOR
Parviz
Saeidi
saidi@aliabadiau.ac.ir
2
Associate Prof., Department of Accounting, Islamic Azad University, Aliabad Katoul branch, Aliabad Katoul, Iran.
LEAD_AUTHOR
Arash
Naderian
naderian@aliabadiau.ac.ir
3
Assistant Prof., Department of Accounting, Islamic Azad University, Aliabad Katoul branch, Aliabad Katoul, Iran.
AUTHOR
Ali
Khozain
khozein@aliabadiau.ac.ir
4
Assistant Prof., Department of Accounting, Islamic Azad University, Aliabad Katoul branch, Aliabad Katoul, Iran.
AUTHOR
Adizes, I. (1989). "Corporate Life cycles: How and Why Corporations Grow and Die and What to Do about it." Prentice Hall, Englewood Cliffs, NJ: 5-136.https://doi.org/10.1016/0024-01(92)90356-7
1
Anthony, J., and K. Ramesh (1992). "Association between Accounting ,Performance Measures and Stock Prices: A Test of the Life Cycle Hypothesis." Journal of Accounting & Economics Vol. 15: Pp. 203-227. https://doi.org/10.1016/0165-101(92)90018-W
2
Badurdeen, F., R. Aydin, et al. (2018). " A multiple lifecycle-based approach to sustainable product configuration design." Journal of Cleaner Production 200: 756-769. https://doi.org/10.1016/j.jclepro.2018.07.317
3
Chuang, K.-S. (2017). "Corporate life cycle, investment banks and shareholder wealth in M&As." The Quarterly Review of Economics and Finance 63: 122-134. https://doi.org/10.1016/j.qref.2016.02.008
4
Collins, D. W., P. Hribar, et al. (2012). "Cross sectional variation in cash flow asymmetric timeliness and its effect on the earnings-based measure of conditional conservatism." University of Iowa. http://dx.doi.org/10.2139/ssrn.2120677
5
Dickinson, V. (2011). "Cash flow patterns as a proxy for firm life cycle." The Accounting Review 86(6): 1969-1994. https://doi.org/10.2308/accr-10130
6
Francis, J. R., S. Huang, et al. (2009). "Does corporate transparency contribute to efficient resource allocation?" Journal of Accounting Research 47(4): 943-989. https://doi.org/10.1111/j.1475-679X.2009.00340.x
7
Hasan, M. M. (2018). "Organization capital and firm life cycle." Journal of Corporate Finance 48: 556-578. https://doi.org/10.1016/j.jcorpfin.2017.12.003
8
Hasan, M. M., M. Hossain, et al. (2015). "Corporate life cycle and cost of equity capital." Journal of Contemporary Accounting & Economics 11(1): 46-60. https://doi.org/10.1016/j.jcae.2014.12.002
9
Jaafar, H. and H. A. Halim (2016). "Refining the firm life cycle classification method: A firm value perspective." Journal of Economics, Business, and Management 4(No 2 February 20). https://doi.org/10.7763/joebm.2016.v4.376
10
Kousenidis, V. D., Earnings–returns relation in Greece: some evidence on the size effect and on the lifecycle hypothesis. Managerial Finance. Technological Educational Institution of Thessa loniki. 2005,31(2), P. 24-54. https://doi.org/10.1108/03074350510769488
11
Phung, D. N. and A. V. Mishra (2016). "Ownership structure and firm performance: Evidence from Vietnamese listed firms." Australian Economic Papers 55(1): 63-98. https://doi.org/10.1111/1467-8454.12056
12
Xu, Bixi. (2007). "Life cycle effect on the value relevance of common risk factors." Review of Accounting and Finance 6(2): 162-175. https://doi.org/10.1108/14757700710750838
13
Zhai, J. and Y. Wang (2016). "Accounting information quality, governance efficiency and capital investment choice." China Journal of Accounting Research 9(4): 251-266. https://doi.org/10.1016/j.cjar.2016.08.001
14