developing multifactor asset pricing models using firm's
TRANSCRIPT
Microsoft Word - 3- Mahdi MirzaeiDeveloping Multifactor Asset
Pricing Models Using Firm's Life Cycle
Mehdi Mirzaie Ph.D. Candidate, Department of Accounting, Faculty of Administrative Science & Economics, University of Isfahan, Isfahan, Iran. E-mail: [email protected].
Abdullah Khani *Corresponding author, Associate Prof., Department of Accounting, Faculty of Administrative Science & Economics, University of Isfahan, Isfahan, Iran. E-mail: [email protected]
Mahmoud Botshekan Assistant Prof., Department of Management, Faculty of Administrative Science & Economics, University of Isfahan, Isfahan, Iran. E-mail: [email protected]
Abstract Objective: This research is aimed at introducing firms' life cycles as a new and effective factor on stock return and comparing the performance of the new multifactor asset pricing models (augmented by firm's life cycle factor) with corresponding conventional multifactor asset pricing models in explaining stock returns.
Methods: To this end, Dickinson's cash flows pattern has been used to measure the firm's life cycle. A Firm's life cycle factors are constructed based on the difference in average returns of firms in maturity stage and firms in other firm's life cycle stages. Then, this factor waS combined with the conventional multi-factor pricing model, namely the Fama and French three-factor model, Carhart four-factor model, Fama and French five-factor model and Ho, Xue, and Zhang four-factor model. In the following, using time series regression approach, the performance of augmented multifactor asset pricing models and corresponding conventional ones are compared. For this purpose, the accounting and market data from companies listed in Tehran stock exchange and Iran Fara Bourse between the years 2004 and 2018 and the variety of test assets based on different firm's characteristics were used
Results: The results show that augmented multifactor pricing models have a better performance compared to corresponding multifactor pricing models in explaining stock returns and outperformance is more evident when test assets are formed using firm's life cycle compared to test assets formed without the firm's life cycle.
Conclusion: The addition of a firm's life cycle factor improves the performance of conventional multifactor pricing models in explaining stock returns.
Keywords: Multifactor asset pricing model, Firm's life cycle, Test assets, Cash flow patterns.
Citation: Mirzaie, M., Khani, A., & Botshekan, M. (2019). Developing Multifactor Asset Pricing Models Using Firm's Life Cycle. Financial Research Journal, 21(4), 545-569. (in Persian) ------------------------------------------------------------ Financial Research Journal, 2019, Vol. 21, No.4, pp. 545-569 DOI: 10.22059/frj.2019.278769.1006850 Received: April 27, 2019; Accepted: November 20, 2019 © Faculty of Management, University of Tehran
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Mehdi Mirzaie Ph.D. Candidate, Department of Accounting, Faculty of Administrative Science & Economics, University of Isfahan, Isfahan, Iran. E-mail: [email protected].
Abdullah Khani *Corresponding author, Associate Prof., Department of Accounting, Faculty of Administrative Science & Economics, University of Isfahan, Isfahan, Iran. E-mail: [email protected]
Mahmoud Botshekan Assistant Prof., Department of Management, Faculty of Administrative Science & Economics, University of Isfahan, Isfahan, Iran. E-mail: [email protected]
Abstract Objective: This research is aimed at introducing firms' life cycles as a new and effective factor on stock return and comparing the performance of the new multifactor asset pricing models (augmented by firm's life cycle factor) with corresponding conventional multifactor asset pricing models in explaining stock returns.
Methods: To this end, Dickinson's cash flows pattern has been used to measure the firm's life cycle. A Firm's life cycle factors are constructed based on the difference in average returns of firms in maturity stage and firms in other firm's life cycle stages. Then, this factor waS combined with the conventional multi-factor pricing model, namely the Fama and French three-factor model, Carhart four-factor model, Fama and French five-factor model and Ho, Xue, and Zhang four-factor model. In the following, using time series regression approach, the performance of augmented multifactor asset pricing models and corresponding conventional ones are compared. For this purpose, the accounting and market data from companies listed in Tehran stock exchange and Iran Fara Bourse between the years 2004 and 2018 and the variety of test assets based on different firm's characteristics were used
Results: The results show that augmented multifactor pricing models have a better performance compared to corresponding multifactor pricing models in explaining stock returns and outperformance is more evident when test assets are formed using firm's life cycle compared to test assets formed without the firm's life cycle.
Conclusion: The addition of a firm's life cycle factor improves the performance of conventional multifactor pricing models in explaining stock returns.
Keywords: Multifactor asset pricing model, Firm's life cycle, Test assets, Cash flow patterns.
Citation: Mirzaie, M., Khani, A., & Botshekan, M. (2019). Developing Multifactor Asset Pricing Models Using Firm's Life Cycle. Financial Research Journal, 21(4), 545-569. (in Persian) ------------------------------------------------------------ Financial Research Journal, 2019, Vol. 21, No.4, pp. 545-569 DOI: 10.22059/frj.2019.278769.1006850 Received: April 27, 2019; Accepted: November 20, 2019 © Faculty of Management, University of Tehran
[email protected] :.
[email protected] : . *
[email protected]: .
:
. . :
. .
1397 1383 .
: ) (
. : .
. :
).1398( :
.569 -545 )4(21 . --------------------------------------------------------------------------------
569 -545. 4 21 1398 DOI: 10.22059/frj.2019.278769.1006850
29/08/1398: 07/02/1398 :
©
546
1980
)CAPM(1 2 ) ) 1966( 4 ) 1965( 3 )1964 .
) 1981( 7 ) 1985( 6 )1980( 5 . CAPM
) CAPM ( 8 ) 1976 9(
11 ) 1993( 10 .
) 1997( ) 1997 12 (
. )2018( )2015( )2015( 13
)2018 17 ( 16 ) 2017 15 ( 14 .
( 18 ). 2019 21 2010 20 2010 19 ) 2019 2018 23 2006 22 (
1. Capital Asset Pricing Model 2. Sharpe 3. Lintner 4. Mossin 5. Stattman 6. Rosenberg, Reid, & Lanstein 7. Banz 8. Asset Picing Anomalies 9. Ross 10. Fama, & French 11. Carhart 12. Campbell, Lo, & Mackinlay 13. Hou, Xue, & Zhang 14. Mispricing Factor 15. Stambaugh, & Yuan 16. Q5 17. Hou, Mo, Xue, & Zhang 18. Accounting Anomalies 19. Richardson, Tuna, & Wysocki 20. Lewellen 21. Wang 22. Ecker, Francis, Olsson, & Schipper 23. Penman, & Zhu
4 21 1398 547
.
) 2019 2 2011 1(
.
)2011( :
( .
) . 3
( )1996 4( . )2016 ( )2015 5
8( ) 2006 7 1995 6 ( . ) 2019
( ( )2012 9 ( )2006 )2018 ( )2016 2015
. ) 2019 (
1. Dickinson 2. Konstantinidi 3. Test Assets 4. Sloan 5. Ball, Gerakos, Linnainmaa, & Nikolaev 6. Loughran, & Ritter 7. Bradshaw, Richardson, & Sloan 8. Wang 9. Hirshleifer, Hou, & Teoh
548
) 2011( . 1 )2019(
.
.
.
). 1995 3( 2)DDM( . )2011( . DDM
. .
) 2019( ) . ) 2015( ) 1993(
. ). ) 2019( ) (
. 1
( )2017 ( )2015 ( . ) 2016 ( ) 2016
2. Dividend Discount Model 3. Ohlson
4 21 1398 549
1
2 (
)2012( 3 ). 2006 4 5 ) 2019( ). 1977 6( . .
) 2019 2011 ( . (
)2017( )
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.
.
) 2019( ) 2018( 8 .
1. Investor Sentiment 2. Baker, & Wurgler 3. Stambaugh, Yu, & Yuan 4. Short-Sale Impediments 5. Short leg 6. Miller
. 7 ) 1993( .
)overreaction( )underreaction ( . ) ) 2005 1999 .
8. Vorst, & Yohn
550
. 1
) 2015( 2 .
. )2011( .
. .
) 1990( 3 . ) ( ) (
.
CAPM ) 1397( . CAPM ) 1397(
)1396( . ) 1395( .
) 1993( .
) 1395 1397(
. ) 1395( . .
1. Out of Sample 2. Hasan, Hossain, Cheung, & Habib 3. Livnat, & Zarowin
4 21 1398 551
) 1995(
: ) 2011( :
: 1 .
: 2 .
: 3 .
: 4 .
. ) 2011( .
. . ) 172 ( 1397 1383
OxMetrics ) . )2009 2007 1
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