Systematic risk in the capital asset pricing model for australia a clinical death

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIET NAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS SYSTEMATIC RISK IN THE CAPITAL ASSET PRICING MODEL FOR AUSTRALIA: A CLINICAL DEATH? BY NGUYEN CONG THANG MASTER OF ARTS IN DEVELOPMENT ECONOMICS Ho Chi Minh City December 2017 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS SYSTEMATIC RISK IN THE CAPITAL ASSET PRICING MODEL FOR AUSTRALIA: A CLINICAL DEATH? A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN CONG THANG Academic Supervisor: Dr VO HONG DUC Ho Chi Minh City December 2017 DECLARATION I hereby declare, that the thesis entitled, “Systematic Risk in the Capital Asset Pricing Model Australia: A Clinical Death?” written and submitted by me in fulfillment of the requirements for the degree of Master of Art in Development Economics to the Vietnam – The Netherlands program This is my original work and conclusions drawn are bases on the material collected by me I further declare that this work has not been submitted to this or any other university for the award of any other degree, diploma or equivalent course Ho Chi Minh City, December 2017 Nguyen Cong Thang ACKNOWLEDGEMENTS I would like to express my special thanks of gratitude to my academic supervisor Dr Duc Vo He gave me the golden opportunity to this wonderful project on the topic of capital asset pricing model I know that, for the last 20 years, you has been spending your youth, your effort to make your life and your future thrive in Australia I appreciate this opportunity I did not realize that my high school knowledge, my skill I had developed as an Android developer could help me jump over challenges during the process of thesis accomplishment On that way, I learnt Visual Basic and R and I expect that they are my friends when I struggle with messy data I want to say thanks for my supervisor and for those introducing Beta and R to me I would also like to send my first few words to my friends at the Business and Economics Research Group (BERG) at Ho Chi Minh City Open University MA Thach Ngoc Pham and MA Anh The Vo Your attitude at work makes me wisdom with a positive slogan “If my work gets wrong, it again” Furthermore, drinking milk tea on every Thursday afternoon is a cute moment to me at BERG After all, I leave my last few words to Mom and Dad This thesis is for you This work is my gift to you I have put all great effort to develop and complete this very first academic study From the bottom of my heart, I apologize for your tears I should have focused on getting thing done to have lived happily and planned carefully my future My dearest loved Mom and Dad! I am still a kid, are not I? ABBREVIATIONS C4F: Cahart four-factor model CAL: Capital allocation line CAPM: Capital asset pricing model DDM: Dividend-discount model FF3F: Fama-French three-factor model GICS: Global Industry Classification Standard HML: High minus Low MPT: Modern portfolio theory SMB: Small minus Big ABSTRACT On the ground of a well-known Markowitz (1952)’s Modern Portfolio Theory, Sharpe (1964) and Lintner (1965) developed a specific relationship between risk and expected return, which has been named as the Sharpe-Lintner Capital Asset Pricing Model (CAPM) CAPM or the Sharpe-Lintner CAPM is a well-known and most widely used model for estimating a rate of return/cost of capital The CAPM confirms that only systematic risk – denoted by ß (beta), does matter and investors are only compensated for taking systematic risk Since its introduction, many studies have been conducted in an effort to assess the validity of the CAPM in practice Practitioners and regulators around the world including Australia, Germany, New Zealand and United Kingdom employed CAPM as a primary model to estimate asset’s return However, various studies demonstrated that CAPM appears to underestimate returns for low-beta assets and overestimate returns for high-beta assets The criticism went further as Fama and French (1992) introduced the three-factor model to estimate the asset’s return The Fama-French three-factor model has been proven to work well in the US market and that beta is alive in the American context However, in contrast to the US market, Vo (2015) argued that the Fama-French three-factor model has been proven to not work well in the Australian context A work by Savor and Wilson (2014) concluded that beta, or systematic risk, is still alive in the US market A similar question is that whether or not beta is still alive in Australia because Vo (2015) has never tested this hypothesis? We are not aware of any study on the issue which has been conducted This study is conducted to fill in the gap This study examines the validity of the Capital Asset Pricing Model (1965) in the context of Australia on the ground of the pioneering work by Savor and Wilson (2014) for the US The choice of Australia is important because, among all nations in the Asia-Pacific region, Australia is one of a few which has required data for the analysis to be conducted In the heart of the CAPM, beta is considered an important measure of systematic risk which is generally defined as an uncertainty about general economic conditions, such as GNP, interest rates, or inflation From that perspective, a key purpose of this study is to examine and quantify whether or not systematic risk is responsive on the days when macroeconomics news/events are announced or scheduled for announcement On the ground of Savor and Wilson (2014), four different types of portfolios are considered in this study including: (i) 10 beta-sorted portfolios; (ii) 10 idiosyncratic risk-sorted portfolios (iii) 25 Fama-French size and book-to-market portfolios; and (iv) industry portfolios In addition, macroeconomic events include announcements in relation to growth, inflation, employment, central bank announcements, bonds, housing, consumer surveys, business surveys and speeches from the Prime Minister or the Governor of the Reserve Bank of Australia Days with these events are allocated into the group (the so-called a-day) which is separated from the n-day (non-announcement days) group In addition, in this study, a sensitivity check, which is beyond Savor and Wilson (2014), by adopting different definition1 of the a-day group including (i) macroeconomics announcements which consist of news about growth, inflation, employment, Central Bank, bonds and speeches; (ii) microeconomics announcements which contains news related to housing, consumer survey and business survey; (iii) economics announcements which are basic news about news about growth, inflation, employment, housing, consumer surveys, business surveys and speeches; and (iv) financial announcements which are combined by news about Central Bank and bonds This study is conducted on a sample including more than 2,200 Australian listed firms collected from Bloomberg for the period from January 2007 to 31 December 2016 is employed As such, the total of nearly million observations has been used in this study Using the linear regression with panel-corrected standard errors method and Fama-Macbeth regression across various portfolios, two fundamental findings achieved from this study are as follows First, there is evidence supporting the presence of systematic risk in the Australian context Second, the above evidence may disappear when different portfolio formations and different definitions of macroeconomic events are adopted In summary, whether or not beta, or systematic risk, is alive in the Australian context depends on how portfolios are formed and macroeconomic events are classified These fundamental issues are generally known as puzzles in asset pricing studies and multi factor model has never been proven to withstand well when different markets/time/techniques are tested An appreciation to an anonymous reviewer who provides critical comments to the previous version of the paper which was presented at the Vietnam’s Business and Economics Research Conference on 16-18th November 2017 TABLE OF CONTENTS CHAPTER INTRODUCTION 1.1 An overview of asset pricing model 1.2 Research questions 1.3 Research objectives 1.4 A choice of Australia in this study CHAPTER LITERATURE REVIEW 2.1 Theoretical literature 2.1.1 Modern Portfolio Theory 2.1.2 Capital Allocation Line 2.1.3 Capital Asset Pricing Model 2.1.4 The Downside of the CAPM 11 2.1.5 Fama-French’s Three factor Model 12 2.1.6 Cahart’s Four factor Model 13 2.1.7 Fama-French’s Five factor Model 13 2.2 Empirical literature 14 CHAPTER DATA AND METHODOLOGY 24 3.1 A brief description of the method 24 3.2 Data requirements and data sources 25 3.3 Portfolio constructions 26 3.3.1 Ten beta-sorted portfolios and Ten idiosyncratic risk-sorted portfolios 26 3.3.2 The 25 Fama-French size and book-to-market portfolios 28 3.3.3 Industry portfolios 30 3.4 Calculations of portfolio’s beta and portfolio’s return 30 3.4.1 Pooled regression 30 3.4.2 Fama-MacBeth regression 31 CHAPTER EMPIRICAL RESULTS 32 4.1 Pooled regression’s result 32 4.2 Fama-MacBeth regression’s result 36 4.3 Result’s discussion 39 CHAPTER CONCLUDING REMARKS AND POLICY IMPLICATIONS 40 5.1 Concluding remarks 40 5.2 Policy implications 42 References 44 Appendix 48 Appendix 52 Appendix 53 Appendix 54 Appendix 58 LIST OF TABLES Table 2-1 Factor classification 18 Table 2-2 Approaches to Portfolio Formations 22 Table 3-1 Summary of the number of firms in 10 beta-sorted portfolios and in 10 idiosyncratic risk-sorted portfolios 27 Table 3-2 Summary of the number of firms in the 25 Fama-French size and book-to-market portfolios 29 Table 3-3 Summary of the number of firms in industry portfolios 30 Table 4-1 Regression results use linear regression with panel-corrected standard errors method 33 Table 4-2 Regression results use Fama-MacBeth regression to value weighted return manipulation 37 Table 4-3 Regression results use Fama-MacBeth regression to equal weighted return manipulation 38 Table 3: This table obtains the difference in the estimated ten idiosyncratic risk – sorted portfolio market beta of announcement days and nonannouncement days βnon is chosen as reference βann and βnon are portfolio market betas in announcement day and non-announcement day, respectively The p-values are reported in the parentheses The portfolio return is weighted average by market value of its all stock returns * significant at 10% level, ** significant at 5% level, *** significant at 1% level Beta Low βnon -0.679 0.163 βann - βnon 0.185 0.053 (0.289) (0.264) Source: Author’s estimates 0.408 0.030 (0.281) 0.610 0.012 (0.485) 0.781 0.028 (0.174) 0.964 0.031 (0.007)*** 1.186 0.023 (0.210) 1.500 -0.015 (0.684) 1.937 -0.061 (0.326) High 3.291 -0.287 (0.102) Table 4: This table obtains the difference in the estimated ten idiosyncratic risk – sorted portfolio market beta of announcement days and nonannouncement days βnon is chosen as reference βann and βnon are portfolio market betas in announcement day and non-announcement day, respectively The p-values are reported in the parentheses The portfolio return is mean of its all stock returns * significant at 10% level, ** significant at 5% level, *** significant at 1% level Beta βnon βann - βnon Low 0.555 0.664 0.029 0.068 (0.508) (0.059)* Source: Author’s estimates 0.798 0.089 (0.068)* 0.939 0.093 (0.003)*** 1.082 0.034 (0.440) 1.110 0.021 (0.597) 1.180 -0.051 (0.288) 1.134 0.015 (0.818) 1.240 0.027 (0.789) High 1.774 -0.063 (0.488) 49 Table 5: This table obtains the difference in the estimated 25 Fama and French size and book-to-market portfolio market beta of announcement days and non-announcement days βnon is chosen as reference βann and βnon are portfolio market betas in announcement day and non-announcement day, respectively The pvalues are reported in the parentheses The portfolio return is weighted average by market value of its all stock returns * significant at 10% level, ** significant at 5% level, *** significant at 1% level Beta βnon βann - βnon Growth Small 0.713 -0.054 (0.617) βnon 0.840 βann - βnon -0.013 (0.896) βnon 0.893 βann - βnon -0.131 (0.144) βnon 0.866 βann - βnon -0.088 (0.235) βnon Large 1.046 βann - βnon -0.050 (0.138) Source: Author’s estimates 0.961 -0.228 (0.353) 0.832 -0.170 (0.078)* 0.823 -0.084 (0.340) 0.691 0.029 (0.247) 0.919 -0.0006 (0.966) 0.291 0.353 (0.398) 0.512 0.113 (0.377) 0.755 -0.068 (0.349) 0.570 0.075 (0.165) 1.039 0.013 (0.136) 0.851 -0.077 (0.636) 0.479 0.193 (0.192) 0.695 -0.026 (0.733) 0.669 0.020 (0.562) 1.031 -0.053 (0.289) Value 0.722 -0.191 (0.487) 0.589 0.284 (0.150) 0.691 -0.051 (0.412) 0.790 -0.060 (0.273) 1.000 0.077 (0.052)* Table 6: This table obtains the difference in the estimated 25 Fama and French size and book-to-market portfolio market beta of announcement days and non-announcement days βnon is chosen as reference βann and βnon are portfolio market betas in announcement day and non-announcement day, respectively The pvalues are reported in the parentheses The portfolio return is mean of its all stock returns * significant at 10% level, ** significant at 5% level, *** significant at 1% level Beta βnon βann - βnon Small βnon βann - βnon βnon βann - βnon βnon βann - βnon βnon βann - βnon Large Growth 0.753 -0.120 (0.393) 0.878 -0.137 (0.203) 0.924 -0.121 (0.107) 0.869 -0.082 (0.285) 0.957 -0.064 (0.301) 0.963 -0.089 (0.671) 0.839 -0.240 (0.088)* 0.817 -0.077 (0.440) 0.718 0.014 (0.593) 0.924 -0.046 (0.030)** 0.486 0.056 (0.745) 0.457 0.122 (0.267) 0.749 -0.058 (0.557) 0.661 -0.013 (0.833) 0.848 -0.027 (0.268) 0.678 0.193 (0.370) 0.575 0.068 (0.607) 0.702 -0.024 (0.809) 0.613 0.029 (0.526) 0.867 -0.027 (0.457) Value 0.921 -0.183 (0.432) 0.779 -0.011 (0.900) 0.724 -0.081 (0.136) 0.699 -0.040 (0.309) 0.907 0.024 (0.488) Source: Author’s estimates 50 Table 7: This table obtains the difference in the estimated 11 industry portfolio market beta of announcement days and non-announcement days βnon is chosen as reference βann and βnon are portfolio market betas in announcement day and non-announcement day, respectively The portfolio return is weighted average by market value of its all stock returns The p-values are reported in the parentheses.* significant at 10% level, ** significant at 5% level, *** significant at 1% level Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Information Technology Materials Real Estate Telecommunication Services Utilities Source: Author’s estimates βnon 0.769 0.681 1.098 1.015 0.636 0.955 0.714 1.394 0.726 0.508 0.591 βann - βnon -0.045 -0.011 -0.029 0.013 -0.012 -0.044 -0.003 -0.017 0.085 0.029 -0.014 p-value (0.301) (0.765) (0.369) (0.615) (0.832) (0.156) (0.926) (0.470) (0.156) (0.683) (0.726) Table 8: This table obtains the difference in the estimated 11 industry portfolio market beta of announcement days and non-announcement days βnon is chosen as reference βann and βnon are portfolio market betas in announcement day and non-announcement day, respectively The portfolio return is mean of its all stock returns The p-values are reported in the parentheses.* significant at 10% level, ** significant at 5% level, *** significant at 1% level Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Information Technology Materials Real Estate Telecommunication Services Utilities Source: Author’s estimates βnon 0.650 0.700 1.218 0.688 0.727 0.877 0.885 1.285 0.596 0.683 0.851 βann - βnon 0.024 -0.121 0.006 0.024 0.047 -0.068 -0.032 0.016 -0.028 0.142 -0.130 p-value (0.469) (0.304) (0.809) (0.581) (0.520) (0.172) (0.702) (0.585) (0.260) (0.084)* (0.307) 51 Appendix Table 1: The table reports the number of announcement-days as well as non-announcement days from 2007 to 2016 Data is available and judged by Forex Factory website (http://www.forexfactory.com) 2007 a-day 179 n-day 81 Source: Forex Factory 2008 194 68 2009 195 66 2010 193 68 2011 195 65 2012 192 69 2013 189 72 2014 184 77 2015 185 76 2016 167 50 Table 2: The table illustrates kinds of announcements in relation to macroeconomic issues as well as their typical examples employed in this study Data is available and judged by Forex Factory website (http://www.forexfactory.com) Type of announcement Growth Inflation Employment Central Bank Housing Consumer Surveys Business Surveys Speeches Source: Forex Factory Example Change in the total value of new credit issued to consumers and businesses Change in the price of goods and services purchased by consumers Percentage of the total work force that is unemployed and actively seeking employment during the previous month Interest rate charged on overnight loans between financial intermediaries Measurement change in the number of new building approvals issued Measurement percentage that consumers expect the price of goods and services to change during the next 12 months Measurement level of a diffusion index based in surveyed service-based companies Reserve Bank of Australia Deputy Governor spoke at the Australian Securities Investment Commission Annual Forum 52 Appendix Primary and secondary models used for estimating cost of equity by international regulator Regulator Primary model Australia Germany New Zealand USA Canada UK Australian Energy Regulator The Federal Network Agency The Commerce Commission New York State Public Utilities Commission The Ontario Energy Board The Office of Gas and Electricity Markets (AER) (FNA) (CC) (NYSPUC) CAPM CAPM/ RPM CAPM DDM Secondary model Other use of DDM (OEB) (Ofgem) RPM CAPM Cross-check on MRP Cross-check* CAPM Cross-check on MRP Cross-check on MRP *On the overall CoE but not for individual firms Source: Sudarsanam, Kaltenbronn, & Park (2011) Notes: CAPM: Sharpe-Lintner-Black capital asset pricing model RPM: Risk premium model DDM: Dividend discount model 53 Appendix Table 1: The table reports estimates and R-squared values of panel regression Ri, t +1 – Rf, t + = α0 + γ1Dt + + γ2βi, t + γ3Dt + 1βi, t + µi, t + using linear regression with panel-corrected standard errors method By default, this estimation method fixes the heteroskedastic and contemporaneously correlated across panels which classified by trading day The table contains results seperately on four distinct types of portfolios (beta sorted portfolio, idiosyncratic risk-sorted portfolio, 25 Fama-French size and book-to-market portfolio and industry portfolio) and seperately on two different approaches: value weighted return manipulation and equal weighted return manipulation p-values are reported in parentheses *significant at 10% level, ** significant at 5% level, *** significant at 1% level Pooled regression (Value weighted) Ten-beta sorted portfolio Intercept Beta Aday*Beta R2 -0.0002651 -0.0003112 -0.0002004 0.0665 (0.633) (0.403) (0.778) Idiosyncratic risk-sorted portfolio Intercept Beta Aday*Beta R2 0.0018833 -0.000875 -0.0008992 0.0666 (0.052)* (0.176) (0.132) 25 Fama and French size and book-to-market portfolio Intercept Beta Aday*Beta R2 0.0075025 -0.0049475 -0.0024687 0.0777 (0.000)*** (0.052)* (0.252) Industry portfolio Intercept Beta Aday*Beta R2 0.0002893 -0.0009589 0.0011206 0.0511 (0.632) (0.143) (0.251) Source: Author’s estimates Pooled regression (Equal weighted) Ten-beta sorted portfolio Intercept Beta Aday*Beta R2 -0.0001405 -0.000304 0.0001754 0.0138 (0.825) (0.084)* (0.555) Idiosyncratic risk-sorted portfolio Intercept Beta Aday*Beta R2 -0.0018712 0.0014571 0.0001095 0.0588 (0.045)** (0.025)** (0.893) 25 Fama and French size and book-to-market portfolio Intercept Beta Aday*Beta R2 -0.0018552 0.001686 0.0000586 0.0527 (0.064)* (0.057)* (0.960) Industry portfolio Intercept Beta Aday*Beta R2 0.0004169 -0.0011796 0.0007027 0.0215 (0.742) (0.280) (0.524) 54 In contrast to compare difference in coefficients of Aday*Beta (or the variable denoted by Dt+1βAi,t) and NotAday*Beta (or the variable denoted by (1 - Dt+1)βNi, t) in the empirical result section, this is done by considering by verifying the significance of Aday*Beta in the Table The reason behind is that because of the independent variable – beta was estimated regardless types of day so it is sufficient to answer whether or not stock return is explained by its market beta 55 Table 2: The table reports estimates and R-squared values of panel regression Ri, t +1 – Rf, t + = α0 + γ1βi, t + µi, t + using Fama MacBeth regresssion The table contains results separately on four distinct types of portfolios (beta sorted portfolio, idiosyncratic risk-sorted portfolio, 25 Fama-French size and book-to-market portfolio and industry portfolio) The portfolio return is weighted average by market value of its all stock returns p-values are reported in parentheses *significant at 10% level, ** significant at 5% level, *** significant at 1% level Fama-MacBeth regression (Value weighted) Ten-beta sorted portfolio InterceptA BetaA 0.0003181 -0.000017 (0.182) (0.957) N Intercept BetaN -6.10e-06 -0.0005966 (0.988) (0.218) InterceptA – InterceptN BetaA - BetaN 0.0003242 0.0005795 (0.501) (0.349) Idiosyncratic risk-sorted portfolio InterceptA BetaA 0.0027319 -0.0012836 (0.000)*** (0.001)*** InterceptN BetaN 0.0022609 -0.0012959 (0.006)*** (0.035)** A N Intercept – Intercept BetaA - BetaN 0.000471 0.0000123 (0.620) (0.987) 25 Fama and French size and book-to-market portfolio InterceptA BetaA 0.0090238 -0.0073734 (0.000)*** (0.000)*** InterceptN BetaN 0.0093792 -0.0074188 (0.000)*** (0.004)*** InterceptA – InterceptN BetaA - BetaN -0.0003554 0.0000454 (0.902) (0.988) Industry portfolio InterceptA BetaA 0.0003155 -0.0001394 (0.373) (0.766) InterceptN BetaN 0.0006777 -0.0013422 (0.224) (0.064)* A N Intercept – Intercept BetaA - BetaN -0.0003622 0.0012028 (0.603) (0.191) Avg.R2 0.3341 Avg.R2 0.3190 Avg.R2 0.1794 Avg.R2 0.1725 Avg.R2 0.0749 Avg.R2 0.0660 Avg.R2 0.2156 Avg.R2 0.1923 Source: Author’s estimates 56 Table 3: The table reports estimates and R-squared values of panel regression Ri, t +1 – Rf, t + = α0 + γ1βi, t + µi, t + using Fama MacBeth regresssion The table contains results separately on four distinct types of portfolios (beta sorted portfolio, idiosyncratic risk-sorted portfolio, 25 Fama-French size and book-to-market portfolio and industry portfolio) The portfolio return is mean of its all stock returns p-value are reported in parentheses *significant at 10% level, ** significant at 5% level, *** significant at 1% level Fama-MacBeth regression (Equal weighted) Ten-beta sorted portfolio InterceptA BetaA 0.0003383 -0.0004869 (0.123) (0.004)*** N Intercept BetaN -0.0001851 -0.000266 (0.623) (0.348) InterceptA – InterceptN BetaA - BetaN 0.0005234 -0.0002209 (0.235) (0.510) Idiosyncratic risk-sorted portfolio InterceptA BetaA -0.0012111 0.0010567 (0.001)*** (0.010)*** InterceptN BetaN -0.0022856 0.0018522 (0.000)*** (0.009)*** InterceptA – InterceptN BetaA - BetaN 0.0010745 -0.0007954 (0.133) (0.334) 25 Fama and French size and book-to-market portfolio InterceptA BetaA -0.0005999 0.0005485 (0.224) (0.295) InterceptN BetaN -0.0011684 0.0010077 (0.178) (0.271) InterceptA – InterceptN BetaA - BetaN 0.0005684 -0.0004592 (0.567) (0.663) Industry portfolio InterceptA BetaA 0.0000703 -0.0002847 (0.838) (0.476) InterceptN BetaN -0.000191 -0.000497 (0.753) (0.491) A N Intercept – Intercept BetaA - BetaN 0.0002613 0.0002123 (0.707) (0.794) Avg.R2 0.2329 Avg.R2 0.2326 Avg.R2 0.2153 Avg.R2 0.1995 Avg.R2 0.0720 Avg.R2 0.0693 Avg.R2 0.1175 Avg.R2 0.1216 Source: Author’s estimates 57 10 Appendix In this section, this study aggregates types of announcement from the Prime Minister or the Governor of the Reserve Bank of Australia which is already mentioned to some groups This study classify nine of them into fours group: macro event-related group, micro event-related group, financial event-related group and economic event-related group Particularly, macro event-related group consists of news about growth, inflation, employment, Central Bank, bonds and speeches Micro event-related group is constructed from news about housing, consumer survey and business survey Financial event-related group contains news about Central Bank and bonds Economic event-related group is characterized by news about growth, inflation, employment, housing, consumer surveys, business surveys and speeches For each group, the results are installed in four tables The first two tables demonstrate result in which beta is estimated separately for a-day and n-day, so call conditional beta A claim “Yes” in the pooled regression row means that coefficient of βNi, t and βAi, t in the following model are different and βAi, t is significant Ri, t +1 – Rf, t + = α0 + γ1Dt + + γ2(1- Dt + 1)βNi, t + γ3Dt + 1βAi, t + µi, t + and vice versa A claim “Yes” in the Fama-Macbeth regression row means that coefficient of βNi, t and βAi, t in the following model are different and βAi, t is significant Although the independent variables - β - in two following equations are denoted differently in the surface, they all equal in value RAi, t+1 – RAf, t+1 = α0 + γ0βAi, t + µi, t+1 RNi, t+1 – RNf, t+1 = α0 + γ0βNi, t + µi, t+1 and vice versa 58 The next two tables are stemmed from estimating beta regardless types of day, so call common beta A claim “Yes” in the pooled regression row means that coefficient γ3Dt + is statistically significant different from zero Ri, t +1 – Rf, t + = α0 + γ1Dt + + γ2βi, t + γ3Dt + 1βi, t + µi, t + and vice versa A claim “Yes” in the Fama-Macbeth regression row means that coefficient of βNi, t and βAi, t in the following model are different and βAi, t is significant RAi, t+1 – RAf, t+1 = α0 + γ0βAi, t + µi, t+1 RNi, t+1 – RNf, t+1 = α0 + γ0βNi, t + µi, t+1 and vice versa 59 Macro event-related group – conditional beta Pooled regression Fama-Macbeth regression Value weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No Yes No No 25 FF portfolio Yes No Industry portfolio No No Pooled regression Fama-Macbeth regression Equal weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio Yes No Industry portfolio No No Pooled regression Fama-Macbeth regression Value weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio No No Industry portfolio No No Pooled regression Fama-Macbeth regression Equal weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio No No Industry portfolio No No Macro event-related group – common beta 60 Micro event-related group – conditional beta Pooled regression Fama-Macbeth regression Value weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio No No Industry portfolio No No Pooled regression Fama-Macbeth regression Equal weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio Yes No Industry portfolio No No Pooled regression Fama-Macbeth regression Value weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio No No Industry portfolio No No Pooled regression Fama-Macbeth regression Equal weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No Yes No No 25 FF portfolio No No Industry portfolio No No Micro event-related group – common beta 61 Financial event-related group – conditional beta Pooled regression Fama-Macbeth regression Value weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio Yes No Industry portfolio Yes No Pooled regression Fama-Macbeth regression Equal weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio Yes No No No 25 FF portfolio No No Industry portfolio No No Pooled regression Fama-Macbeth regression Value weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No Yes No No 25 FF portfolio No No Industry portfolio No Yes Pooled regression Fama-Macbeth regression Equal weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio Yes No Yes No 25 FF portfolio No No Industry portfolio No Yes Financial event-related group – common beta 62 Economic event-related group – conditional beta Pooled regression Fama-Macbeth regression Value weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No Yes No No 25 FF portfolio No No Industry portfolio No No Pooled regression Fama-Macbeth regression Equal weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio Yes No Industry portfolio No No Pooled regression Fama-Macbeth regression Value weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio Yes No No No 25 FF portfolio No No Industry portfolio No No Pooled regression Fama-Macbeth regression Equal weighted return manipulation Ten-beta sorted portfolio Idiosyncratic risk-sorted portfolio No No No No 25 FF portfolio No No Industry portfolio No No Economic event-related group – common beta 63 ... research raise up a hypothesis that whether the single factor asset pricing model- CAPM is usable or not in calculation of a return on equity in Asia-Pacific in general or in Australia in particular... Minh City December 2017 DECLARATION I hereby declare, that the thesis entitled, Systematic Risk in the Capital Asset Pricing Model Australia: A Clinical Death? ” written and submitted by me in. .. countries including in the Asia Pacific region, Australia is the best candidate at least on the following aspects: (i) a substantially large volume of data for listed firms are available (more than