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Modelling systematic risk and return using accounting-based information

Modelling systematic risk and return using accounting-based information The study explored the linkages between the financial parameters of a firm derived from reported accounting data, and systematic risk as described by estimated market beta coefficients. The relationships between financial variables and risk proposed by previous theoretical and empirical research were tested for 135 JSE-listed Industrial companies, in each of three consecutive 5-year periods. Correlation analysis and stepwise multiple regression were used to establish significant explanatory beta models. Strong positive associations with risk were found for measures of firm growth, profitability, leverage and the variabilities of earnings and cashflows. Significant negative relationships emerged for liquidity, stock turnover and dividend yield.Secondly, the linkages between the same financial parameters and observed equity returns were tested in a multifactor asset-pricing model for the same sample. Measures of firm growth, profitability and size were positively correlated to share returns, while the variabilities of earnings and cashflows as well as debtors' collection period were negatively related.For both betas and equity returns, correlations with financial parameters improved for portfolios of shares. Several of the significant financial variables were derived from cashflow-based data, as opposed to standard accrual-accounting values. While significant regression models emerged for risk and return in each of the 5-year analysis periods, they were non-stationary across the periods. Consequently, fairly poor predictive performance was observed for the models over consecutive time intervals. Nevertheless, it was apparent that certain classes of fundamental financial data were strongly related to beta as a measure of systematic risk, and to ex post share returns. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Investment Analysts Journal Taylor & Francis

Modelling systematic risk and return using accounting-based information

Investment Analysts Journal , Volume 25 (43): 18 – Dec 1, 1996

Modelling systematic risk and return using accounting-based information

Investment Analysts Journal , Volume 25 (43): 18 – Dec 1, 1996

Abstract

The study explored the linkages between the financial parameters of a firm derived from reported accounting data, and systematic risk as described by estimated market beta coefficients. The relationships between financial variables and risk proposed by previous theoretical and empirical research were tested for 135 JSE-listed Industrial companies, in each of three consecutive 5-year periods. Correlation analysis and stepwise multiple regression were used to establish significant explanatory beta models. Strong positive associations with risk were found for measures of firm growth, profitability, leverage and the variabilities of earnings and cashflows. Significant negative relationships emerged for liquidity, stock turnover and dividend yield.Secondly, the linkages between the same financial parameters and observed equity returns were tested in a multifactor asset-pricing model for the same sample. Measures of firm growth, profitability and size were positively correlated to share returns, while the variabilities of earnings and cashflows as well as debtors' collection period were negatively related.For both betas and equity returns, correlations with financial parameters improved for portfolios of shares. Several of the significant financial variables were derived from cashflow-based data, as opposed to standard accrual-accounting values. While significant regression models emerged for risk and return in each of the 5-year analysis periods, they were non-stationary across the periods. Consequently, fairly poor predictive performance was observed for the models over consecutive time intervals. Nevertheless, it was apparent that certain classes of fundamental financial data were strongly related to beta as a measure of systematic risk, and to ex post share returns.

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Publisher
Taylor & Francis
Copyright
© 1996 Taylor and Francis Group, LLC
ISSN
2077-0227
eISSN
1029-3523
DOI
10.1080/10293523.1996.11082360
Publisher site
See Article on Publisher Site

Abstract

The study explored the linkages between the financial parameters of a firm derived from reported accounting data, and systematic risk as described by estimated market beta coefficients. The relationships between financial variables and risk proposed by previous theoretical and empirical research were tested for 135 JSE-listed Industrial companies, in each of three consecutive 5-year periods. Correlation analysis and stepwise multiple regression were used to establish significant explanatory beta models. Strong positive associations with risk were found for measures of firm growth, profitability, leverage and the variabilities of earnings and cashflows. Significant negative relationships emerged for liquidity, stock turnover and dividend yield.Secondly, the linkages between the same financial parameters and observed equity returns were tested in a multifactor asset-pricing model for the same sample. Measures of firm growth, profitability and size were positively correlated to share returns, while the variabilities of earnings and cashflows as well as debtors' collection period were negatively related.For both betas and equity returns, correlations with financial parameters improved for portfolios of shares. Several of the significant financial variables were derived from cashflow-based data, as opposed to standard accrual-accounting values. While significant regression models emerged for risk and return in each of the 5-year analysis periods, they were non-stationary across the periods. Consequently, fairly poor predictive performance was observed for the models over consecutive time intervals. Nevertheless, it was apparent that certain classes of fundamental financial data were strongly related to beta as a measure of systematic risk, and to ex post share returns.

Journal

Investment Analysts JournalTaylor & Francis

Published: Dec 1, 1996

References