Anomalies, risk adjustment and seasonality: Australian evidence

Zhong, Angel; Limkriangkrai, Manapon; Gray, Philip

doi:10.1016/j.irfa.2014.09.004

Cited by 35 publications

(33 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As with most of the findings in the finance literature, other capital market anomalies such as liquidity (e.g., Chan and Faff 2005;Chai et al 2013), momentum (e.g., Demir et al 2004;Brailsford and O'Brien 2008), profitability (e.g., Dou et al 2013;Zhong et al 2014), and asset growth (e.g., Gray and Johnson 2011;Dou et al 2013) are also documented in Australia. As in the findings from other international markets, the three-factor model is unable to explain these anomalies.…”

Section: Introductionsupporting

confidence: 60%

See 1 more Smart Citation

A Better Model? An Empirical Investigation of the Fama–French Five‐factor Model in Australia

et al. 2016

Self Cite

View full text Add to dashboard Cite

Recently, Fama and French () propose a five‐factor model by adding profitability and investment factors to their three‐factor model. This model outperforms the three‐factor model previously proposed by Fama and French (). Using an extensive sample over the 1982–2013 period, we investigate the performance of the five‐factor model in pricing Australian equities. We find that the five‐factor model is able to explain more asset pricing anomalies than a range of competing asset pricing models, which supports the superiority of the five‐factor model. We also find that despite the results documented by Fama and French (), the book‐to‐market factor retains its explanatory power in the presence of the investment and profitability factors. Our results are robust to alternative factor definitions and the formation of test assets. The study provides a strong out‐of‐sample test of the model, adding to the comparative evidence across international equity markets.

show abstract

Section: Introductionsupporting

confidence: 60%

“…ROA is measured as EBIT (earnings before interest and taxes) scaled by lagged total assets. GP is defined as earnings before interest, taxes, depreciation, and amortization scaled by lagged total assets (following Zhong et al ()). NOA is the operating assets less operating liabilities, scaled by lagged total assets.…”

Section: Methodsmentioning

confidence: 99%

A Better Model? An Empirical Investigation of the Fama–French Five‐factor Model in Australia

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Specifically, the mean HML is highly significant, while SMB is insignificant. Most importantly, our profitability and 6 The justification for these modifications is outlined in detail in a series of recent papers (Brailsford et al 2012a;Zhong et al 2014;Chiah et al 2016). 7 For any variable in this paper that requires accounting information, we ensure at least 6 months have elapsed since the balance date to allow for reporting lags.…”

Section: B Constructing Asset Pricing Factorsmentioning

confidence: 99%

“…There is a respectable body of work demonstrating the importance of multi-factor models incorporating size and BM risk factors. 1 In light of a series of recent papers that document cross-sectional regularities relating to profitability (Dou et al 2013;Zhong et al 2014) and investment (Bettman et al 2011;Gray and Johnson 2011;Dou et al 2013), Chiah et al (2016) provide cautious support for the superiority of a five-factor model.…”

Section: Introductionmentioning

confidence: 99%

Explaining Anomalies in Australia with a Five‐factor Asset Pricing Model

Huynh

2017

Int Rev Finance

View full text Add to dashboard Cite

This paper compares the ability of three‐factor and five‐factor asset pricing models to explain the apparent profitability of a broad selection of anomalies in Australian equity returns. Rather than examining the fit of each model to common test portfolios, our focus is on the spread return to long–short trading strategies designed around so‐called anomalies. After documenting significant spread returns to 16 anomalies (including several not previously studied in Australia), the empirical analysis provides cautious support that the recently‐proposed investment and profitability factors have a role to play. The number of anomalies that remains after risk adjustment decreases under the five‐factor model. Further, while the magnitude of reduction in alpha is modest, our testing shows that it is statistically significant in many cases. However, both three‐factor and five‐factor models repeatedly fail the Gibbons, Ross, and Shanken's (1989) (GRS) test, suggesting that the quest for a better asset pricing model is not yet complete.

show abstract

“…5 We utilize the March ratings each year; hence, the holding period is from the current year's April to the following year's March. 6 For detailed factor construction methodology and recent empirical seasonality evidence, please refer toZhong et al (2014).…”

mentioning

confidence: 99%

Environmental, Social, and Governance (ESG) Profiles, Stock Returns, and Financial Policy: Australian Evidence

2016

Self Cite

View full text Add to dashboard Cite

This study investigates the independent effects of environmental (E), social (S), corporate governance (G), and the composite ESG ratings on stock returns and corporate financing decisions of the largest stocks in the Australian equity market. Firms with high composite ESG ratings tend to increase their leverage. For the individual ratings, we find different inferences: firms with low E and high G ratings tend to raise less debt. Firms with high G ratings hold less cash, while those with low G ratings have lower dividend payouts. S ratings have no impact on corporate financing decisions. There appears to be no significant difference in risk‐adjusted returns for portfolios based on ESG ratings, effectively indicating that there is no cost of ESG investment.

show abstract

Anomalies, risk adjustment and seasonality: Australian evidence

Cited by 35 publications

References 46 publications

A Better Model? An Empirical Investigation of the Fama–French Five‐factor Model in Australia

A Better Model? An Empirical Investigation of the Fama–French Five‐factor Model in Australia

Explaining Anomalies in Australia with a Five‐factor Asset Pricing Model

Environmental, Social, and Governance (ESG) Profiles, Stock Returns, and Financial Policy: Australian Evidence

Contact Info

Product

Resources

About