Includes bibliographical references. / Style anomalies comprise patterns and relationships found in the cross-section of stock returns data, which contradict the existing asset-pricing models. They have proven to be reasonably effective at explaining the return-genera ting process of ordinary shares, and have bro ad uses within modern finance. Empirically, style anomalies are found to have statistically significant rewards in individual markets and s mall market groupings, and are found to be significant at a sector level on a global scale, but have not been tested at a firm level on a global scale. The aim of this study is to explain the cross-section of returns of the 1468 largest global firms by market capitalisation. The worldwide study considers stocks from 53 different countries and 112 industries, and investigates the end of month return forecasting power of 44 different firm-specific attributes over the period August 2003 to August 2013. A univariate analysis is performed through a cross-sectional regression of the forward stock re turns on the firm-specific attributes in a similar method to Fama and MacBeth (1973). A ‘Full Data’ regression is also conducted, and results are presented both before and after a beta-adjustment for market risk. Following this, a multivariate analysis is conducted and a forward stepwise procedure is used to construct a multi-factor model. According to the results of this study, style anomalies exist and have a statistically significant reward at a firm level on a global scale. In a univariate setting there are 25 firm-specific style factors that have a significant return payoff at a 5% level of significance. The specific style groups containing significant firm-specific attributes are the Value, Growth, Momentum, Size and Liquidity, Leverage, and Emerging Market groupings. Ten attributes within these style groupings are found to be robust as they are highly significant both before and after beta-adjustment, and within both a univariate and multivariate setting, namely: EBITDA to Share Price (EBP), Emerging Market (EM), CAPEX to Sales (CXS), Sales to Total Assets (STA), Payout Ratio (PR), 24-month growth in Turnover by Volume (TVO24), Sales to Share Price (SP), 6-month growth in Earnings (E6), 1-month prior return (MOM1), and 3-month prior return (MOM3). This confirms that style effects exist both independently, in a univariate setting, and in a multi-factor model. The results of this study show that the Value and Emerging Market styles have the highest cumulative payoffs over the 10-year period, and the evidence of strong correlation between attributes within specific styles gives further validation to the traditional style groupings. The behaviour of, and relationships between the firm-specific style factors give great insight into the payoffs to investing in different style factors over time, and are key to the construction of a multi-factor model. The fifteen firm-specific style factors that are significant in a multivariate setting form the core of a multi-factor style model, which can potentially be used to explain a degree of unexplained returns, predict returns, give insight into global market behaviour, and price global assets for use within a global portfolio. These firm-specific attributes include: EBITDA to Share Price (EBP), Emerging Market (EM), CAPEX to Sales (CXS), Sales to Total Assets (STA), Payout Ratio (PR), 24-month growth in Turnover by Volume (TVO24), Sales to Share Price (SP), 6-month growth in Earnings (E6), 1-month prior return (MOM1), 3-month prior return (MOM3), the natural log of Enterprise Value (LNEV), Interest Cover before Tax (ITBT), 6-month prior return (MOM6), Price-to-Book value (PTB), and Cash Flow-to-Price (CFP).
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/13081 |
| Date | January 2014 |
| Creators | Baars, Monique |
| Contributors | Van Rensburg, Paul |
| Publisher | University of Cape Town, Faculty of Commerce, Department of Finance and Tax |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Master Thesis, Masters, MCom |
| Format | application/pdf |
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