In this dissertation, I examine investors' use of balance sheet information to infer earnings management constraint and the extent to which they utilize that information to assess the quality of subsequent earnings surprises. Ex-ante constrained firms may not have sufficient ability to manage earnings towards desired earnings thresholds and thus their reported earnings surprises are more likely to be the result of real performance. Ex-ante flexible firms, however, have more room to manage earnings and so it becomes less clear to investors whether the reported earnings surprises are the result of real performance or earnings management. My tests provide mixed support for the constraint theory. I find evidence that the market reacts more to small positive earnings surprises when they are reported by ex-ante constrained firms than when reported by ex-ante flexible firms. This suggests that the market interprets the earnings surprise reported by constrained firms to be of higher quality. However, I also find that earnings surprises reported by ex-ante constrained firms are no more persistent with regard to one-year-ahead earnings than those reported by ex-ante flexible firms, a result that is not consistent with the differential reaction to earnings surprises.

Financial analysts are considered inefficient when they do not fully incorporate relevant information into their forecasts. In this dissertation, I investigate differences in the observable efficiency of analysts' earnings forecasts between firms that consistently meet or exceed analysts' earnings expectations and those that do not. I then analyze the extent to which the market incorporates this (in)efficiency into its earnings expectations. Consistent with my hypotheses, I find that analysts are relatively less efficient with respect to prior returns for firms that do not consistently meet expectations than for firms that do follow such a strategy, especially when prior returns convey bad news. However, forecast errors for firms that consistently meet expectations do not appear to be serially correlated to a greater extent than those for firms that do not consistently meet expectations. It is not clear whether the market considers such inefficiency when setting its own expectations. While the evidence suggests they may do so in the context of a shorter historical pattern of realized forecast errors, other evidence suggests they may not distinguish between predictable and surprise components of forecast error when the historical forecast error pattern is more established.

Financial analysts are considered inefficient when they do not fully incorporate relevant information into their forecasts. In this dissertation, I investigate differences in the observable efficiency of analysts' earnings forecasts between firms that consistently meet or exceed analysts' earnings expectations and those that do not. I then analyze the extent to which the market incorporates this (in)efficiency into its earnings expectations. Consistent with my hypotheses, I find that analysts are relatively less efficient with respect to prior returns for firms that do not consistently meet expectations than for firms that do follow such a strategy, especially when prior returns convey bad news. However, forecast errors for firms that consistently meet expectations do not appear to be serially correlated to a greater extent than those for firms that do not consistently meet expectations. It is not clear whether the market considers such inefficiency when setting its own expectations. While the evidence suggests they may do so in the context of a shorter historical pattern of realized forecast errors, other evidence suggests they may not distinguish between predictable and surprise components of forecast error when the historical forecast error pattern is more established.

Despite extensive research on price and earnings estimations, there are still puzzling results that have not been resolved. One of the puzzles in price estimation is that multiples using earnings forecasts outperform multiples using the residual income model (Liu, Nissim and Thomas, 2002). This puzzle undermines the validity of theory-based valuation models, which are originated from valuation theory and have been developed over the century. The first two projects of this thesis address this puzzle and explain mathematically how the pricing error of a multiple is determined by the correlation coefficient between price and a value driver. The projects then demonstrate that the puzzle in Liu, Nissim and Thomas (2002) is caused by the bad selection of residual income models and, in fact, the majority of residual income models (i.e. well-chosen residual income models) actually outperform multiples using earnings forecasts in pricing error. When models are examined in terms of future return generation, residual income models again outperform multiples using earnings forecasts, providing evidence that theory-based valuation models are superior to rule-of- thumb based multiples in price and intrinsic value estimations. The third project addresses an issue in earnings estimation by cross-sectional models. Recently, Hou, van Dijk and Zhang (2012) and Li and Mohanram (2014) introduce cross-sectional models in earnings estimation and argue that their cross-sectional models produce better earnings forecasts than analyst forecasts. However, their models suffer from one fundamental problem of cross-sectional models: the loss of firm-specific information in earnings estimation (Kothari, 2001). In other words, cross-sectional models apply the same coefficients (i.e. the same earnings persistence and future prospects) to all firms to estimate their earnings forecasts. The third project of this thesis addresses this issue by proposing a new model, a conditional cross-sectional model, which allows the coefficient on earnings to vary across firms. By allowing firms to use different earnings coefficients (i.e. different earnings persistence and future prospects), the project shows that a conditional cross-sectional model improves a cross-sectional model in all dimensions: a) bias, accuracy and earnings response coefficient; b) unscaled and scaled earnings estimations; and c) across all forecast horizons. The thesis contributes to the price and earnings estimations literature. First, the thesis addresses the decade-old puzzle in price estimation and rectifies the previous misunderstanding of valuation model performance. By demonstrating the superiority of theory-based valuation models over rule-of-thumb based multiples, the thesis encourages further development of theory-based valuation models. Second, in earnings estimation, the thesis provides future researchers a new model, which overcomes the fundamental problem of cross-sectional models in earnings estimation while keeping their advantages. In sum, the thesis improves the knowledge and practices of price and earnings estimations.

This dissertation investigates the relation between firm disclosure, analyst forecast bias, and the cost of equity capital (COEC). Since analyst forecast bias is associated with both implied COEC estimates and disclosure, it is important to control for or remove it from COEC estimates when estimating the relation between disclosure and ex ante expected returns. I begin my analysis by predicting and removing systematic ex ante bias from analyst forecasts to produce de-biased analyst forecasts that better proxy for the market’s ex ante earnings expectations. I use these de-biased analyst forecasts to produce estimates of ex ante expected returns, both at the portfolio- and the firm-level. In addition, I develop a novel estimate of ex ante expected returns by applying Vuolteenaho’s (2002) return decomposition framework to ex post realized returns and accounting data. Finally, using several techniques to control for analyst forecast bias and self-selection bias, I find theoretically consistent evidence of a negative association between regular disclosure and ex ante expected returns. I predict and show that inferences can change when analyst forecast bias is controlled for.

This dissertation investigates the relation between firm disclosure, analyst forecast bias, and the cost of equity capital (COEC). Since analyst forecast bias is associated with both implied COEC estimates and disclosure, it is important to control for or remove it from COEC estimates when estimating the relation between disclosure and ex ante expected returns. I begin my analysis by predicting and removing systematic ex ante bias from analyst forecasts to produce de-biased analyst forecasts that better proxy for the market’s ex ante earnings expectations. I use these de-biased analyst forecasts to produce estimates of ex ante expected returns, both at the portfolio- and the firm-level. In addition, I develop a novel estimate of ex ante expected returns by applying Vuolteenaho’s (2002) return decomposition framework to ex post realized returns and accounting data. Finally, using several techniques to control for analyst forecast bias and self-selection bias, I find theoretically consistent evidence of a negative association between regular disclosure and ex ante expected returns. I predict and show that inferences can change when analyst forecast bias is controlled for.

This study investigates whether auditors respond to earnings management pressure created by analyst forecasts. Analyst forecasts create an important earnings target for management, and professional standards direct auditors to consider how this pressure could affect their clients. Using annual analyst forecasts available during the planning phase of the audit, I examine whether this form of earnings management pressure affects clients’ financial statement misstatements. Next, I investigate whether auditors respond to earnings forecast pressure through audit fees and reporting delay. I find that higher levels of analyst forecast pressure increase the likelihood of client restatement. I also find that auditors charge higher audit fees and delay the issuance of the audit report in response to pressure from analyst expectations. Finally, I find that when audit clients are subject to high analyst forecast pressure, a high audit fee response by auditors mitigates the likelihood of client misstatements.

Comparability is a desirable qualitative characteristic of financial information and critical for financial statement users' ability to identify and understand similarities and differences in financial results among reporting entities. Yet, little research explicitly considers either the determinants or benefits of comparability because of difficulty in identifying and measuring the theoretical construct of comparability. Further, the widespread global adoption of IFRS, a relatively homogenous set of accounting standards, is expected to increase comparability among companies that operate in different national jurisdictions. However, prior studies that examine the average impact of mandatory IFRS adoption on comparability find mixed results. I hypothesized that the impact of mandatory IFRS adoption on comparability varies with managers' reporting incentives and differences between countries' domestic standards and IFRS. Using listed firms from 34 countries, I documented that comparability under non-IFRS domestic standards is higher in countries that provide strong reporting incentives (i.e. countries with strict enforcement regimes or high earnings transparency). Additionally, I found an increase in comparability following IFRS adoption (relative to a control sample of non-adopters) in countries that provide strong reporting incentives or with large domestic GAAP-IFRS differences. In contrast, I found evidence of a decrease following IFRS adoption (relative to a control sample of non-adopters) in countries with weak reporting incentives or with small domestic GAAP-IFRS differences. Finally, I showed that changes in comparability surrounding adoption are positively associated with changes in the quality of firms' information environments.

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Warren Buffet started an investment partnership of $100 in 1956 and has gone on to accumulate a personal net worth of over $60 billion. He started primarily as a value investor, and gradually changed over time to a strategy which uses the PEG ratio as its main tool. Peter Lynch, one of the most successful fund managers in history and had a compound annual growth rate of 29% for 13 years, was the man to first introduce the world to the PEG ratio. With such prominence, however, widespread use of previously successful strategies tend to render them ineffective due to everyone using them, and today the PEG ratio’s effectiveness as a valuation tool remains a topical debate between market commentators.This study sets out to determine if the PEG ratio adds value using JSE Main Board data from 2002 to 2012. Returns from five portfolios constructed directly from share quintiles based on PEG ratio magnitude are compared to returns of a portfolio constructed from the optimum quintile of value shares. The PEG ratio portfolio returns are examined based on 3 rebalancing period strategies, and on relative performance between the quintiles within each strategy.It is found that a 24 monthly rebalancing strategy provides superior returns to that of 3 or 12 monthly rebalancing for PEG quintiles of selected stocks. Furthermore, the lowest PEG ratio quintile in this strategy outperforms the value portfolio by a compound annual growth rate of 4.3%. The second lowest PEG ratio quintile portfolio performs slightly better to ensure that 40% of stocks selected based on the PEG ratio produced sustained superior returns to the optimum quintile value portfolio. / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted