Analysts' earnings forecasts in Australia and their implications in the extractive industry
thesisposted on 29.03.2022, 00:06 authored by Xiaomeng Chen
Analysts’ earnings forecasts have long been recognized as proxies for market expectations of future earnings because they are more accurate and have a stronger association with excess returns on the date of the earnings announcement than time-series models of earnings (Brown and Rozeff, 1978; Bradshaw et al., 2012). A large literature establishes the important role of analysts’ forecasts. For example, Kothari (2001) suggests that “almost all models of valuation either directly or indirectly use earnings forecasts” (p. 144). The predictability of share returns is also associated with the properties of analysts’ forecasts (Frankel and Lee, 1998; Jorgensen et al., 2012). Australian research has increasingly used analysts’ forecasts as proxies for expected earnings (Brown et al., 1999; Jackson, 2005; Beekes and Brown, 2006). While extensive research on analysts’ forecasts focuses largely on the U.S. market, few studies relate to analysts’ forecasts using Australian data. Motivated by the distinctiveness of the Australian setting with continuous disclosure to the stock market, and the prominence of the resources sector in the Australian economy, this thesis examines the properties of analysts’ forecasts in Australia. The aims and objectives of the thesis are addressed in three papers, that is, a comparison of the relative accuracy of alternative earnings forecast measures, and the impact of the intensity of exploration and evaluation (E&E) activities on analysts’ private information acquisition, forecast accuracy and bias in the extractive industry setting. Specifically, the first paper (in Chapter Two) compares the relative accuracy of the consensus forecast against the most recent forecast in the month before the earnings announcement. It investigates how the number of analysts following a company and the timeliness of an individual analyst’s forecast impacts on the differential accuracy of the consensus and the most recent forecast in Australia. The results suggest that, whilst in the late 1980s there is some evidence that the most recent forecast is more accurate than the consensus, since the early 1990s the accuracy of the consensus forecast has consistently outperformed the most recent forecast. The forecasting superiority of the consensus forecast can be attributed to the aggregation value of the consensus outweighing the small timing advantage of the most recent forecast over the short forecast horizon examined in this study. The second paper (in Chapter Three) examines whether analysts in the extractive industries adjust their private information development activities in response to the complexity of information about E&E activities. The results suggest that both the proportion of private information in their forecasts and the accuracy of their forecasts increase with the intensity of E&E activities. Additional analyses reveal that this effect is more pronounced for firms with substantial E&E activities but limited production activities, and that analysts’ private information development activities are mainly related to the capitalized E&E expenditure. The third paper (in Chapter Four) investigates whether the nature and extent of the uncertainty associated with E&E expenditure is a potential determinant of biases in analysts’ forecasts, and also investigates an inter-temporal pattern of analysts’ forecasts for firms with substantial E&E activities. The study finds that pessimism in analysts’ forecasts increases with the intensity of E&E activities, suggesting that the effect of analysts biasing their forecasts to gain information access from managers is more pronounced for firms with higher levels of E&E expenditure. Moreover, analysts are more likely to follow a pessimistic-to-pessimistic pattern in response to greater exploration intensity, consistent with analysts’ strategic use of pessimistic biases to increase their forecast error consistency.