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Author: Mikhail Pevzner
Publisher:
ISBN:
Category :
Languages : en
Pages : 46
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Book Description
We examine whether the properties of earnings forecasts - bias and dispersion are different across periods when macroeconomic forecasts are optimistic than non-optimistic, and whether this difference in analyst forecast optimism is stronger during recessionary periods. We find that the long-horizon earnings forecasts are more optimistically biased in periods when the macroeconomic forecasts are optimistically biased as well, and the bias is more pronounced during periods of recession. We also find that the long-horizon earnings forecast dispersion is lower in periods when the long-horizon macroeconomic forecasts are optimistic than in other periods. These results suggest that firms that meet or beat earnings forecasts when there is no recession and macroeconomic forecast is optimistic are likely to have opportunistically biased their long-term forecasts and walked them down, i.e. opportunistic; and that firms that meet or beat earnings forecasts when there is recession and macroeconomic forecast is optimistic are likely to be the ones that are positioned to perform well when the economy recovers. Consistent with this we find that premium for meeting or beating the analysts' earnings forecasts is highest in periods when there is recession and macroeconomic forecasts are optimistic; and there is no premium when there is no recession and macroeconomic forecast is optimistic. Collectively, the results show the interaction between the macroeconomic outlook and firm-level forecast properties.
Author: Mikhail Pevzner
Publisher:
ISBN:
Category :
Languages : en
Pages : 46
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Book Description
We examine whether the properties of earnings forecasts - bias and dispersion are different across periods when macroeconomic forecasts are optimistic than non-optimistic, and whether this difference in analyst forecast optimism is stronger during recessionary periods. We find that the long-horizon earnings forecasts are more optimistically biased in periods when the macroeconomic forecasts are optimistically biased as well, and the bias is more pronounced during periods of recession. We also find that the long-horizon earnings forecast dispersion is lower in periods when the long-horizon macroeconomic forecasts are optimistic than in other periods. These results suggest that firms that meet or beat earnings forecasts when there is no recession and macroeconomic forecast is optimistic are likely to have opportunistically biased their long-term forecasts and walked them down, i.e. opportunistic; and that firms that meet or beat earnings forecasts when there is recession and macroeconomic forecast is optimistic are likely to be the ones that are positioned to perform well when the economy recovers. Consistent with this we find that premium for meeting or beating the analysts' earnings forecasts is highest in periods when there is recession and macroeconomic forecasts are optimistic; and there is no premium when there is no recession and macroeconomic forecast is optimistic. Collectively, the results show the interaction between the macroeconomic outlook and firm-level forecast properties.
Author: Dmitri Yu Kantsyrev
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This study examines forecast errors in financial analysts' annual earnings forecasts and finds that analysts exhibit systematic optimism for a specific subset of companies. The magnitude of the analysts' optimistic forecast bias increases with the difficulty of the forecasting task, which is represented by statistical characteristics of a firm's earnings as well as the overall economic activity. We find that both the mean and median forecast errors are largest for companies with the most volatile earnings that move against or independently of the market earnings. We also develop a model of the analysts' forecasting behavior and provide evidence that the analysts' optimistic forecast error increases in periods of economic downturns, and somewhat slowly decreases throughout the forecast horizon. In contrast to most of the existing literature, which deals with samples, we analyze all available consensus as well as timely constructed forecasts for the 1987-2004 period.
Author: Sati P. Bandyopadhyay
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
Little attention has been paid to a principal decision context in which analysts' earnings forecasts are prepared, namely, as an input to their recommendations. We use two data sets, Value Line, USA, and Research Evaluation Service, Canada, and examine the importance of analysts' earnings forecasts for their stock price forecasts via three hypotheses: (1) analysts' earnings forecasts are important for their stock price forecasts; (2) analysts' long-term earnings forecasts are more important than their short-term earnings forecasts for their predictions of stock prices over a particular stock price forecast horizon; (3) the importance of analysts' earnings forecasts for their stock price forecasts rises as the joint earnings and stock price forecast horizon increases. We show that: (1) when the earnings forecast horizon is the next fiscal year, forecasted earnings explain only 30% of the variation in forecasted price; (2) the importance of forecasted earnings for forecasted price rises as the earnings forecast horizon increases; (3) in the long run, (i.e. three to five years hence), forecasted earnings explain about 60% of the variation in forecasted price. Decision usefulness is an ex ante concept, but tests regarding the usefulness of earnings for stock price generally have used actual (not expectational) data. Our evidence suggests that earnings expectations are decision useful, where the decision context is sell-side analysts' stock price forecasts. Our results are potentially important to users of sell-side analyst research reports. When a stock recommendation is accompanied only by short-run earnings forecasts, investors need to closely examine estimates of non-earnings variables to assess the quality of stock recommendations. In contrast, when stock recommendations are accompanied by both short-run and long-run earnings forecasts, investors need to examine estimates of non-earnings information variables less closely.
Author: Tim de Silva
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This paper quantifies the amount of noise and bias in analysts' forecast of corporate earnings at various horizons. We first show analyst forecasts outperform statistical forecasts at short-horizons, but underperform at longer horizons. We next decompose the relative accuracy of these forecasts into three components: (i) noise, (ii) bias and (iii) analysts' information advantage over statistical forecasts. We find the information advantage is constant across forecasting horizons, while both noise and bias are increase linearly. We then show most existing models lack a mechanism to account for these facts. To generate such a mechanism, we consider a parsimonious variant of the model of Patton and Timmermann (2010) with a noisy cognitive default and show it quantitatively fits the data. The intuition underlying this model is that forecasters rely on their biased and noisy defaults more at longer horizons, as rational forecasts are less accurate. This model also quantitatively matches two non-targeted empirical relationships: (i) analyst disagreement increases with horizon and (ii) noise is an increasing function of volatility.
Author: Hyungjin Cho
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
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Book Description
Using analysts' multi-period earnings forecasts, this paper investigates whether analyst forecast errors are related to asset growth and, if so, to what extent analysts' optimism for high-growth firms can explain the asset growth anomaly. We find that analyst forecasts are more optimistic for firms with high asset growth, particularly for longer-term forecasts (e.g., two- and three-year-ahead forecasts than one-year-ahead forecasts). We also find that analysts' optimism for high-growth firms is more pronounced for (1) firms that have maintained similar levels of growth in recent periods, (2) firms with higher information uncertainty, and (3) forecasts with longer forecast horizons (e.g., forecasts issued far before fiscal year end). Adding forecast errors to a growth-return regression substantially reduces the coefficient on asset growth, suggesting an important role of forecast errors in the growth anomaly. Path analysis suggests that analysts' long-term forecast errors, but not short-term forecast errors, are important mediators through which biased expectations about asset growth are incorporated into stock returns. Overall, our findings support the extrapolation bias explanation for the asset growth anomaly.
Author: Steven A. Sharpe
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
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Book Description
The long-term growth forecasts of equity analysts do not have well-defined horizons, an ambiguity of substantial import for many applications. I propose an empirical valuation model, derived from the Campbell-Shiller dividend-price ratio model, in which the forecast horizon used by the quot;marketquot; can be deduced from linear regressions. Specifically, in this model, the horizon can be inferred from the elasticity of the price-earnings ratio with respect to the long-term growth forecast. The model is estimated on industry- and sector-level portfolios of Samp;P 500 firms over 1983-2001. The estimated coefficients on consensus long-term growth forecasts suggest that the market applies these forecasts to an average horizon of at least 6 years, and as many as 10 years.
Author: Stefan Nagel
Publisher: Princeton University Press
ISBN: 0691218706
Category : Business & Economics
Languages : en
Pages : 156
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Book Description
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
Author: Yasuyuki Fuchita
Publisher: Brookings Institution Press
ISBN: 0815729820
Category : Business & Economics
Languages : en
Pages : 216
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Book Description
A Brookings Institution Press and Nomura Institute of Capital Markets Research publication Developed country capital markets have devised a set of institutions and actors to help provide investors with timely and accurate information they need to make informed investment decisions. These actors have become known as "financial gatekeepers" and include auditors, financial analysts, and credit rating agencies. Corporate financial reporting scandals in the United States and elsewhere in recent years, however, have called into question the sufficiency of the legal framework governing these gatekeepers. Policymakers have since responded by imposing a series of new obligations, restrictions, and punishments—all with the purpose of strengthening investor confidence in these important actors. Financial Gatekeepers provides an in-depth look at these new frameworks, especially in the United States and Japan. How have they worked? Are further refinements appropriate? These are among the questions addressed in this timely and important volume. Contributors include Leslie Boni (University of New Mexico), Barry Bosworth (Brookings Institution), Tomoo Inoue (Seikei University), Zoe-Vonna Palmrose (University of Southern California), Frank Partnoy (University of San Diego School of Law), George Perry (Brookings Institution), Justin Pettit (UBS), Paul Stevens (Investment Company Institute), Peter Wallison (American Enterprise Institute).
Author: John Guerard
Publisher: JAI Press(NY)
ISBN:
Category : Business & Economics
Languages : en
Pages : 264
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Book Description
Part of a series on contemporary studies in economic and financial analysis, this volume focuses on security analyst forecasting and asset allocation. Topics include market response to earning forecasts; and the effectiveness of security analysts' forecasts; among others.
Author: Werner Fransiscus Marcel De Bondt
Publisher: Cfa Inst
ISBN: 9780943205137
Category : Stock price forecasting
Languages : en
Pages : 36
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Book Description
