预测中国宏观经济变量:专家与模型的组合预测

本文使用组合预测方法,探究以“朗润预测”为代表的专家预测以及计量模型对于中国宏观经济变量的预测效果,并研究对不同预测进行组合预测是否有助于改进预测效果。本文发现,对我国CPI和GDP的增长率,专家预测效果总体上优于模型预测。从原因看,一方面,专家在预测时已经考虑了计量模型的预测信息;另一方面,在经济出现“拐点”的时期,专家通过对实际经济环境和政策的把握,得出更准确的经济预测。组合预测有助于提升预测精度,对专家预测进行组合得到的预测效果优于大多数的专家预测,“模型—专家”组合预测的效果也优于所有的模型和大部分专家预测。     

Earnings forecast disclosure regulation and earnings management: evidence from Taiwan IPO firms

This study examines whether the Taiwanese regulation requiring disclosure of earnings forecasts in the IPOs resulted in disclosure of more optimistic earnings forecasts and whether the forecast error was reduced more by manipulating the reported earnings rather than revising the earnings forecasts to meet the forecast error threshold. The study is based on 759 forecasts issued by the Taiwanese IPO firms from 1994 to 2001, i.e. 8-year period after the regulation was modified to increase the forecast error threshold to 20%. The findings show that the disclosure regulation resulted in more optimistic forecasts than conservative forecasts, especially for firms expecting better performance in the forecast year compared to the previous year. Firms disclosing optimistic earnings forecasts engaged more in manipulation of reported earnings than revision of forecasts to meet the forecast error threshold. These findings thus suggest that the disclosure regulation resulted in earnings manipulation, which reduced the quality of reported earnings. We received valuable comments at the 2003 American Accounting Association and 2004 Euorpean Accounting Association annual meetings. We also thank the participants at the research seminars at Rutgers University, City University of Hong Kong, and Pace University, for their insightful comments. Picheng Lee especially thanks Pace University for 2003 summer research grant.     

超预期股本扩张与分析师每股盈余预测

卖方分析师的每股盈余预测在实证文献中被广泛使用。该指标同时依赖于分析师对目标公司未来净利润的预测和对目标公司未来股本数量的预测。因此,如果在分析师发布预测后,目标公司的股本数量发生超出分析师预期的扩张,则每股盈余预测将无法代表分析师对目标公司未来基本面的预测。本文构建了“调整后每股盈余预测”指标,该指标可剔除超预期股本扩张对每股盈余预测的影响,真实反映分析师对目标公司未来基本面的预测。本文应用该指标,在两个具体的实证研究场景中证明了,忽略超预期股本扩张的影响可能得出错误的实证结论。本文还进一步指出了三个忽略超预期股本扩张的影响可能导致错误实证结果的研究场景。     

超预期股本扩张与分析师每股盈余预测

卖方分析师的每股盈余预测在实证文献中被广泛使用。该指标同时依赖于分析师对目标公司未来净利润的预测和对目标公司未来股本数量的预测。因此,如果在分析师发布预测后,目标公司的股本数量发生超出分析师预期的扩张,则每股盈余预测将无法代表分析师对目标公司未来基本面的预测。本文构建了“调整后每股盈余预测”指标,该指标可剔除超预期股本扩张对每股盈余预测的影响,真实反映分析师对目标公司未来基本面的预测。本文应用该指标,在两个具体的实证研究场景中证明了,忽略超预期股本扩张的影响可能得出错误的实证结论。本文还进一步指出了三个忽略超预期股本扩张的影响可能导致错误实证结果的研究场景。     

Developing Confidence Intervals for Office Market Forecasts

This study focuses on the precision of models that forecast office construction and absorption. The article is novel because for the first time it applies Feldsteins (1971) technique for developing forecast standard errors in the presence of stochastic exogenous variables. The purpose of the article is not to find behavioral relationships but rather to evaluate forecasts. We find that in the case of many office markets, standard errors of long-term forecasts for absorption and completions are quite large, and therefore the forecasts themselves should not be used as a reliable basis for underwriting.     

The effect of management earnings forecast characteristics on cost of equity capital

This study uses a framework presented in Hirst, Koonce, and Venkataraman (2008) to assess how differences in management earnings forecast characteristics influence a firm's cost of equity capital. I find that less specific forecasts, pessimistic forecasts, and forecasts that predict a loss for the period are associated with higher cost of equity capital levels and more timely forecasts and forecasts with more information content are associated with lower cost of equity capital levels. Analysis interacting control variables and forecast antecedents with forecast characteristics indicates that the effects forecast characteristics have on cost of equity capital are either enhanced or moderated depending on firm beta, firm size, firm book-to-market ratios, analyst following, prior forecast bias, and earnings quality. The results highlight the importance of interacting key variables when interpreting the market effect of management earnings forecasts.     

Initial public offering prospectus forecast errors and mandatory explanations: evidence from New Zealand

This paper examines management forecast errors in initial public offering (IPO) prospectuses of New Zealand firms and subsequent management explanations for earnings forecast errors in recent years. New Zealand has several unique features and recent changes that are worthy of research, including the requirement for management to make forecasts; a requirement to explain differences between forecast and actual; and a recent change that provides the voluntary opportunity to obtain a negative assurance opinion on forecasts. Using hand-collected IPO data between 1998 and 2014, we find that firms that include a negative assurance opinion on the prospective financial information in the prospectus have more accurate earnings and cash flow forecasts. We investigate the subsequent explanations for forecast errors, which are mandated by a financial reporting standard. We show that management tends to attribute negative and large forecast errors to external causes rather than their own actions. Our findings have implications for regulators and investors in New Zealand and other settings.     

Are more able managers good future tellers? Learning from Japan

Baik et al. (2011) find that high-ability managers in the U.S. are more likely to issue accurate management earnings forecasts. Focusing on Japan, where management earnings forecasts are effectively mandated, we extend the literature by exploring (1) whether the relationship between managerial ability and forecast accuracy is unique to the U.S. disclosure system, where management forecasts are voluntary, and (2) how high-ability managers increase their forecast accuracy. We find that managerial ability is negatively associated with forecast errors based on initial forecasts, suggesting that high-ability managers are more likely to issue accurate forecasts at the beginning of the fiscal year. We then show that high-ability managers are less likely to revise their initial earnings forecasts and less likely to use earnings management to improve the accuracy of their earnings forecasts. Our findings show that, while high-ability managers are more likely to issue accurate initial management forecasts, low-ability managers are more likely to revise their forecasts and conduct earnings management to reduce their forecast errors.     

An examination of the effects of management earnings forecast form and explanations on financial analyst forecast revisions

Using a sample of 978 quarterly management earnings-per-share forecasts made during the period 1993 to 1999, we document that financial analyst revisions to management earnings forecasts are a function of management forecast form. More precise forecasts (measured three different ways) lead to greater revision of financial analyst consensus EPS forecasts for a given level of unexpected earnings as predicted by Kim and Verrecchia (1991) and Bayesian adjustment models. Also, consistent with our arguments, maximum forecasts are interpreted as bad news by analysts. Our results, while consistent with theory, are inconsistent with recent experimental studies which do not reject the null hypothesis of no effect of management earnings forecast form on the association between unexpected earnings and financial analyst forecast revisions. We also re-examine Baginski, Hassell, and Kimbrough's (2004) finding that attributions used to explain management forecasts affect the reaction to the forecast using analyst data. Consistent with their findings using stock prices, the attribution presence (especially external attributions) increases financial analyst revisions pursuant to management forecasts.     

On the rationality and efficiency of inflation forecasts: Evidence from advanced and emerging market economies

This paper provides a full characterization of inflation rate forecasts using the mean values from Consensus Economics for a sample of 78 advanced and emerging economies between 1989 and 2014. It also assesses the performance of inflation rate forecasts around business cycles’ turning points. As expected, that inflation forecasts start to mirror the actual data as the forecast horizon draws to a close, particularly in advanced economies. The mean forecast error is positive and larger than one point when we pool all countries, but this masks inter-group differences. Moreover, we find evidence for biasedness, inefficiency or information rigidities, with a clear tendency for “forecast smoothing”. Accounting for cross-country informational linkages is important: forecasters fail to adjust their inflation forecasts quick enough in response to domestic news and news from abroad. Finally, during recession episodes forecasts generally appear to be inefficient. The same holds true for recoveries.     

Volatility Forecasting in the Hang Seng Index using the GARCH Approach

The aim of this paper is to add to the literature on volatility forecasting using data from the Hong Kong stock market to determine if forecasts from GARCH based models can outperform simple historical averaging models. Overall, unlike previous studies we find that the GARCH models with non-Normal distributions show a robust volatility forecasting performance in comparison to the historical models. The results indicate that although not all models outperform simple historical averaging, the EGARCH based models, with non-normal conditional volatility, tend to produce more accurate out-of-sample forecasts using both standard measures of forecast accuracy and financial loss functions. In addition we test for asymmetric adjustment in the Hang Seng, finding strong evidence of asymmetries due to the domination of financial and property firms in this market.     

我国证券分析师预测代表市场预期的能力提高了吗?来自年报信息市场反应检验的证据

本文以集中发布了分析师行为规范的2005年为转折点,参考对比随机模型和管理层预测,在年报公布后的长窗口内检验分析师的盈利预测代表投资者盈利预期的能力是否有所提高。结果表明,在2005年之后,伴随着分析师预测准确度的提高,其代表市场预期的能力也有所提高,这从投资者认同的角度揭示了我国分析师行业的逐步规范和发展。本文的发现还表明,相对于管理层预测和随机模型的预测而言,分析师预测可能是更合适的市场预期替代变量。     

STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index

This paper studies whether it is possible to exploit the nonlinear behaviour of daily returns on the Spanish Ibex-35 stock index returns to improve forecasts over short and long horizons. In this sense, we examine the out-of-sample forecast performance of smooth transition autoregression (STAR) models and artificial neural networks (ANNs). We use one-step (obtained by using recursive and nonrecursive regressions) and multi-step-ahead forecasting methods. The forecasts are evaluated with statistical and economic criteria. In terms of statistical criteria, we compared the out-of-sample forecasts using goodness of forecast measures and various testing approaches. The results indicate that ANNs consistently surpass the random walk model and, although the evidence for this is weaker, provide better forecasts than the linear AR model and the STAR models for some forecast horizons and forecasting methods. In terms of the economic criteria, we assess the relative forecast performance in a simple trading strategy including the impact of transaction costs on trading strategy profits. The results indicate a better fit for ANN models, in terms of the mean net return and Sharpe risk-adjusted ratio, by using one-step-ahead forecasts. These results show there is a good chance of obtaining a more accurate fit and forecast of the daily stock index returns by using one-step-ahead predictors and nonlinear models, but that these are inherently complex and present a difficult economic interpretation.     

Optimistic bias in management forecasts by Japanese firms to avoid forecasting losses

Listed firms in Japan are effectively compelled to report management forecasts of sales, ordinary income, and net income along with actual earnings and sales each year. Prior studies report that Japanese managers tend to announce optimistic forecasts of earnings. We show that a large part (61.6%) of the overall optimistic bias in management earnings forecasts in Japan can be explained by loss forecast avoiding behavior of a small fraction (5.25%) of firms. Such behavior is caused in part by the view of the main bank and power group that the management forecast of earnings is the manager's earnings target. Our findings suggest that the Japanese stock market recognizes such loss forecast avoidance and accordingly discounts new information in management forecasts.     

Out‐of‐sample stock return predictability in emerging markets

This article builds on the widely debated issue of stock return predictability by applying a broad range of predictor variables and comprehensively considering the in‐sample and out‐of‐sample stock return predictability of ten advanced emerging markets. It compares forecasts from models with a single predictor variable, multiple predictor variables and a combination forecast approach. The results confirm the findings of Welch and Goyal (2008) for US data that only a limited number of individual predictor variables are able to deliver significant out‐of‐sample forecasts. However, a combination forecast approach provides statistically and economically significant out‐of‐sample forecast results.     

A re-examination of analysts’ superiority over time-series forecasts of annual earnings

We re-examine the widely held belief that analysts?? earnings per share (EPS) forecasts are superior to random walk (RW) time-series forecasts. We investigate whether analysts?? annual EPS forecasts are superior, and if so, under what conditions. Simple RW EPS forecasts are more accurate than analysts?? forecasts over longer horizons, for smaller or younger firms, and when analysts forecast negative or large changes in EPS. We also compare the accuracy of a third forecast of longer-term earnings based on a na?ve extrapolation of analysts?? 1-year-ahead forecasts. Surprisingly, this na?ve extrapolation provides the most accurate estimate of long-term (2- and 3-year-ahead) earnings. These findings recharacterize prior generalizations about the superiority of analysts?? forecasts and suggest that they are incomplete, misleading, or both. Moreover, in certain settings, researchers can rely on forecasts other than these explicit forecasts.     

Forecasting the past: the case of US interest rate forecasts

This study evaluates 10-year US government bond yield forecasts and three-month US Treasury bill rate forecasts for the period between October 1989 and December 2004. In total, 136 forecast time series with approximately 13,800 forecast data were scrutinized, making this the most extensive analysis of interest rate forecasts to date. Not one of the forecast time series proved to be unbiased. In the majority of cases, information from the past was not efficiently integrated into the forecasts. The sign accuracy is significantly better than random walk forecasts in only a very few of the forecast time series. The modified Diebold–Mariano test for forecast encompassing reveals that the information content of most of the forecast time series is lower than that of the naïve forecasts, the simple ARIMA models, the implicit forward rates, or average interest rate expectations. The forecasting process is dominated by the present and past market situation.     

The sensitivity of the credit default swap market to financial analysts’ forecast revisions

We examine the impact of analysts’ earnings per share (EPS) and cash flow per share (CPS) forecast revisions on the market for credit default swaps. We find that while the issuance of both EPS and CPS forecast revisions are inversely associated with changes in credit default swap (CDS) spreads, cash flow forecast revisions have a larger effect. We demonstrate that the relationship between CPS forecast revisions and CDS spreads tends to be stronger in cases of financial distress. We provide evidence that cash flow forecasts dominate earnings forecasts in some situations and that participants in the CDS market discriminate between analysts' forecast revisions and recommendation changes.     

The Impact of Analysts' Forecast Errors and Forecast Revisions on Stock Prices

Abstract:  We present a comprehensive analysis of the association between stock returns, quarterly earnings forecast errors, and quarter-ahead and year-ahead earnings forecast revisions. We find that forecast errors and the two forecast revisions have significant effects on stock prices, indicating each conveys information content. Findings also show that the fourth quarter differs from other quarters—the relative importance of the forecast error (quarter-ahead forecast revision) is lower (higher). We also find a marked upward shift over time in the forecast error and forecast revision coefficients, consistent with the I/B/E/S database reflecting an improved quality of both earnings forecasts and actual earnings.     

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance.