Accuracy, Bias, and Dispersion in Analysts' Earnings Forecasts: The Case of Cross-Listed Foreign Firms

This paper examines differences in analysts' earnings forecast characteristics for foreign incorporated non-U.S. firms cross-listed in the U.S. stock markets relative to a control sample of purely domestic firms. Examining summary earnings forecasts over the calendar years 1984 through 1989, this paper provides evidence that there are statistically significant differences in bias and accuracy between domestic and cross-listed foreign firms. Consistent with prior research, we find a horizon effect in accuracy; i.e., accuracy improves as we get closer to the actual earnings announcement for both types of firms. However, the differences in accuracy between the cross-listed and domestic firms persist only in the earlier forecast horizons where analysts' forecasts are less accurate for foreign cross-listed firms compared with domestic firms. The evidence is also consistent with analysts' exhibiting less optimism with respect to cross-listed foreign firms compared with the domestic firms. Finally, the paper also documents that there is a greater consensus among analysts for foreign cross-listed firms than for domestic firms.     

The impact of regulation fair disclosure on earnings management and analyst forecast bias

Regulation Fair Disclosure (FD) has changed the information transfer process in the US securities market. We examine the impact that regulation FD has had on earnings management and analyst forecast bias. First, we examine the accuracy of financial analysts' earnings forecasts in the post-FD period. We find that analysts have become less accurate in forecasting earnings in the post-FD period and tend to overestimate earnings more relative to the pre-FD period. Second, we examine the level of earnings management after the passage of regulation FD and we find that the level of earnings management did not change after the implementation of regulation FD     

证券分析师跟踪:决定因素与经济后果

以会计信息的价值相关性模型为基础,探讨了分析师跟踪上市公司的决定因素及其对盈余信息价值相关性的影响。研究发现,分析师更倾向于选择盈余平滑程度较高且盈余操纵较少的公司。在分析师市场形成后,投资者将分析师的预测作为估计公司未来盈利的主要依据;对没有分析师跟踪的公司,投资者仍然倾向于运用以往的盈利作为公司未来盈利预测基础。     

How reliable are forecasts of GDP growth and inflation for countries with limited coverage?

This paper analyses the performance of GDP growth and inflation forecasts for 25 transition countries between 1994 and 2007, as provided by 13 international institutions, including multilateral, private and academic forecasters. The empirical results show that there is a positive correlation between the number of forecasters covering a given country and the forecast accuracy. Simple combined forecasts are shown to be unbiased and more accurate than most of the individual forecasters, although also inefficient. However, only a few institutions provide efficient and unbiased forecasts, with just one out of 13 forecasters providing both unbiased and efficient forecasts of both GDP growth and inflation in the observed period. The directional analysis shows a correct forecast of the change in the forecast indicator in over two thirds of cases. However, the eventual outcome is within the range of available forecasts in less than half of the cases, with more than 40% of outcomes for GDP growth above the highest forecast. Encouragingly, forecasts are shown to be improving over time and becoming more accurate with the increase in the number of forecasting institutions – forecast accuracy measured by mean absolute error improves by 0.3 percentage points for growth and by 0.2 percentage points for inflation for each additional institution providing forecasts.     

The Delphi method in forecasting financial markets— An experimental study

Experts were used as Delphi panellists and asked to present forecasts on financial market variables in a controlled experiment. We found that the respondents with the least accurate or least conventional views were particularly likely to modify their answers. Most of these modifications were in the right direction but too small, probably because of belief-perseverance bias. This paper also presents two post-survey adjustment methods for Delphi method based forecasts. First, we present a potential method to correct for the belief perseverance bias. The results seem promising. Secondly, we test a conditional forecasting process, which unexpectedly proves unsuccessful.     

Analysts' Earnings Forecasts and the Value Relevance of 20-F Reconciliations from non-U.S. to U.S. GAAP

This study extends existing research into the value relevance of reconciliations to U.S. GAAP by examining the role of analysts' earnings forecasts in explaining potential market reactions to the earnings reconciliation. One possible reason why the evidence on the value relevance of the earnings reconciliation is weak is that earnings are forecast by analysts. Their forecasts may pre-empt some of the information content of the earnings reconciliation disclosures. Our findings indicate that analysts' forecasts are value relevant and that they play a pre-emptive role for some firms. For others, however, the earnings reconciliation has information content that is not pre-empted by analysts' forecasts.     

Automatic feature identification and graphical support in rule-based forecasting: a comparison

We examined automatic feature identification and graphical support in rule-based expert systems for forecasting. The rule-based expert forecasting system (RBEFS) includes predefined rules to automatically identify features of a time series and selects the extrapolation method to be used. The system can also integrate managerial judgment using a graphical interface that allows a user to view alternate extrapolation methods two at a time. The use of the RBEFS led to a significant improvement in accuracy compared to equal-weight combinations of forecasts. Further improvement were achieved with the user interface. For 6-year ahead ex ante forecasts, the rule-based expert forecasting system has a median absolute percentage error (MdAPE) 15% less than that of equally weighted combined forecasts and a 33% improvement over the random walk. The user adjusted forecasts had a MdAPE 20% less than that of the expert system. The results of the system are also compared to those of an earlier rule-based expert system which required human judgments about some features of the time series data. The results of the comparison of the two rule-based expert systems showed no significant differences between them.     

Accuracy of German federal election forecasts, 2013 & 2017

The present study reviews the accuracy of four methods (polls, prediction markets, expert judgment, and quantitative models) for forecasting the two German federal elections in 2013 and 2017. On average across both elections, polls and prediction markets were most accurate, while experts and quantitative models were least accurate. However, the accuracy of individual forecasts did not correlate across elections. That is, the methods that were most accurate in 2013 did not perform particularly well in 2017. A combined forecast, calculated by averaging forecasts within and across methods, was more accurate than three of the four component forecasts. The results conform to prior research on US presidential elections in showing that combining is effective in generating accurate forecasts and avoiding large errors.     

Alternate manpower forecasts for the coming decade: Second guessing the U.S. department of labor

This paper presents alternate detailed forecasts of U.S. occupational requirements in the coming decade obtained from simulations conducted with the Center for Advanced Computation manpower forecasting model. These alternate manpower forecasts, developed on the basis of different mixes of federal social programs and budget priorities, are compared with the long-range employment projections developed by the U.S. Department of Labor, and the differences in methodology are analyzed. The sources of potential error in the Labor Department forecasts are identified, and a potentially more accurate picture of the shape of U.S. manpower requirements in the coming decade is presented.     

Differencing versus nondifferencing in factor-based forecasting

This paper studies performance of factor-based forecasts using differenced and nondifferenced data. Approximate variances of forecasting errors from the two forecasts are derived and compared. It is reported that the forecast using nondifferenced data tends to be more accurate than that using differenced data. This paper conducts simulations to compare root mean squared forecasting errors of the two competing forecasts. Simulation results indicate that forecasting using nondifferenced data performs better. The advantage of using nondifferenced data is more pronounced when a forecasting horizon is long and the number of factors is large. This paper applies the two competing forecasting methods to 68 I(1) monthly US macroeconomic variables across a range of forecasting horizons and sampling periods. We also provide detailed forecasting analysis on US inflation and industrial production. We find that forecasts using nondifferenced data tend to outperform those using differenced data.     

Evaluating the forecast accuracy and bias of alternative population projections for states

"A common perception among producers (and users) of population projections is that complex and/or sophisticated techniques produce more accurate forecasts than simple and/or naive techniques. In this paper we test the validity of that perception by evaluating the forecast accuracy and bias of eight commonly used projection techniques drawn from...four categories [trend extrapolation, ratio extrapolation, cohort-component, and structural]. Using data for [U.S.] state population projections from a number of different time periods, we find no evidence that complex and/or sophisticated techniques produce more accurate or less biased forecasts than simple, naive techniques."     

DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are crucial for having the right inventory available at the right time and in the right place. This paper proposes DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an autoregressive recurrent neural network model on a large number of related time series. We demonstrate how the application of deep learning techniques to forecasting can overcome many of the challenges that are faced by widely-used classical approaches to the problem. By means of extensive empirical evaluations on several real-world forecasting datasets, we show that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.     

Deposit insurance as private club: is Germany a model?

This paper describes and evaluates the deposit insurance scheme set up by private commercial banks in Germany in 1975. Unlike schemes in most countries, its funding and management is completely private. While other schemes rely on monitoring by depositors to decrease moral hazard, the German scheme relies on peer monitoring by its member banks. This paper evaluates the German deposit insurance scheme against the background of its unique characteristics, a very concentrated private banking market, a strong institutional environment and an antibankruptcy bias in Germany, and determines to which extent it serves as a model for other countries.     

Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation

This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000–2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral college vote outcomes and provides a detailed ex-post evaluation of the forecasts released in real time before the election. It is shown that all of these forecasts outperform autoregressive benchmarks. A pooled national model using One-Covariate-at-a-time-Multiple-Testing (OCMT) variable selection significantly outperformed all models in forecasting the U.S. mainland national vote share and electoral college outcomes (forecasting 236 electoral votes for the Republican party compared to 232 realized). This paper also shows that key determinants of voting outcomes at the county level include incumbency effects, unemployment, poverty, educational attainment, house price changes, and international competitiveness. The results are also supportive of myopic voting: economic fluctuations realized a few months before the election tend to be more powerful predictors of voting outcomes than their long-horizon analogs.     

Further Evidence on the Impact of SFAS 52 on Analysts' Earnings Forecasts

This study examines the impact of Statement of Financial Accounting Standards Number 52 (SFAS 52) on analysts' earnings forecasts. Value-Line forecasts of multinational firms' earnings were examined for the years 1979-1984. Forecast errors did not exhibit a significant linear trend. However, the results show a decline in signed forecast errors from the pre-SFAS 52 to the post-SFAS 52 period reflecting a change from an overstatement bias to relatively unbiased forecasts.
Overall, the results suggest that (1) overstatement errors were greater in the pre-SFAS 52 period than in the post SFAS 52 period, and (2) overstatement errors in the year that SFAS 52 was adopted were greater than surrounding years. In addition, a strong negative relation was found between 1981 signed forecast errors and both revisions in forecasts and the income effect of the accounting change.     

The Corrosive Effect of Offshore Financial Centers on Multinational Firms’ Disclosure Strategy

Abstract

This study investigates whether U.S. multinational firms with subsidiaries located in offshore financial centers (OFCs) (i.e. offshore firms) are more likely to be opaque in their voluntary disclosure relative to U.S. multinationals without such subsidiaries (non-offshore firms). We use management earnings forecasts to capture corporate voluntary disclosure. Consistent with the opportunism view, but inconsistent with the efficiency argument, our results (including robustness checks) show that offshore firms are less likely to issue earnings forecasts, disclose forecasts less frequently, exhibit a stronger tendency to withhold bad news forecasts, and release less precise forecasts than non-offshore firms. Moreover, of the three distinct dimensions of OFCs’ institutional environment, namely, low taxation, lax regulation, and secrecy policy, each plays a role in negatively shaping firms’ disclosure strategy. Thus, OFCs’ institutional features exacerbate the opacity that plagues firms seeking to avoid taxes via their OFC subsidiaries. Our results are consistent with the notion that, beyond the scope of taxes, multinational firms’ use of OFCs has a corrosive effect on market information dynamics. Hence, OFCs have a much wider impact on the U.S. economy as well as other major economies than just tax avoidance or evasion.     

Predicting recessions with a composite real-time dynamic probit model

In this paper we propose a composite indicator for real-time recession forecasting based on alternative dynamic probit models. For this purpose, we use a large set of monthly macroeconomic and financial leading indicators from the German and US economies. Alternative dynamic probit regressions are specified through automated general-to-specific and specific-to-general lag selection procedures on the basis of slightly different initial sets. The resulting recession probability forecasts are then combined in order to decrease the volatility of the forecast errors and increase their forecasting accuracy. This procedure features not only good in-sample forecast statistics, but also good out-of-sample performances, as is illustrated using a real-time evaluation exercise.     

THE ECONOMIC EFFECT OF HIGHWAY CONSTRUCTION: A COMPARATIVE ANALYSIS FOR THE U.K., GERMANY AND JAPAN

This paper attempts to estimate the marginal productivity of highway construction in the U.K., Germany and Japan during the 15 years following the first oil crisis. In addition, highway-improvement policy in Japan is evaluated using a simple macro-economic regression model. The following conclusions were obtained: in every country the marginal productivity of highway construction decreased over the period 1975-85, with the decrease most pronounced in Japan; subsequently, it increased slowly in the U.K. and Germany, with Japan leveling off though still having the highest value among the three countries; the U.K. maintained a high level of highway productivity largely as a result of a high degree of utilization. There is scope in Japan for attaining a higher level (approximately equal to that of the late 1970s) if the extent of highway utilization can be maintained by additional construction. This implies that future highway-construction policy should be of efficiency-seeking nature.     

Economic, organizational, and political influences on biases in forecasting state sales tax receipts

This paper investigates factors influencing fixed bias in forecasting state sales taxes revenues. By extending an existing model used to explain forecast accuracy to include a series of complex interactions related to the potential political and policy use of revenue forecasts, the paper extends our understanding of the forecasting process in government. Exploratory empirical analysis based on survey data is used to provide evidence that bias in forecasting results, at least in part, from political and policy manipulation. There is also evidence that institutional reforms associated with ‘good management’ practices affect forecast bias, but in complex ways depending upon the extent to which political competition exists within the state.     

Polls to probabilities: Comparing prediction markets and opinion polls

The forecasting of election outcomes is a hugely popular activity, and not without reason: the outcomes can have significant economic impacts, for example on stock prices. As such, it is economically important, as well as of academic interest, to determine the forecasting methods that have historically performed best. However, the forecasts are often incompatible, as some are in terms of vote shares while others are probabilistic outcome forecasts. This paper sets out an empirical method for transforming opinion poll vote shares into probabilistic forecasts, and then evaluates the performances of prediction markets and opinion polls. We make comparisons along two dimensions, bias and precision, and find that converted opinion polls perform well in terms of bias, while prediction markets are good for precision.