Analysts' utilization of historical earnings information

This study examines whether security analysts (in)efficiently utilize the information contained in past series of annual and quarterly earnings in producing earnings forecasts. To do so, it investigates whether equal-weighted combinations of security analysts' forecasts with forecasts from statistical models based on historical earnings are superior, both in terms of being a better surrogate for the market's expectations of earnings and of accuracy, to forecasts from either one of these two sources. The empirical findings indicate that, although analysts' forecasts are superior to forecasts from statistical models, performance can be improved—both in terms of accuracy and also of being a better surrogate for market earnings expectations—by combining analysts' forecasts with forecasts from statistical models based on past quarterly earnings. Improvements in proxying for market earnings expectations were obtained even when analysts' forecasts made in June of the forecast year were used in the combinations. An implication of these findings is that investors can improve their investment decisions by using an average of the mean analysts' forecasts and the forecast produced by a time-series model of quarterly earnings in their investment decisions.     

A note on forecasting the prices of gold and silver: Asymmetric loss and forecast rationality

Given that the prices of gold and silver have witnessed large and substantial swings in recent years, policymakers and investors need readily available and reliable forecasts of the prices of these two precious metals. Survey data of forecasts of the prices of gold and silver provide a particularly rich data environment for policymakers and investors to study developments in the markets for gold and silver. Our research helps to develop a deeper understanding of the properties of survey data of the prices of gold and silver. We study the shape of forecasters’ loss function and the rationality of their forecasts. Assuming an asymmetric loss function weakens evidence against forecast rationality, but results depend on the empirical model being studied.     

Combining probability forecasts

We consider different methods for combining probability forecasts. In empirical exercises, the data generating process of the forecasts and the event being forecast is not known, and therefore the optimal form of combination will also be unknown. We consider the properties of various combination schemes for a number of plausible data generating processes, and indicate which types of combinations are likely to be useful. We also show that whether forecast encompassing is found to hold between two rival sets of forecasts or not may depend on the type of combination adopted. The relative performances of the different combination methods are illustrated, with an application to predicting recession probabilities using leading indicators.     

A simple combination of univariate models

This paper describes the approach that we implemented for producing the point forecasts and prediction intervals for our M4-competition submission. The proposed simple combination of univariate models (SCUM) is a median combination of the point forecasts and prediction intervals of four models, namely exponential smoothing, complex exponential smoothing, automatic autoregressive integrated moving average and dynamic optimised theta. Our submission performed very well in the M4-competition, being ranked 6^th for the point forecasts (with a small difference compared to the 2^nd submission) and prediction intervals and 2^nd and 3^rd for the point forecasts of the weekly and quarterly data respectively.     

Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts

If ‘learning by doing’ is important for macro-forecasting, newcomers might be different from regular, established participants. Stayers may also differ from the soon-to-leave. We test these conjectures for macro-forecasters’ point predictions of output growth and inflation, and for their histogram forecasts. Histogram forecasts of inflation by both joiners and leavers are found to be less accurate, especially if we suppose that joiners take time to learn. For GDP growth, there is no evidence of differences between the groups in terms of histogram forecast accuracy, although GDP point forecasts by leavers are less accurate. These findings are predicated on forecasters being homogeneous within groups. Allowing for individual fixed effects suggests fewer differences, including leavers’ inflation histogram forecasts being no less accurate.     

Combining forecasts: An application to elections

We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six US presidential elections from 1992 to 2012, we report the reductions in error obtained by averaging forecasts within and across four election forecasting methods: poll projections, expert judgment, quantitative models, and the Iowa Electronic Markets. Across the six elections, the resulting combined forecasts were more accurate than any individual component method, on average. The gains in accuracy from combining increased with the numbers of forecasts used, especially when these forecasts were based on different methods and different data, and in situations involving high levels of uncertainty. Such combining yielded error reductions of between 16% and 59%, compared to the average errors of the individual forecasts. This improvement is substantially greater than the 12% reduction in error that had been reported previously for combining forecasts.     

Forecasting small area populations with long short-term memory networks

Local and state governments depend on small area population forecasts to make important decisions concerning the development of local infrastructure and services. Despite their importance, current methods often produce highly inaccurate forecasts. Recent years have witnessed promising developments in time series forecasting using Machine Learning across a wide range of social and economic variables. However, limited work has been undertaken to investigate the potential application of Machine Learning methods in demography, particularly for small area population forecasting. In this paper we describe the development of two Long-Short Term Memory network architectures for small area populations. We employ the Keras Tuner to select layer unit numbers, vary the window width of input data, and apply a double training and validation regime which supports work with short time series and prioritises later sequence values for forecasts. These methods are transferable and can be applied to other data sets. Retrospective small area population forecasts for Australia were created for the periods 2006–16 and 2011–16. Model performance was evaluated against actual data and two benchmark methods (LIN/EXP and CSP-VSG). We also evaluated the impact of constraining small area population forecasts to an independent national forecast. Forecast accuracy was influenced by jump-off year, constraining, area size, and remoteness. The LIN/EXP model was the best performing method for the 2011-based forecasts whilst deep learning methods performed best for the 2006-based forecasts, including significant improvements in the accuracy of 10 year forecasts. However, benchmark methods were consistently more accurate for more remote areas and for those with populations <5000.     

Do IMF forecasts respect Okun’s law? Evidence for advanced and developing economies

This paper provides an assessment of the IMF’s unemployment forecasts, which have not received much scrutiny to date. The focus is on the internal consistency of the IMF’s growth and unemployment forecasts, and specifically on seeing whether the relationship between the two is consistent with the relationship in the data, i.e., with Okun’s Law. We find that the average performance is good, in the sense that the relationship between growth and unemployment forecasts is fairly comparable to that which prevails in the data: on average, the Okun coefficient in the forecasts mirrors the Okun coefficient in the data. Nevertheless, there is room for improvement, particularly in the year-ahead forecasts and for the group of middle-income countries. We show that a linear combination of Okun-based unemployment forecasts and WEO unemployment forecasts can deliver significant gains in forecast accuracy for developing economies.     

The use of multiple scenarios in sales forecasting: An empirical test

Nearly 500 annual sales forecasts were generated from the responses of 82 subjects who were presented with either a time-series plot of historical sales data by itself or with the same plus three scenarios, and were then asked to make forecasts. Sales forecasts were made in either a stable or an unstable environment. The findings did not support the claims made by scenario advocates. Scenarios did not make unexpected outcomes less surprising. Instead, scenarios were found to increase confidence in a favored forecast. Furthermore, no support was found for the contention that scenarios improved upon ‘eyeball’ extrapolations or made judgmental sales forecasts more accurate than quantitative extrapolations. Scenarios were found to be tainted by many of the same biases previously identified by cognitive psychologists.     

Forecasting Earnings Using Geographical Segment Data: Some UK Evidence

This study examines the question of whether or not the geographical segment data disclosed by UK companies can be used to generate forecasts of earnings that outperform forecasts based upon past consolidated data. One year ahead forecasts of attributable earnings or net income before extraordinary items are generated for both geographical sales data combined with a consolidated attributable earnings to sales margin and segmental earnings data. The forecasts are based upon forecasts of changes in the GNP of individual countries, both with and without the addition of forecasted inflation rates. It is found that models based upon both geographical segment sales and segment earnings outperform the random walk and random walk plus drift consolidated models for the years 1981 to 1983. The difference in the sizes of the errors generated by the segment data based models and the consolidated data based models are significant in the majority of cases especially when the errors are truncated at 100%. However, there is no additional advantage in terms of forecast accuracy in using segment earnings data rather than segment sales data.     

Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support

How effective are different approaches for the provision of forecasting support? Forecasts may be either unaided or made with the help of statistical forecasts. In practice, the latter are often crude forecasts that do not take sporadic perturbations into account. Most research considers forecasts based on series that have been cleansed of perturbation effects. This paper considers an experiment in which people made forecasts from time series that were disturbed by promotions. In all conditions, under-forecasting occurred during promotional periods and over-forecasting during normal ones. The relative sizes of these effects depended on the proportions of periods in the data series that contained promotions. The statistical forecasts improved the forecasting accuracy, not because they reduced these biases, but because they decreased the random error (scatter). The performance improvement did not depend on whether the forecasts were based on cleansed series. Thus, the effort invested in producing cleansed time series from which to forecast may not be warranted: companies may benefit from giving their forecasters even crude statistical forecasts. In a second experiment, forecasters received optimal statistical forecasts that took the effects of promotions into account fully. This increased the accuracy because the biases were almost eliminated and the random error was reduced by 20%. Thus, the additional effort required to produce forecasts that take promotional effects into account is worthwhile.     

数据挖掘应用性的研究

随着信息技术的高速发展,人们积累的数据量急剧增长,如何从海量的数据中提取有用的知识成为当务之急。数据挖掘就是为顺应这种需要应运而生发展起来的数据处理技术。数据挖掘(Data Mining)是从存放在数据库、数据仓库或其他信息库中的大量数据中挖掘有趣知识的过程。数据挖掘算法和可视化工具适用于挖掘数据中的重要模式,并且提供有价值的预测,它是当代人工智能、统计学等技术在数据库领域中的应用。     

Co-integration constraint and forecasting: An empirical examination

Does co-integration help long-term forecasts? In this paper, we use simulation, real data sets, and multi-step-ahead post-sample forecasts to study this question. Based on the square root of the trace of forecasting error-covariance matrix, we found that for simulated data imposing the ‘correct’ unit-root constraints implied by co-integration does improve the accuracy of forecasts. For real data sets, the answer is mixed. Imposing unit-root constraints suggested by co-integration tests produces better forecasts for some cases, but fares poorly for others. We give some explanations for the poor performance of co-integration in long-term forecasting and discuss the practical implications of the study. Finally, an adaptive forecasting procedure is found to perform well in one- to ten-step-ahead forecasts.     

Flexibility and specialization in question; birth,growth and death rates of Cambridge new technology-based firms 1988–92

Insight into the investment behaviour of firms is central in understanding economic dynamics. A critical question, however, is whether firms provide sufficiently reliable data to enable them to make plausible forecasts at the meso (regional or sectoral) level. This paper analyses Dutch investment forecasts at different levels of aggregation. The central research question is whether entrepreneurs, individually or as a group, make systematic errors in their investment forecasts. A statistical test reveals that investment forecasts are not biased at the aggregated (regional and sectoral) level. At the micro level, however, there is a significant bias. Hence, using aggregated (regional and sectoral) data to test the lack of bias (unbiasedness) of forecasts may lead to the wrong conclusions. Moreover, aggregated investment forecasts may then be an inappropriate source for policy recommendations, despite their seemingly high reliability. This finding may in principle be valid for many European countries, since data collection on investment is organized in similar ways throughout Europe.     

Dimensionality reduction in forecasting with temporal hierarchies

Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. It can be challenging to estimate the full cross-covariance matrix for a temporal hierarchy, which can easily be of very large dimension, yet it is difficult to know a priori which part of the error structure is most important. To address these issues, we propose to use eigendecomposition for dimensionality reduction when reconciling forecasts to extract as much information as possible from the error structure given the data available. We evaluate the proposed estimator in a simulation study and demonstrate its usefulness through applications to short-term electricity load and financial volatility forecasting. We find that accuracy can be improved uniformly across all aggregation levels, as the estimator achieves state-of-the-art accuracy while being applicable to hierarchies of all sizes.     

Long-range U.S. Manpower forecasts in retrospect: How accurate were we?

This paper compares and evaluates the accuracy of long-range occupational manpower forecasts made for 1980 in the early 1970s by the U.S. Bureau of Labor Statistics and by the author. The different assumptions and forecasting methodologies utilized are discussed, and the occupational forecasts are then compared to the actual 1980 employment data. The relative accuracy of the different sets of forecasts is assessed according to several different criteria, and the larger question of the usefulness of either set of forecasts is addressed. It is found that neither set of forecasts was clearly superior, that the accuracy of both sets of forecasts was generally poor, and that the projections for individual occupations were often so wide of the mark as to be of questionable usefulness for manpower planning and vocational guidance. The implications of these findings for manpower forecasting are discussed.The author is grateful to several referees for helpful comments on an earlier draft of this paper, but retains sole responsibility for the opinions expressed here and for any errors.     

An Exploratory Study of the Effect on Forecast Accuracy of Using Different Geographic Segment Data Sources

The specific research question this study investigates is whether or not model-based earnings forecasts utilizing COMPUSTAT's country specific geographic segment data produce more accurate earnings forecasts than model-based forecasts utilizing data from actual geographic segment footnotes. This study compares the accuracy of earnings forecasts from models using the geographic segment information in actual geographic segment footnotes to the accuracy of earnings forecasts from models using the geographic segment information in the COMPUSTAT business segment tape. Evidence collected in this study indicates that geographic segment data provided in the COMPUSTAT business segment data base appears to make it possible to more accurately predict sales than does the geographic segment data provided in the same companies' actual geographic segment footnote.     

On the selection of forecasting models

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and finite-sample properties of these methods in terms of their ability to mimimize the true out-of-sample PMSE, allowing for possible misspecification of the forecast models under consideration. We show that under suitable conditions the IC method will be consistent for the best approximating model among the candidate models. In contrast, under standard assumptions the SOOS method, whether based on recursive or rolling regressions, will select overparameterized models with positive probability, resulting in excessive finite-sample PMSEs.     

Combination of long term and short term forecasts, with application to tourism demand forecasting

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.     

Managerial evaluation of exogenous forecast sources

This paper concerns the managerial evaluation of forecast vendors—individuals or firms offering for sale future forecasts of random variables relevant to managerial decision making. Assuming the forecasts are exogenous in the sense they are generated by a methodology unknown or unproven to management, the paper uses a logistic regression model to present a statistical test for informativeness that allows for an interpretation of the vendor's abilities. The advantage of the approach is that it requires as input only knowledge of the unconditional probability distribution of the variable being forecast and a relatively small historical track record of the vendor's forecasting performance. No benchmark forecast is necessary and few assumptions are required about the statistical process that generates the forecasts. As an illustrative empirical application, the paper presents an evaluation of the informativeness of the published long-range price forecasts by a veteran analyst of the Iowa hog market.