The usefulness of oil price forecasts—Evidence from survey predictions

This paper evaluates survey forecasts for crude oil prices and discusses the implications for decision makers. A novel disaggregated data set incorporating individual forecasts for Brent and Western Texas Intermediate is used. We carry out tests for unbiasedness, sign accuracy, and forecast encompassing, followed by the computation of coefficients for topically oriented trend adjustments and the Theil's U measure. We also control for the forecast horizon finding heterogeneous results. Forecasts are more precise for shorter horizons, but less accurate than the naïve prediction. For longer horizons, topically oriented trend adjustments become more pronounced, but forecasters tend to outperform the naïve predictions.     

Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)

While combining forecasts is well-known to reduce error, the question of how to best combine forecasts remains. Prior research suggests that combining is most beneficial when relying on diverse forecasts that incorporate different information. Here, I provide evidence in support of this hypothesis by analyzing data from the PollyVote project, which has published combined forecasts of the popular vote in U.S. presidential elections since 2004. Prior to the 2020 election, the PollyVote revised its original method of combining forecasts by, first, restructuring individual forecasts based on their underlying information and, second, adding naïve forecasts as a new component method. On average across the last 100 days prior to the five elections from 2004 to 2020, the revised PollyVote reduced the error of the original specification by eight percent and, with a mean absolute error (MAE) of 0.8 percentage points, was more accurate than any of its component forecasts. The results suggest that, when deciding about which forecasts to include in the combination, forecasters should be more concerned about the component forecasts’ diversity than their historical accuracy.     

The European Market Abuse Directive: Has it Worked?

In this paper, we examine whether the Market Abuse Directive (MAD) has been effective in achieving its objectives of deterring the market manipulation activities, increasing the timeliness of information and decreasing the disclosure of inside information to select groups. Our sample consists of firms listed on Frankfurt Stock Exchange. We use stock prices and analysts' forecast‐based proxies to examine the impact and effectiveness of MAD. The analysis based on these two set of proxies provides evidence that the adoption of MAD has been effective. In particular, we find that after the implementation of MAD, on average, (1) the volatility of stock prices around earnings announcement declines, (2) stock prices remain closer to their post earnings announcement level during the period before earnings announcement, (3) the accuracy of analyst forecasts improves, (4) the dispersion of analyst forecasts decreases, and (5) the number of analysts following a company declines.     

Time Series Based Errors and Empirical Errors in Fertility Forecasts in the Nordic Countries*

We use ARCH time series models to derive model based prediction intervals for the Total Fertility Rate (TFR) in Norway, Sweden, Finland, and Denmark up to 2050. For the short term (5–10 yrs), expected TFR‐errors are compared with empirical forecast errors observed in historical population forecasts prepared by the statistical agencies in these countries since 1969. Medium‐term and long‐term (up to 50 years) errors are compared with error patterns based on so‐called naïve forecasts, i.e. forecasts that assume that recently observed TFR‐levels also apply for the future.     

Bait and ditch: Consumer naïveté and salesforce incentives

We analyze a model of price competition between a transparent retailer and a deceptive one in a market where a fraction of consumers is naïve. The transparent retailer is an independent shop managed by its owner. The deceptive retailer belongs to a chain and is operated by a manager. The two retailers sell an identical base product, but the deceptive one also offers an add‐on. Rational consumers never consider buying the add‐on while naïve ones can be “talked” into buying it. By offering the manager a contract that pushes him to never sell the base good without the add‐on, the chain can induce an equilibrium in which both retailers obtain more‐than‐competitive profits. The equilibrium features price dispersion and market segmentation, with the deceptive retailer targeting only naïve consumers whereas the transparent retailer serves only rational ones.     

Macroeconomic forecasting and structural change

The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time‐varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.     

Recession forecasting using Bayesian classification

We demonstrate the use of a Naïve Bayes model as a recession forecasting tool. The approach is closely connected with Markov-switching models and logistic regression, but also has important differences. In contrast to Markov-switching models, our Naïve Bayes model treats National Bureau of Economic Research business cycle turning points as data, rather than as hidden states to be inferred by the model. Although Naïve Bayes and logistic regression are asymptotically equivalent under certain distributional assumptions, the assumptions do not hold for business cycle data. As a result, Naïve Bayes has a larger asymptotic error rate, but converges to the error rate more quickly than logistic regression, resulting in more accurate recession forecasts with limited data. We show that Naïve Bayes outperforms competing models and the Survey of Professional Forecasters consistently for real-time recession forecasting up to 12 months in advance. These results hold under standard error measures, and also under a novel measure that varies the penalty on false signals, depending on when they occur within a cycle; for example, a false signal in the middle of an expansion is penalized more heavily than one that occurs close to a turning point.     

Feature weighted naïve Bayes algorithm for information retrieval of enterprise systems

Automated information retrieval is critical for enterprise information systems to acquire knowledge from the vast amount of data sets. One challenge in information retrieval is text classification. Current practices rely heavily on the classical naïve Bayes algorithm due to its simplicity and robustness. However, results from this algorithm are not always satisfactory. In this article, the limitations of the naïve Bayes algorithm are discussed, and it is found that the assumption on the independence of terms is the main reason for an unsatisfactory classification in many real-world applications. To overcome the limitations, the dependent factors are considered by integrating a term frequency–inverse document frequency (TF-IDF) weighting algorithm in the naïve Bayes classification. Moreover, the TF-IDF algorithm itself is improved so that both frequencies and distribution information are taken into consideration. To illustrate the effectiveness of the proposed method, two simulation experiments were conducted, and the comparisons with other classification methods have shown that the proposed method has outperformed other existing algorithms in terms of precision and index recall rate.     

Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models

In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information from a large number of predictors. So far, the dynamic factor model and its variants have been the most successful models for such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities for forecasting twenty important macroeconomic variables. These alternative models can handle hundreds of data series simultaneously, and extract useful information for forecasting. We also show, both analytically and empirically, that combing forecasts from LASSO-based models with those from dynamic factor models can reduce the mean square forecast error (MSFE) further. Our three main findings can be summarized as follows. First, for most of the variables under investigation, all of the LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at economically meaningful block levels, the new methods greatly enhance the interpretability of the models. Third, once forecasts from a LASSO-based approach are combined with those from a dynamic factor model by forecast combination techniques, the combined forecasts are significantly better than either dynamic factor model forecasts or the naïve random walk benchmark.     

Accounting and the Hope of Action

The paper discusses the role of hope in the construction of an accounting technology to realize a program, by looking at a process of choosing non-financial indicators in an effort to achieve healthier workplaces. By exploring the literature dealing with the concept of hope and by drawing on the debate on the relationship between accounting and action, we highlight the features of three hope-related concepts (hopelessness, naïve hope, and reflective hope). We also highlight how these concepts relate to different areas of uncertainty (validity, accuracy, and relevance) in the development of accounting technologies. Evidence collected through particiapant observation of a team involved in the construction of indicators offers empirical material to investigate the interplay between hopelessness, naïve hope, and reflective hope in relation to uncertainties concerning the link between accounting and action. Beyond analyzing how team members move from a naïve to a reflective hope in making the accounting–action link, the paper shows that among practitioners it is accepted that unintended consequences constitute the rule rather than the exception in the accounting–action link.     

An Evaluation of Financial Analysts' Earnings Forecasts for Hong Kong Firms

This study evaluates the accuracy and potential bias of analyst forecasts for Hong Kong firms published in the Estimate Directory and compares analyst forecasts to model forecasts. It also examines the association of forecast accuracy with various firm characteristics. The findings of the study show that on an overall basis analyst forecasts for Hong Kong firms are more accurate than model forecasts. Analyst forecasts for Earnings Per Share (EPS) are generally biased towards overstatement. The analysis of the association between forecast accuracy and company characteristics suggests that analyst forecasts for larger firms are comparatively more accurate than for smaller firms. As expected, the results also show that analyst forecasts with shorter time horizons are more accurate than forecasts with longer time horizons. The variability in firms' earnings, beta (market risk) or industry classification have no significant impact on the accuracy of analyst forecasts.     

Maximum likelihood estimation of a binomial proportion using one‐sample misclassified binary data

In this article, we construct two likelihood‐based confidence intervals (CIs) for a binomial proportion parameter using a double‐sampling scheme with misclassified binary data. We utilize an easy‐to‐implement closed‐form algorithm to obtain maximum likelihood estimators of the model parameters by maximizing the full‐likelihood function. The two CIs are a naïve Wald interval and a modified Wald interval. Using simulations, we assess and compare the coverage probabilities and average widths of our two CIs. Finally, we conclude that the modified Wald interval, unlike the naïve Wald interval, produces close‐to‐nominal CIs under various simulations and, thus, is preferred in practice. Utilizing the expressions derived, we also illustrate our two CIs for a binomial proportion parameter using real‐data example.     

MILTON FRIEDMAN ON THE INEFFECTIVENESS OF FISCAL POLICY

This article examines the evolution of the views of Milton Friedman on the (lack of) effectiveness of fiscal policy as compared with monetary policy. Though his views changed, it would not be accurate to say that he began as a naïve Keynesian. The empirical evidence bears out Friedman's later views.     

The influence of executive compensation on the dispersion of security analyst forecasts

This study examines the association between executive compensation and security analyst forecast dispersion in an agency setting. It is hypothesized that firms that compensate their managers with long-term performance plans and high percentages of managerial stock will be less likely to engage in manipulation of financial statements and their financial performance will be easier to predict, thus resulting in less disperse forecasts. The results provide evidence that firms that compensate their managers with long-term performance plans and higher levels of the company stock have less dispersion associated with their security analyst forecasts and greater dispersion of their long-term growth in earnings.     

Principles and algorithms for forecasting groups of time series: Locality and globality

Global methods that fit a single forecasting method to all time series in a set have recently shown surprising accuracy, even when forecasting large groups of heterogeneous time series. We provide the following contributions that help understand the potential and applicability of global methods and how they relate to traditional local methods that fit a separate forecasting method to each series:

• •Global and local methods can produce the same forecasts without any assumptions about similarity of the series in the set.
• •The complexity of local methods grows with the size of the set while it remains constant for global methods. This result supports the recent evidence and provides principles for the design of new algorithms.
• •In an extensive empirical study, we show that purposely naïve algorithms derived from these principles show outstanding accuracy. In particular, global linear models provide competitive accuracy with far fewer parameters than the simplest of local methods.

     

An enhanced dynamic hash TRIE algorithm for lexicon search

Information retrieval (IR) is essential to enterprise systems along with growing orders, customers and materials. In this article, an enhanced dynamic hash TRIE (eDH-TRIE) algorithm is proposed that can be used in a lexicon search in Chinese, Japanese and Korean (CJK) segmentation and in URL identification. In particular, the eDH-TRIE algorithm is suitable for Unicode retrieval. The Auto-Array algorithm and Hash-Array algorithm are proposed to handle the auxiliary memory allocation; the former changes its size on demand without redundant restructuring, and the latter replaces linked lists with arrays, saving the overhead of memory. Comparative experiments show that the Auto-Array algorithm and Hash-Array algorithm have better spatial performance; they can be used in a multitude of situations. The eDH-TRIE is evaluated for both speed and storage and compared with the naïve DH-TRIE algorithms. The experiments show that the eDH-TRIE algorithm performs better. These algorithms reduce memory overheads and speed up IR.     

Analysts' Forecasts in Asian-Pacific Markets: The Relationship among Macroeconomic Factors, Accounting Systems, Bias and Accuracy

In this study, we describe determinants of accuracy/bias of analysts' forecasts in 13 economies of the Asian‐Pacific region. Examination of the accuracy of analysts' earnings forecasts allows us to judge how accounting systems and macroeconomic distinctions in this region affect earnings predictability. As many investors rely on analysts' earnings forecasts instead of producing their own, the growth of international investment means forecasts in non‐US markets will become increasingly important to investors worldwide. Using a sample of firms with data available on Global Vantage and I/B/E/S International, we find that the analysts on average have a pessimistic bias in Asian‐Pacific markets. We examine whether macroeconomic factors explain part of the difference in the size of analyst forecast errors, using the global competitiveness rankings of the World Economic Forum (WEF). We expect that those nations which are more open to foreign trade and investment and are ranked more highly by the WEF in its Global Competitiveness Index will also have more accurate analyst forecasts, as increased global competitiveness demands greater integration into the world economy, and such integration should lead to more transparent financial statements and more accurate earnings forecasts. Our findings are consistent with this prediction. We also find that countries with low book‐tax conformity have more accurate earnings forecasts.     

How to Measure Analyst Forecast Effort

Abstract

We introduce a new way to measure the forecast effort that analysts devote to their earnings forecasts by measuring the analyst's general effort for all covered firms. While the commonly applied effort measure is based on analyst behaviour for one firm, our measure considers analyst behaviour for all covered firms. Our general effort measure captures additional information about analyst effort and thus can identify accurate forecasts. We emphasise the importance of investigating analyst behaviour in a larger context and argue that analysts who generally devote substantial forecast effort are also likely to devote substantial effort to a specific firm, even if this effort might not be captured by a firm-specific measure. Empirical results reveal that analysts who devote higher general forecast effort issue more accurate forecasts. Additional investigations show that analysts' career prospects improve with higher general forecast effort. Our measure improves on existing methods as it has higher explanatory power regarding differences in forecast accuracy than the commonly applied effort measure. Additionally, it can address research questions that cannot be examined with a firm-specific measure. It provides a simple but comprehensive way to identify accurate analysts.     

Environmental proactivism and firm performance: evidence from security analyst earnings forecasts

There is an ongoing debate over the impact of corporate pro-environment actions and strategies (reflected, for example, in pollution prevention and emission reductions, product re-design, materials stewardship) on corporate financial performance in US corporations today. A review of the existing literature in this area yields no consistent pattern of relationships between corporate environmental proactivism and financial performance when historical corporate accounting performance and stock market measures of performance are used. We revisit this relationship using a novel measure of firm performance: security analyst earnings forecasts. Specifically, we demonstrate a significant, negative relationship between environmental proactivism (using Toxic Release Inventory data) and industry analyst 1- and 5-year earnings-per-share performance forecasts for a sample of 523 US firms in 1992. We discuss the implications of these findings and provide suggestions for future research. © 1997 John Wiley & Sons, Ltd and ERP Environment.     

Earnings forecasts in enterprise information systems environment

With the globalisation and integration of world financial markets, the application of enterprise information systems has become more and more popular in the financial service sector. This paper examines the analyst's decision regarding allocation of effort to the precision and timeliness of earnings forecasts with financial decision support systems. Once an analyst decides to follow a firm, the analyst must choose the level of effort to devote to generating outputs, such as earnings forecasts. This paper provides insights into this by examining three issues concerning the forecasts of the individual analyst. First, forecast accuracy and frequency are modelled simultaneously. Then, whether these two effort allocation choices are complements or substitutes is tested. Finally, how competition affects these two characteristics is examined. The accuracy and frequency are examined with simultaneous equations. Results from this analysis suggest that analysts allocate effort among these two forecast criteria in a complementary way. Finally, empirical results reveal a positive association between competition and forecast frequency by the individual analyst. However, a significant association between competition and the individual analyst's forecast accuracy is not found.