Consensus Forecasts Research Articles

Spatial Aligned Mean: A Method to Improve Consensus Forecasts of Precipitation from Convection-Allowing Model Ensembles

Abstract Ensembles of convection-allowing model (CAM) forecasts are increasingly being used in operational numerical weather forecasting. Several approaches have been devised to find consensus among ensemble forecast fields, including the arithmetic ensemble mean and, more recently, the patchwise localized probability-matched (LPM) mean. However, differences in spatial distribution and intensity of precipitation features among ensemble members make it difficult to construct an ensemble mean product that characterizes the consensus while preserving precipitation structures forecasted by the individual ensemble members. To overcome this problem, this study aims to develop and test a method for improving ensemble consensus precipitation forecasts by directly considering the spatial offsets among ensemble members. This study uses a multiscale spatial alignment technique to align the precipitation features of each ensemble member to a common location, and the spatial aligned mean (SAM) is obtained by averaging the realigned members. It is shown that implementing SAM and subsequently applying the LPM technique to the average of all aligned members (SAM–LPM) can significantly improve the warm season precipitation forecast scores using common metrics such as equitable threat score (ETS). Also, improvement in the structure of features of heavy rainfall is shown from summer 2023 flash-flooding cases. Thus, SAM and SAM–LPM can be excellent candidate methods for calculating an ensemble consensus and providing ensemble consensus guidance to forecasters. Significance Statement High-impact rainfall events, such as flash floods, result in many billion-dollar loss events in the United States each year. This study seeks to improve the prediction of such events when using guidance from convection-allowing model (CAM) ensemble forecasts, such as the U.S. operational High-Resolution Ensemble Forecast (HREF) and the nascent Rapid Refresh Forecast System (RRFS). The proposed method, the spatial aligned mean (SAM), directly addresses the common issue of disparity in the predicted location of convective systems among ensemble members that confounds traditional ensemble consensus methods. In this study, it is found that SAM improves ensemble consensus guidance for high-impact rainfall events in both the HREF and the Center for Analysis and Prediction of Storms (CAPS) Finite-Volume Cubed-Sphere (FV3)-limited area model (LAM) CAM ensemble forecast system, a proxy for the future RRFS.

Основные особенности климатических условий зимнего сезона 2023/2024 гг. по данным мониторинга и прогнозов

The main results of the comprehensive analysis of the Northern Hemisphere large-scale atmospheric circulation features are presented for the 2023/2024 winter season. Skill scores of the consensus forecast for the 2023/2024 Northern Eurasia winter season are discussed in the context of reproducing the temperature and humidity regime. The qualitative analysis of the multimodel forecast from WMO’s Lead Center and the consensus forecast issued by NEACOF for seasonal anomalies in air temperature and precipitation for the winter of 2023/2024 resulted in the conclusion of the superior accuracy of the consensus forecast. This forecast was based on the data of three Russian models SL-AV, MGO, and INM with equal weighting coefficients.

ORTHODOX-INNOVATIVE EXPOSITION OF FINANCIAL MECHANISMS OF THE POST-WAR RECONSTRUCTION OF THE ECONOMY OF UKRAINE

The article is devoted to the study of modern financial mechanisms, which in the medium term can be effectively applied to rebuild the economy of Ukraine after the end of the war. The authors emphasize that the orthodox-innovative exposition of financial mechanisms represents a flexible and adaptive approach capable of effectively responding to current challenges, threats and needs of the country in the process of its reconstruction.The main focus of the article is on the study of such aspects as: outlines of the main challenges faced by the Ukrainian economy in the conditions of the war period, including the destruction of infrastructure, loss of production capacity and a decrease in the standard of living of the population; disclosure of traditional financial approaches, such as international loans and grants, that can be applied to the reconstruction of the economy; identification of modern innovative financial instruments, including “green” bonds, blockchain, crowdfunding and other financial instruments in order to provide additional sources of financing and increase trust on the part of international partners and donors; determining the role of international financial organizations, institutions and donors in the process of reconstruction of our state. An analysis of the assessment of the damage caused in Ukraine by the russian federation was carried out, which is based on the reports of the project “russia will pay” KSE Institute with the support of international partners and organizations and the report on the updated joint assessment of damages and needs that arose as a result of the invasion of the russian federation in Ukraine (Rapid Damage and Needs Assessment, RDNA3, developed by the Government together with the World Bank Group, the UN and the European Commission. The authors carried out a SWOT analysis, which made it possible to assess the strengths and weaknesses, opportunities and threats (risks) associated with the integration of orthodox and innovative financial mechanisms in the recovery process economy of Ukraine after the war. The article focuses on the Matrix of reforms – a huge plan of changes that systematizes and integrates reforms, includes officially approved recommendations and conditions under macro-financial support programs, in particular: “structural beacons” of the IMF, Plan of Ukraine under the Ukraine Facility program, documents of the European Commission, conditions of the World Bank (Development Policy Loan, DPL). Also, within the framework of the study, an analysis of the consensus forecast of the Ministry of Economy of Ukraine “Ukraine: Scenarios of Restorative Growth” was carried out to provide and assess risks for 2024–2027.

Fundamental Analysis via Machine Learning

We examine the efficacy of machine learning in a central task of fundamental analysis: forecasting corporate earnings. We find that machine learning models not only generate significantly more accurate and informative out-of-sample forecasts than the state-of-the-art models in the literature but also perform better compared to analysts’ consensus forecasts. This superior performance appears attributable to the ability of machine learning to uncover new information through identifying economically important predictors and capturing nonlinear relationships. The new information uncovered by machine learning models is of considerable economic value to investors. It has significant predictive power with respect to future stock returns, with stocks in the most favorable new information quintile outperforming those in the least favorable quintile by approximately 34 to 77 bps per month on a risk-adjusted basis.

WoFS and the Wisdom of the Crowd: The Impact of the Warn-on-Forecast System on Hourly Forecasts during the 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiment

Abstract During the 2021 Spring Forecasting Experiment (SFE), the usefulness of the experimental Warn-on-Forecast System (WoFS) ensemble guidance was tested with the issuance of short-term probabilistic hazard forecasts. One group of participants used the WoFS guidance, while another group did not. Individual forecasts issued by two NWS participants in each group were evaluated alongside a consensus forecast from the remaining participants. Participant forecasts of tornadoes, hail, and wind at lead times of ∼2–3 h and valid at 2200–2300, 2300–0000, and 0000–0100 UTC were evaluated subjectively during the SFE by participants the day after issuance, and objectively after the SFE concluded. These forecasts exist between the watch and the warning time frame, where WoFS is anticipated to be particularly impactful. The hourly probabilistic forecasts were skillful according to objective metrics like the fractions skill score. While the tornado forecasts were more reliable than the other hazards, there was no clear indication of any one hazard scoring highest across all metrics. WoFS availability improved the hourly probabilistic forecasts as measured by the subjective ratings and several objective metrics, including increased POD and decreased FAR at high probability thresholds. Generally, expert forecasts performed better than consensus forecasts, though expert forecasts overforecasted. Finally, this work explored the appropriate construction of practically perfect fields used during subjective verification, which participants frequently found to be too small and precise. Using a Gaussian smoother with σ = 70 km is recommended to create hourly practically perfect fields in future experiments. Significance Statement This work explores the impact of cutting-edge numerical weather prediction ensemble guidance (the Warn-on-Forecast System) on severe thunderstorm hazard outlooks at watch-to-warning time scales, typically between 1 and 6 h of lead time. Real-time forecast products in this time frame are currently provided on an as-needed basis, and the transition to continuous probabilistic forecast products across scales requires targeted research. Results showed that hourly probabilistic participant forecasts were skillful subjectively and statistically, and that the experimental guidance improved the forecasts. These results are promising for the implementation and value of the Warn-on-Forecast System to provide improved hazard timing and location guidance within severe weather watches. Suggestions are made to aid future subjective evaluations of watch-to-warning-scale probabilistic forecasts.

Итоги 25-й сессии Северо- Евразийского климатического форума и стартового регионального семинара ЭСКАТО ООН по проекту: Расширение знаний и потенциала для повышения устойчивости к медленно наступающим стихийным бедствиям в Центральной Азии

The North Eurasian Climate Center held the 25th session of the North Eurasian Climate Outlook Forum (NEACOF-25) combined with the project inception regional workshop under the auspices of the UN Economic and Social Commission for Asia and the Pacific (ESCAP). In total, 143 participants from 12 countries were registered at the event, 60 of them took part in the forum in person. During NEACOF-25, a consensus forecast of air temperature and precipitation anomalies for the territory of Northern Eurasia for the winter of 2023/2024 was presented. The NEACOF-25 consensus forecast is in agreement with the outlook of the large-scale atmospheric circulation from other world forecasting centers. Based on the consensus forecast and using the ESCAP methodology, an experimental outlook of the impact on agriculture, water resources and emergency management was given for the first time. During the program, work was also done to identify and harmonize national climate risk profiles in the countries of Central Asia. Keywords: North Eurasian Climate Outlook Forum, ESCAP, climate model, climate risks, consensus outlook, air temperature and precipitation seasonal anomalies

Итоги 25-й сессии Северо- Евразийского климатического форума и стартового регионального семинара ЭСКАТО ООН по проекту: Расширение знаний и потенциала для повышения устойчивости к медленно наступающим стихийным бедствиям в Центральной Азии

The North Eurasian Climate Center held the 25th session of the North Eurasian Climate Outlook Forum (NEACOF-25) combined with the project inception regional workshop under the auspices of the UN Economic and Social Commission for Asia and the Pacific (ESCAP). In total, 143 participants from 12 countries were registered at the event, 60 of them took part in the forum in person. During NEACOF-25, a consensus forecast of air temperature and precipitation anomalies for the territory of Northern Eurasia for the winter of 2023/2024 was presented. The NEACOF-25 consensus forecast is in agreement with the outlook of the large-scale atmospheric circulation from other world forecasting centers. Based on the consensus forecast and using the ESCAP methodology, an experimental outlook of the impact on agriculture, water resources and emergency management was given for the first time. During the program, work was also done to identify and harmonize national climate risk profiles in the countries of Central Asia. Keywords: North Eurasian Climate Outlook Forum, ESCAP, climate model, climate risks, consensus outlook, air temperature and precipitation seasonal anomalies

Market risk in Project Finance: Importance for Investors and Reliability of Forecasts

Relevance. Any ongoing business as well as new investment projects under development are associated with many risks. One of the most obvious and significant risks is the market risk i.e., in the context of the article, the risk of failure to achieve prices, volumes and, in general, the proceeds from sales, compared to those planned in the business plan and financial model of the project. In modern academic and business literature, approaches to the analysis of the market as a whole and marketing issues are discussed in sufficient level of details, however, the problems of examining these aspects in project finance and while investing in new projects in general are not considered.The purpose is Consider approaches to understanding significance of the market risk and measures to deal with the risk in the process of review of an investment project.Objectives. To discuss the possible components of the risk, provide an example of assessing its materiality for the project investment attractiveness ratios, approaches to analysis of the projections.Methodology. Methods of modelling, analysis and synthesis, induction and deduction, critical analysis and comparison of sources as well as analysis of publicly available information are used.Results. The article discusses approaches to the analysis of market risk in the implementation of an investment project, presents a simplified example of its importance based on a brief model of discounted cash flows, and identifies key assumptions and limitations. The consideration of possible mistakes in consensus forecast provides the greatest practical importance.Conclusions. The market risk is one of the key risks in the development of an investment project and project review in project finance. A set of measures is needed to account for and reduce of the risk. One of the most important issues is the critical analysis of available forecasts. In the next article, other issues will be considered in details i.e. approaches to working with an independent consultant, a number of specific requirements in the marketing consultant scope of work. Technical measures to minimise market risk in the financial model of the project will also be considered.

Corporate financial reporting and taxes: How important is prior performance?

Research Question: How do firms behave after significantly missing or exceeding analysts’ earnings estimates in terms of managing earnings and avoiding taxes? Motivation: Prior research provides strong evidence suggesting that managers are motivated to perform at or above analysts' expectations and steer earnings higher to prevent unpleasant earnings surprises. Prior studies have also documented that firms are likely to manage their earnings when they are close to meeting or missing analysts’ expectations. However, little is known about how firms behave after either substantially missing or beating analyst earnings estimates. Idea: This study provides evidence on firms’ earnings management and tax avoidance activities subsequent to the year in which firms substantially fail or succeed meeting analysts’ earnings consensus forecasts. Data: The data were collected from a sample of South Korean firms listed on the Korean Composite Stock Price Index for the years between 2013 and 2020. Tools: Multiple panel data regressions and robustness tests were conducted. Propensity score matching is also used to minimize endogeneity related problems. Findings: Firms are more likely to manage their earnings upward subsequent to significantly missing analysts’ expectations. However, their tendency to avoid taxes is lower. Contribution: Little has been explored on how firms significantly missing analysts’ expectations could behave in the subsequent period. The findings reported in this study have important implications for regulators, investors, and auditors. This research is also different from most prior related studies in terms of its setting.

Deterministic Rapid Intensity Forecast Guidance for the Joint Typhoon Warning Center’s Area of Responsibility

Abstract Intensity consensus forecasts can provide skillful overall guidance for intensity forecasting at the Joint Typhoon Warning Center as they provide among the lowest mean absolute errors; however, these forecasts are far less useful for periods of rapid intensification (RI) as guidance provided is generally low biased. One way to address this issue is to construct a consensus that also includes deterministic RI forecast guidance in order to increase intensification rates during RI. While this approach increases skill and eliminates some bias, consensus forecasts from this approach generally remain low biased during RI events. Another approach is to construct a consensus forecast using an equally-weighted average of deterministic RI forecasts. This yields a forecast that is generally among the top performing RI guidance, but suffers from false alarms and a high bias due to those false alarms. Neither approach described here is a prescription for forecast success, but both have qualities that merit consideration for operational centers tasked with the difficult task of RI prediction.

The Anchoring of US Inflation Expectations Since 2012

The stabilization, or anchoring, of inflation expectations at a target can help a central bank meet its goals. This paper develops a measure of expectations’ anchoring that combines the deviation of a consensus forecast from an inflation target with forecaster disagreement. We apply the measure to survey-based forecasts of PCE price inflation at medium- and longer-run horizons. Following the FOMC’s 2012 announcement of a 2 percent inflation target, the anchoring of both forecast series steadily improved through 2020:Q4. Recently, while longer-run expectations have remained well-anchored, the anchoring of medium-run expectations weakened significantly during the pandemic before strengthening in 2023:Q1.

Особенности атмосферной циркуляции летом 2022 г.

The main features of the atmospheric circulation in the summer of 2022 based on the Northern Hemisphere climate system monitoring are discussed. Estimates of the temperature and precipitation regime in Northern Eurasia based on observational data are given. The regions in which the anomalies of geopotential height, surface air pressure, and sea surface temperature are most/least successfully simulated by WMO multi-model forecasts and SL-AV model forecasts are identified. Good skill scores of consensus air temperature and precipitation forecasts for the summer of 2022 issued during NEACOF-22 is noted. For the entire territory of Northern Eurasia, the accuracy of forecasts was 80% for surface air temperature and 64% for precipitation. Keywords: large-scale atmospheric circulation, sea surface temperature, multi-model forecast, consensus forecast, air temperature, precipitation, skill scores

The role of stickiness, extrapolation and past consensus forecasts in macroeconomic expectations

We estimate a simple reduced-form model of expectation formation with three distinct deviations from full-information rational expectations that combines underreaction with overreaction to new information. In particular, forecasts are sticky, extrapolate the most recent news about the current period, and depend on the lagged consensus forecast about the period being forecast. We find that all three biases are present in the Survey of Professional Forecasters as well as in the Livingston Survey, and that their magnitudes depend on the forecasting horizon. We also stress the point that using the past consensus forecast to form expectations is a reasonable thing to do if a forecaster is not able to come up with full-information rational expectations all by herself. Finally, we show that forecasters that have more sticky expectations generally tend to rely less on the lagged consensus forecast but extrapolate news about the current period more.

Do Differences in Analyst Quality Matter for Investors Relying on Consensus Information?

This study investigates whether investors can reap economic benefits from analyzing differences in analyst quality. Although high-quality analysts’ average forecast is more accurate than the consensus forecast for firms with a large analyst following, the benefits of using high-quality analysts’ average forecasts are not economically significant. In contrast, the value of analyst quality differentiation exists in the second moment of forecasts. High-quality analysts’ forecast dispersion gives investors an advantage in dealing with uncertainty by predicting return volatility and providing opportunities for economically significant returns using option straddle and post-earnings announcement drift investment strategies. This paper was accepted by Suraj Srinivassan, accounting. Funding: A. Rubin and A. Vedrashko thank the financial support of the Social Sciences and Humanities Research Council of Canada (SSHRC). Supplemental Material: The data are available at https://doi.org/10.1287/mnsc.2023.4699 .

Do Analysts Learn from Each Other? Evidence from Analysts’ Location Diversity

Consistent with the idea that some of the noise in analysts' earnings forecasts originates in their geographic locations, we find that when analysts' locations are geographically more dispersed, the consensus forecast is more accurate, suggesting a diversification effect. Importantly, analysts' individual forecasts are also more accurate, implying that analysts incorporate idiosyncratic (private) information in their peer's forecasts when generating their own forecasts. Moreover, in line with efficient weighted average forecasting behavior, the weights assigned to peer forecasts vary with measures of the precision of the analyst's signal and those of the peers. Overall, we find strong evidence of analyst learning.

Модель контролю та оцінки ризику зовнішньоекономічної безпеки національних економік в умовах світових криз

The aim of the research work is to form a model of control and risk assessment of foreign economic security (FES) of national economies in the conditions of global crises. Its implementation will make it possible to take into account the likely future threats of FES in time and introduce effective mechanisms for their extinguishing or reduction in the present time to form an optimal trajectory for the development of foreign economic relations. The article substantiates that as the forecast values of FES indicators for the formation of a model of control and risk assessment of FES of national economies, it is advisable to include the following indicators: the level of openness of the economy; import export coverage ratio; growth rates of exports and imports; the share of imports in domestic consumption of the country; level of unemployment. As a method of forecasting the FES indicators, it is proposed to use a consensus forecast based on the principle of calculating the median values of indicators based on expert assessments by macroeconomic analysis and forecasting specialists. The practical implementation of the model of control and risk assessment of the FES of national economies was carried out on the example of the Ukrainian economy as one that is subject to the strong influence of crisis phenomena that cause a complex of threats to the FES. The projected level of FES risk for Ukraine according to a pessimistic and probable estimate is dangerously high, according to an optimistic one – high. This means the presence of a destabilizing negative impact of the crisis occurrences on the foreign economic activity of the national economy and a significant decrease in its functionality. To ensure the proper level of the FES of Ukraine in the context of crisis, it is necessary to: improve the regulatory framework by introducing a mandatory norm for calculating the FES index annually along with the forecast value of the risk level; introduction of mechanisms to increase Ukraine's role in the world trade system through the development of high-tech sectors of the economy, in particular through attracting international investment; search for new opportunities for foreign trade cooperation and development of the internal market.

How sensitive is the economy to large interest rate increases? Evidence from the taper tantrum

The “taper tantrum” of 2013 represents one of the largest monetary policy shocks since the 1980s. During this episode, long-term interest rates spiked 100 basis points—a move unintentionally induced by policymakers. However, this had no observable negative effect on the overall U.S. economy. Output, employment, and other important variables, all performed either in line with or better than consensus forecasts, often improving considerably relative to their earlier trends. We conclude that, from low levels, a 100 basis point increase in long-term interest rates is probably too small to affect overall economic activity and discuss the implications for monetary policy.

MEASURES TO SUPPORT FOR THE RUSSIAN ECONOMY AND POSSIBLE MACROECONOMIC CONSEQUENCES UNDER SANCTION PRESSURE

The relevance of the research has been substantiated. Russia has faced extraordinary economic circumstances. Russia is under unprecedented external pressure from a wide range of economic sanctions. In this connection, there is an objective need for a systematic analysis and forecasting of measures taken to support the Russian economy. The purpose of the research is to study measures to support the Russian economy and identify possible macroeconomic consequences of sanctions. In the research such methods of modern scientific knowledge as analysis, synthesis, induction, abstraction have been used. This article discusses the main directions of the anti-sanctions plan of the Russian Government, such as tax incentives, subsidies and the weakening of state control. The current price situation has been considered. The financial measures taken by the government to ensure the stability of the economy and support citizens in the condition of sanctions, as well as tax support measures have been considered. The possible results of the measures taken have been indicated. Some results of the forecast for the Russian economy made by S&P Global Market Intelligence have been presented. In addition, these data are compared with the first macroeconomic consensus forecast of the Bank of Russia since the expansion of Western sanctions against Russia and the extension of blocking to the reserves of the Central Bank itself. The author's presentation of the macroeconomic mechanism of sanctions has been augmented.

Sustainable Withdrawal Rates by Return Environment: A Time-Varying Bayesian Analysis

As investors depend more on their investment portfolios to generate income in retirement, financial advisors have developed rules of thumb to estimate a portfolio’s sustainable withdrawal rate (SWR). The most famous heuristic is Bengen’s “4% rule.” The strength of these rules is their ease of implementation. Among their limitations is their insensitivity to long-term variation in return environments. We borrow a statistical technique from the macroeconomics literature—a Bayesian vector autoregression with time-varying parameters—to model the impact of changing return dynamics on SWRs. We first illustrate how changes in stock-bond correlation, return and inflation volatilities, and returns have affected historical SWRs. Building on the consensus forecast, we then use these insights to identify 2.8% to 3.3% to be a relevant SWR for those retiring in today’s evironment characterized by low prospective returns and inflationary uncertainties.

Confirmation Bias in Analysts’ Response to Consensus Forecasts

This paper provides evidence of confirmation bias by sell-side analysts in their earnings forecasts. We show that analysts tend to put higher weight on public information when the current forecast consensus is more consistent with their previous forecasts. We further find that analysts with better past forecasting performance, longer firm-specific experience, or forecasting earlier, tend to be more subject to confirmation bias, consistent with some of the existing cognitive and social psychology theories. The results remain significant after controlling for analyst incentives. Finally, we distinguish evidence of confirmation bias from that of other important behavioral biases such as conservative bias.