Relative Forecast Accuracy Research Articles

Sustainability and Dividends: Complements or Substitutes?

We examine the relationship between environmental, social, and governance commitment levels (ESG) and firm dividend-payer status. Given that larger and more profitable firms are positively associated with both payer status and ESG, it could be that ESG and dividends are complements. However, given that both dividends and ESG relate to firm spending decisions, it may be that the choice is “either/or”, and that ESG and dividends are substitutes. We document a positive relationship between ESG and dividend-payer status in U.S. firms over the period 1991–2016. In particular, we find that the proportion of dividend-payers is roughly 13% higher for firms with positive ESG compared to those with negative ESG. Including ESG in the models used to predict payer status provides, on average, a nearly 26% improvement in relative forecast accuracy. Our results are robust in regards to estimation techniques and the inclusion of variables known to be determinants of payer status. The results indicate that, on average, firms are not forced to sacrifice dividends in favor of ESG spending.

Forecasting crude oil price volatility out-of-sample using news-based geopolitical risk index: What forms of nonlinearity help improve forecast accuracy the most?

We evaluate the degree to which the geopolitical risk (GPR) index suggested in Caldara and Iacoviello (2018) helps improve the accuracy of one-month ahead out-of-sample point (density) forecasts of West Texas Intermediate (WTI) crude oil price log-realized volatility, and what forms of nonlinearity offer the largest degree of relative forecast accuracy gains. The central finding in this study is to demonstrate that contrary to what is perceived in the current literature, the form of nonlinearity that delivers the largest degree of point (density) forecast accuracy gains relative to the benchmark does not have to do with asymmetry (i.e. positive or negative GPR changes) or higher than average GPR increases, but rather GPR increases beyond the maximum GPR value over the last twelve months. In other words, GPR increases help deliver the highest degree of forecast accuracy gains relative to the benchmark if they exceed the maximum value in recent memory. However, GPR decreases below recent minimums do not help deliver relative accuracy gains.

Local versus foreign analysts' forecast accuracy: does herding matter?

The aim of this paper is to compare the information and resource endowments possessed by different analyst types, classified by both location and employment within the context of seven emerging Asian markets. Our results show that local analysts are more accurate than expatriate and global analysts when we consider all earnings forecasts. However, when we control for the segregated herding behaviour of analysts, we find that herding local forecasts are one of the least accurate compared to other herding forecasts. By contrast, bold local (that is, non‐herding local) forecasts are more accurate than all other bold forecasts. This suggests that the information endowment of bold local analysts is superior to the information and resource endowments of bold expatriate analysts. We show that the superior accuracy of bold local forecasts does not stem from business group affiliations, investment banking relationships, demand for local analysts’ services or the specialisation of analysts vis‐à‐vis countries or sectors. We consistently find that bold local analysts are better at assessing the earnings of the firm they forecast. Our results show that the prior documented advantage of local analysts in terms of forecasting accuracy is driven by the bold local analysts, with herding locals diluting this effect. To the best of our knowledge, this is the first study to explore the segregated herding behaviour of local, expatriate and global analysts, and its impact on relative forecast accuracy across analyst types.

New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?

We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, the nowcasting model forecasts at best are at least as good as the historical mean model, whereas during the recessionary periods, there are very substantial gains corresponding in the reduction in MSFE of about 90% relative to the benchmark model. We show how the asymmetry in the relative forecasting performance can be verified by the use of such recursive measures of relative forecast accuracy as Cumulated Sum of Squared Forecast Error Difference (CSSFED) and Recursive Relative Mean Squared Forecast Error (based on Rearranged observations) (R2MSFE(+R)). Ignoring these asymmetries results in a biased judgement of the relative forecasting performance of the competing models over a sample as a whole, as well as during economic expansions, when the forecasting accuracy of a more sophisticated model relative to naive benchmark models tends to be overstated. Hence, care needs to be exercised when ranking several models by their forecasting performance without taking into consideration various states of the economy.

Forecasting UK inflation bottom up

We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series combined with a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods and non-linear machine learning models. We find that exploiting CPI item series over the period 2011–19 yields strong improvements in forecasting UK inflation against an autoregressive benchmark, above and beyond the gains from macroeconomic predictors. Ridge regression and other shrinkage methods perform best across specifications that include item-level data, yielding gains in relative forecast accuracy of up to 70% at the one-year horizon. Our results suggests that the combination of a large and relevant information set combined with efficient penalisation is key for good forecasting performance for this problem. We also provide a model-agnostic approach to address the general problem of model interpretability in high-dimensional settings based on model Shapley values, partial re-aggregation and statistical testing. This allows us to identify CPI divisions that consistently drive aggregate inflation forecasts across models and specifications, as well as to assess model differences going beyond forecast accuracy.

Bayesian forecast combination in VAR-DSGE models

We evaluate the performance of individual and combination forecasts produced by Bayesian vector autoregressions (Bayesian VARs) with economic and/or non-economic information. In particular, we conduct an out-of-sample forecasting experiment using Bayesian VARs with statistical and/or DSGE prior(s) over two subsamples, representing the so-called “Great Inflation” and “Great Moderation” periods, respectively. Our main findings are summarized as follows. First, in most cases, the inclusion of prior information, either statistical or economic prior(s), improves the accuracy of point forecasts produced by the Bayesian VARs, relative to the ARMA(1,1) benchmark. Second, forecast combination helps to produce unbiased forecasts in most cases. Moreover, it significantly improves the forecast accuracy of the macro-econometric models, especially during the “Great Moderation” period. Third, the selection of the weighting scheme in forecast combination, simple averaging or Bayesian model averaging (BMA), does not affect the conclusions made above. Finally, partially in line with the recent literature, we find a positive relationship between the persistence of inflation and its relative forecast accuracy at the four-quarter horizon.

Range has it: decoding the information content of forecast ranges

Given that most management forecasts in recent years are issued in range form and that most firms are followed by multiple analysts issuing dispersed forecasts, the conventional measure of management forecast news based on the midpoint of management forecast and the consensus analyst forecast does not fully capture the information content conveyed by range forecasts. This paper seeks to partially uncover such information by investigating an unexamined dimension unique to range forecasts –– overlapping between management forecast ranges and the ranges formed by individual analyst forecasts, which occurs in nearly 84% of range forecasts. Controlling for the width of management forecasts and analyst forecast dispersion, we find that compared with “overlap” management forecasts, “non-overlap” forecasts have greater impact on analyst revisions and stock market reaction to per unit forecast news. The non-overlap forecasts also turn out to have high relative forecast accuracy over analyst consensus forecasts. Moreover, individual analysts whose prior forecasts are outside of management forecast ranges respond more strongly to “non-overlap” forecasts than to “overlap” forecasts, and this difference is significant only when management forecasts convey bad news. Overall, our findings suggest that range forecasts convey more information beyond just the midpoint and analysts’ consensus.

Forecasting House Prices in Italy

Forecasting house prices is a difficult task given the strong relationship between real estate markets, economic activity and financial stability, but it is an important one. This paper evaluates the out-of-sample forecasting performance of various models of house prices in a quasi-real time setting. Focusing on Italy, we consider two structural models (using simultaneous equations) and a Bayesian VAR and compute both conditional and unconditional forecasts. We find that the models perform better than a simple autoregressive benchmark; however, the relative forecast accuracy depends on the forecast horizon and also changes over time. For the full sample period the simultaneous equation model, which takes into account credit supply restrictions and real estate taxation, shows the best performance measured in terms of root mean squared forecasting error (RMSFE). In the first part of the sample (2005-2010), medium-term forecasts of house prices greatly benefit from conditioning on the evolution of households’ disposable income, whereas from 2010 onwards the path of the stock of mortgages becomes important.

Which Analysts Benefited Most from Mandatory IFRS Adoption in Europe?

ABSTRACTThis study examines whether financial analysts' research structure and portfolio selection choices helped in improving relative earnings forecast accuracy around mandatory IFRS adoption in Europe. Using a sample of 68,665 one-year ahead forecasts for 1,980 publicly listed firms, we find that market-wide mandatory IFRS adoption in the European Union resulted in a significant improvement in the relative forecast accuracy of sector specialists compared to generalists, while country specialists retained a relative accuracy advantage independent of the period of study. Moreover, sector specialists outperformed generalists especially for firms with large local GAAP to IFRS income reconciliations in the first-time IFRS adoption year and when analysts' research portfolios prior to IFRS adoption exhibited high variability in local GAAP reporting practices. Additional analysis indicates that IFRS enabled analysts to initiate coverage of more firms, especially smaller ones, in their sector of specialization. Overall, our results are consistent with the notion that mandatory IFRS adoption in Europe reduced information processing costs and enlarged the within-industry valuation scope.JEL Classifications: G15; M41.

Under weighting of Private Information by Top Analysts

It is conventionally perceived in the literature that weak analysts are likely to under weight their private information and strategically bias their announcements in the direction of the public beliefs to avoid scenarios where their private information turns out to be wrong, whereas strong analysts tend to adopt an opposite strategy of over weighting their private information and shifting their announcements away from the public beliefs in an attempt to stand out from the crowd. Analyzing a reporting game between two financial analysts, who are compensated based on their relative forecast accuracy, we demonstrate that it could be the other way around. An investigation of the equilibrium in our game suggests that, contrary to the common perception, analysts who benefit from information advantage may strategically choose to understate their exclusive private information and bias their announcements toward the public beliefs, while exhibiting the opposite behavior of overstating their private information when they estimate that their peers are likely to be equally informed.

Range Has it: Decoding the Information Content of Forecast Ranges

Given that most management forecasts in recent years are issued in range form and that most firms are followed by multiple analysts issuing dispersed forecasts, the conventional measure of management forecast news based on the midpoint of management forecast and the consensus analyst forecast does not fully capture the information content conveyed by range forecasts. This paper seeks to partially uncover such information by investigating an unexamined dimension unique to range forecasts –– overlapping between management forecast ranges and the ranges formed by individual analyst forecasts, which occurs in nearly 84% of range forecasts. Controlling for the width of management forecasts and analyst forecast dispersion, we find that compared with “overlap” management forecasts, “non-overlap” forecasts have greater impact on analyst revisions and stock market reaction to per unit forecast news. The non-overlap forecasts also turn out to have high relative forecast accuracy over analyst consensus forecasts. Moreover, individual analysts whose prior forecasts are outside of management forecast ranges respond more strongly to “non-overlap” forecasts than to “overlap” forecasts, and this difference is significant only when management forecasts convey bad news. Overall, our findings suggest that range forecasts convey more information beyond just the midpoint and analysts’ consensus.

Herding by Top Analysts

It is conventionally perceived in the literature that weak analysts are likely to under-weight their private information and strategically bias their announcements in the direction of the public beliefs to avoid scenarios where their private information turns out to be wrong, whereas strong analysts tend to adopt an opposite strategy of over-weighting their private information and shifting their announcements away from the public beliefs in an attempt to stand out from the crowd. Analyzing a reporting game between two financial analysts, who are compensated based on their relative forecast accuracy, we demonstrate that it could be the other way around. An investigation of the equilibrium in our game suggests that, contrary to the common perception, analysts who benefit from information advantage may strategically choose to understate their exclusive private information and bias their announcements toward the public beliefs, while exhibiting the opposite behavior of overstating their private information when they estimate that their peers are likely to be equally informed.

Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts

Summary Reservoir inflow forecasting is an integral element of hydropower systems operation and is of paramount importance to hydropower producers. Effective forecasts directly impact power production scheduling, which in turn effects the revenues earned from power production. In this study, we implement a filter updating procedure (GU-COMP) that updates the gains on the error-forecasting component of a complementary forecasting framework (COMP) that also comprises a conceptual model with time invariant parameters. The GU-COMP procedure is applied for forecasting hourly flows of the Krinsvatn catchment (207 km 2 ; located in Norway) over a forecast lead-time of 24 h. The gain coefficients are considered as the only state variables and are updated daily using the error observed between measured and forecasted flows at the catchment outlet. The performance is rated based on evaluation of filter performance (i.e. convergence and consistency), and relative assessment of forecasting skills using the root mean square error (RMSE) and the percentage volume error (PVE) metrics. Bracketing close to 95% of the innovation sequences within two standard deviations from the mean, the filter is found to be well behaved. The RMSE and PVE metrics agree that GU-COMP outperforms COMP in reducing the forecast errors, and significantly altering distributional characteristics of the PVEs in the spring and summer seasons. It is also noted that the relative forecast accuracy enhancement diminishes for forecast lead-times beyond 20 time steps (hours).

An analysis of persistence in analyst’s relative forecast accuracy

We examine the persistence in analysts’ relative earnings forecast accuracy. When analysts are ranked into forecast accuracy quintiles, calculated over all the firms they cover in each year, we find that 52% (45%) of superior (inferior) analysts, i.e. analysts in the lowest (highest) quintile, remain in this quintile in the subsequent period. We show that a variable we develop and denote as forecasting complexity, i.e. the extent to which analysts’ earnings forecasts vary when predicting a firm’s earnings, is important in explaining variation in the persistence of the relative forecast accuracy of analysts. When we control for forecasting complexity, the probability of analyst relative forecast accuracy to persist is reduced by about half. This reduced persistence, however, measures true forecasting ability. When we form portfolios using recommendations of analysts identified as superior in two consecutive periods, controlling for forecasting complexity, we find significant abnormal returns after adjustin...

Testing the CAPM with Analyst Data

We examine the relative forecast accuracy of expected returns for individual stocks sourced from analyst target prices, earnings per share estimates, management forecasts, earnings yields, stock yields, historical averages and the random walk model. In doing so, we avoid the use of predictive regressions. The most accurate estimate of the realized time series is provided by consensus (median) target prices, a forecast that has received little attention empirically. The return proxy is best for approximately 60% of the S&P 500 firms, offering valuations that are direct and subject to well-defined forecast horizons. Future studies should consider its use.

REAL-MONEY VS. PLAY-MONEY FORECASTING ACCURACY IN ONLINE PREDICTION MARKETS – EMPIRICAL INSIGHTS FROM IPREDICT

Prediction markets are online trading platforms where contracts on future events are traded with payoffs being exclusively linked to event occurrence. Scientific research has shown that market prices of such contracts imply high forecasting accuracy through effective information aggregation of dispersed knowledge. This phenomenon is related to incentives for truthful aggregation in the form of real-money or play-money rewards. The question whether real- or play-money incentives enhance higher relative forecast accuracy has been addressed by previous works with diverse findings. The current state of empirical research in his field is subject to two inherent deficiencies. First, inter-market studies suffer from market disparities and differences in the definition of underlying events. Comparisons between two different platforms (one for play-money contracts, one for real-money contracts) are potentially biased by different trading behaviour. Second, the majority of studies are based upon identical datasets of market platforms (IOWA stock exchange, Tradesports/Intrade, NewsFutures).This paper contributes new insights by analysing 44,169 trading observations on ipredict, where real-money and play-money contracts are traded on a variety of events. Forecasting accuracy is analysed on overall trading activity as well as comparison of equal contracts under different monetary incentive schemes. Statistical models are built to analyse the influence of order volumes and days to expiry under both incentive schemes. Ignoring different events in underlying trading activity, play-money contracts imply statistically insignificant excess accuracy. In direct comparison of equal events, real-money contracts, however, real-money contracts predict at significantly higher accuracy. This paper finds a relationship between order volumes and forecasting accuracy whereas the influence of days to expiry and aggregated volumes showed lower R² than was expected by formed hypotheses.

Do Industry‐Level Analyses Improve Forecasts of Financial Performance?

ABSTRACTPrior research documents mean reversion in firm profitability and growth under the implicit assumption that profitability and growth of all firms revert to a common benchmark at the same rate. However, a large body of academic research suggests that there are systematic interindustry differences (e.g., industry barriers to entry) that differentially affect firm performance based on industry membership. We evaluate the relative forecast accuracy of mean reverting models at the industry and economywide levels and find that industry‐specific models are generally more accurate in predicting firm growth but not profitability.

Does foreign investor demand for information affect forecast accuracy? Evidence from the Chinese stock markets

Prior international accounting studies have examined mainly the supply side of analyst research. Specific trading rights and financial reporting systems in China provide a unique opportunity to test both demand and supply factors of analyst information. For shares traded only by less-informed foreign investors, the increased information demand may create incentives for analysts to exert greater efforts than for shares traded by local investors. This study provides evidence that analyst effort (proxied by analyst coverage) and expertise (proxied by consensus cross-analyst forecast variability) affect forecast accuracy more significantly for shares traded by foreign investors than for shares traded by local investors. Additionally, I find that the relation between analyst characteristics and relative forecast accuracy is stronger for shares traded by the foreign investors. The findings are consistent with analysts responding to demands for information by less-informed investors.

FOMC Consensus Forecasts

In November 2007, the Federal Open Market Committee (FOMC) announced a change in the way it communicates its view of the economic outlook: It increased the frequency of its forecasts from two to four times per year, and it increased the length of the forecasting horizon from two to three years. The FOMC does not release the individual members' forecasts or standard measures of consensus such as the mean or median. Rather, it continues to release the forecast information as a range of forecasts, both the full range between the high and the low and a central tendency that omits the extreme values. This paper uses individual forecaster data from the Survey of Professional Forecasters (SPF) to mimic the FOMC's method for creating their central tendency. The authors show that the midpoint of the central tendency of the SPF is a reliable measure of the consensus, suggesting that the FOMC reporting method is also a reliable measure of consensus. For the dates when both are available, the authors also compare the relative forecast accuracy of the FOMC and SPF consensus forecasts for output growth and inflation. Overall, the differences in forecast accuracy are too small to be statistically significant.

Favorable versus Unfavorable Recommendations: The Impact on Analyst Access to Management‐Provided Information

ABSTRACTThis study examines recent regulatory and practitioner concerns that managers provide more (less) information to analysts with more (less) favorable stock recommendations. We examine the relative forecast accuracy of analysts before and after a recommendation issuance under the assumption that increases (decreases) in management‐provided information will increase (decrease) analysts' relative forecast accuracy. We find that analysts issuing more favorable recommendations experience a greater increase in their relative forecast accuracy compared with analysts with less favorable recommendations. Additional tests on the change in frequency with which analysts issue forecasts independent of or in conjunction with other analysts after their recommendation change yield corroborating results. In addition, we find that the greater increase in relative accuracy for analysts with more favorable recommendations exists prior to the passage of Regulation FD but not after. The combined results are consistent with analysts receiving relatively more management‐provided information following the issuance of more favorable recommendations.