A new insight into combining forecasts for elections: The role of social media

This study is devoted to gain insight into a timely, accurate, and relevant combining forecast by considering social media (Facebook), opinion polls, and prediction markets. We transformed each type of raw data into the possibility of victory as a forecasting model. Besides the four single forecasts, namely Facebook fans, Facebook “people talking about this” (PTAT) statistics, opinion polls, and prediction markets, we generated three combined forecasts by associating various combinations of the four components. Then, we examined the predictive performance of each forecast on vote shares and the elected/non‐elected outcome across the election period. Our findings, based on the evidence of Taiwan's 2018 county and city elections, showed that incorporating the Facebook PTAT statistic with polls and prediction markets generates the most powerful forecast. Moreover, we recognized the matter of the time horizons where the best proposed model has better accuracy gains in prediction—in the “late of election,” but not in “approaching election”. The patterns of the trend of accuracy across time for each forecasting model also differ from one another. We also highlighted the complementarity of various types of data in the paper because each forecast makes important contributions to forecasting elections.     

Sports forecasting: a comparison of the forecast accuracy of prediction markets,betting odds and tipsters

This article compares the forecast accuracy of different methods, namely prediction markets, tipsters and betting odds, and assesses the ability of prediction markets and tipsters to generate profits systematically in a betting market. We present the results of an empirical study that uses data from 678–837 games of three seasons of the German premier soccer league. Prediction markets and betting odds perform equally well in terms of forecasting accuracy, but both methods strongly outperform tipsters. A weighting‐based combination of the forecasts of these methods leads to a slightly higher forecast accuracy, whereas a rule‐based combination improves forecast accuracy substantially. However, none of the forecasts leads to systematic monetary gains in betting markets because of the high fees (25%) charged by the state‐owned bookmaker in Germany. Lower fees (e.g., approximately 12% or 0%) would provide systematic profits if punters exploited the information from prediction markets and bet only on a selected number of games. Copyright © 2008 John Wiley & Sons, Ltd.     

Are all crowds equally wise? a comparison of political election forecasts by experts and the public

Four groups made forecasts of the outcome of the Swedish Parliamentary election in the fall of 2006. They consisted of members of the public, political scientists, journalists writing about domestic politics in Swedish daily newspapers, and journalists who were editing sections of readers' letters in daily newspapers. They estimated, using a 12‐step category scale, which percentage of the votes that they believed seven parties would get in the election. Data were then obtained on the outcome of the election, and on the two opinions polls closest in time to it. When median forecasts were compared across groups, it was found that the group from the public was most successful in forecasting the outcome of the election. This was in spite of the fact that the median error made by individual members of that group was about 50% larger than the median error made by members of other groups. The two polls were less efficient than the group from the public and overestimated the span between the incumbent government and the opposition by a factor of 2. The members of the public and journalists showed some wishful thinking in their forecasts. There were large and consistent individual differences in forecasting ability. Men performed better than women, as did those who expressed more interest and knowledge in politics. Copyright © 2008 John Wiley & Sons, Ltd.     

Bias‐corrected bootstrap prediction regions for vector autoregression

This paper examines small sample properties of alternative bias‐corrected bootstrap prediction regions for the vector autoregressive (VAR) model. Bias‐corrected bootstrap prediction regions are constructed by combining bias‐correction of VAR parameter estimators with the bootstrap procedure. The backward VAR model is used to bootstrap VAR forecasts conditionally on past observations. Bootstrap prediction regions based on asymptotic bias‐correction are compared with those based on bootstrap bias‐correction. Monte Carlo simulation results indicate that bootstrap prediction regions based on asymptotic bias‐correction show better small sample properties than those based on bootstrap bias‐correction for nearly all cases considered. The former provide accurate coverage properties in most cases, while the latter over‐estimate the future uncertainty. Overall, the percentile‐t bootstrap prediction region based on asymptotic bias‐correction is found to provide highly desirable small sample properties, outperforming its alternatives in nearly all cases. Copyright © 2004 John Wiley & Sons, Ltd.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Do experts' adjustments on model‐based SKU‐level forecasts improve forecast quality?

Model‐based SKU‐level forecasts are often adjusted by experts. In this paper we propose a statistical methodology to test whether these expert forecasts improve on model forecasts. Application of the methodology to a very large database concerning experts in 35 countries who adjust SKU‐level forecasts for pharmaceutical products in seven distinct categories leads to the general conclusion that expert forecasts are equally good at best, but are more often worse than model‐based forecasts. We explore whether this is due to experts putting too much weight on their contribution, and this indeed turns out to be the case. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecasting US employment growth using forecast combining methods

We examine different approaches to forecasting monthly US employment growth in the presence of many potentially relevant predictors. We first generate simulated out‐of‐sample forecasts of US employment growth at multiple horizons using individual autoregressive distributed lag (ARDL) models based on 30 potential predictors. We then consider different methods from the extant literature for combining the forecasts generated by the individual ARDL models. Using the mean square forecast error (MSFE) metric, we investigate the performance of the forecast combining methods over the last decade, as well as five periods centered on the last five US recessions. Overall, our results show that a number of combining methods outperform a benchmark autoregressive model. Combining methods based on principal components exhibit the best overall performance, while methods based on simple averaging, clusters, and discount MSFE also perform well. On a cautionary note, some combining methods, such as those based on ordinary least squares, often perform quite poorly. Copyright © 2008 John Wiley & Sons, Ltd.     

The Predictive Power of Survey‐Based Exchange Rate Forecasts: Is there a Role for Dispersion?

Previous research has shown that the consensus of individual exchange rate forecasts performs no better than many commonly used forecasting models in predicting future exchange rates. Studies on equity and bond markets have explored the effects of dispersion in forecasts on the predictive power of forecasts; however, no earlier paper has investigated such effects in the context of the foreign exchange market. This study explores the role of consensus forecast dispersion as a factor leading to bias and anchoring in exchange rate forecasts. Our analysis of five currency pairs reveals that consensus forecasts mostly appear to be unbiased predictors of exchange rates in the long run, but most are unable to pass tests for short‐run unbiasedness. In three of the five currencies examined it appears that forecasters should take greater account of reported forecast dispersion. Copyright © 2015 John Wiley & Sons, Ltd.     

Second‐Generation Prediction Markets for Information Aggregation: A Comparison of Payoff Mechanisms

Initial applications of prediction markets (PMs) indicate that they provide good forecasting instruments in many settings, such as elections, the box office, or product sales. One particular characteristic of these ‘first‐generation’ (G1) PMs is that they link the payoff value of a stock's share to the outcome of an event. Recently, ‘second‐generation’ (G2) PMs have introduced alternative mechanisms to determine payoff values which allow them to be used as preference markets for determining preferences for product concepts or as idea markets for generating and evaluating new product ideas. Three different G2 payoff mechanisms appear in the existing literature, but they have never been compared. This study conceptually and empirically compares the forecasting accuracy of the three G2 payoff mechanisms and investigates their influence on participants' trading behavior. We find that G2 payoff mechanisms perform almost as well as their G1 counterpart, and trading behavior is very similar in both markets (i.e. trading prices and trading volume), except during the very last trading hours of the market. These results indicate that G2 PMs are valid instruments and support their applicability shown in previous studies for developing new product ideas or evaluating new product concepts. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal selection of forecasts

Many studies have shown that, in general, a combination of forecasts often outperforms the forecasts of a single model or expert. In this paper we postulate that obtaining forecasts is costly, and provide models for optimally selecting them. Based on normality assumptions, we derive a dynamic programming procedure for maximizing precision net of cost. We examine the solution for cases where the forecasters are independent, correlated and biased. We provide illustrative examples for each case.     

Forecasting the Outcome of Closed‐Door Decisions: Evidence from 500 Years of Betting on Papal Conclaves

Closed‐door decisions may be defined as decisions in which the outcome is determined by a limited number of decision‐makers and where the process is shrouded in at least some secrecy. In this paper, we examine the use of betting markets to forecast one particular closed‐door decision: the election of the pope. Within the context of 500 years of papal election betting, we employ a unique dataset of betting on the 2013 papal election to investigate how new public information is incorporated into the betting odds. Our results suggest that the market was generally unable to incorporate effectively such information. We venture some possible explanations for our findings and offer suggestions for further research into the prediction and predictability of other ‘closed‐door’ decisions. Copyright © 2015 John Wiley & Sons, Ltd.     

Financial density forecasts: A comprehensive comparison of risk‐neutral and historical schemes

We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies—small samples, limited models, and nonholistic validations—by performing a comprehensive comparison of 15 predictive schemes during a time period of over 21 years. All densities are evaluated in terms of their statistical consistency, local accuracy and forecasting errors. Using a new composite indicator, the integrated forecast score, we show that risk‐neutral densities outperform historical‐based predictions in terms of information content. We find that the variance gamma model generates the highest out‐of‐sample likelihood of observed prices and the lowest predictive errors, whereas the GARCH‐based GJR‐FHS delivers the most consistent forecasts across the entire density range. In contrast, lognormal densities, the Heston model, or the nonparametric Breeden–Litzenberger formula yield biased predictions and are rejected in statistical tests.     

Combining exchange rate forecasts: What is the optimal consensus measure?

In this paper a high-quality disaggregate database is utilized to examine whether individual forecasters produce efficient exchange rate predictions and also if the properties of the forecasts change when they are combined. The paper links a number of themes in the exchange rate literature and examines various methods of forecast combination. It is demonstrated, inter alia, that some forecasters are better than others, but that most are not as good as a naive no-change prediction. Combining forecasts adds to the accuracy of the predictions, but the gains mainly reflect the removal of systematic and unstable bias.     

Can cointegration‐based forecasting outperform univariate models? An application to Asian exchange rates

Conventional wisdom holds that restrictions on low‐frequency dynamics among cointegrated variables should provide more accurate short‐ to medium‐term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long‐term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short‐ and medium‐term forecasting accuracy of univariate Box–Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling‐window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving‐average terms of order >1. ECMs perform better over medium‐term time horizons for series with no moving average terms. The results suggest a need to distinguish between ‘sequential’ and ‘synchronous’ forecasting ability in such comparisons. Copyright © 2002 John Wiley & Sons, Ltd.     

Are Analysts' Loss Functions Asymmetric?

Despite displaying a statistically significant optimism bias, analysts' earnings forecasts are an important input to investors’ valuation models. Understanding the possible reasons for any bias is important if information is to be extracted from earnings forecasts and used optimally by investors. Extant research into the shape of analysts' loss functions explains optimism bias as resulting from analysts minimizing the mean absolute forecast error under symmetric, linear loss functions. When the distribution of earnings outcomes is skewed, optimalforecasts can appear biased. In contrast, research into analysts' economic incentives suggests that positive and negative earnings forecast errors made by analysts are not penalized or rewarded symmetrically, suggesting that asymmetric loss functions are an appropriate characterization. To reconcile these findings, we exploit results from economic theory relating to the Linex loss function to discriminate between the symmetric linear loss and the asymmetric loss explanations of analyst forecast bias. Under asymmetric loss functions optimal forecasts will appear biased even if earnings outcomes are symmetric. Our empirical results support the asymmetric loss function explanation. Further analysis also reveals that forecast bias varies systematically across firm characteristics that capture systematic variation in the earnings forecast error distribution. Copyright © 2011 John Wiley & Sons, Ltd.     

Do Experts’ SKU Forecasts Improve after Feedback?

We analyze the behavior of experts who quote forecasts for monthly SKU‐level sales data, where we compare data before and after the moment that experts received different kinds of feedback on their behavior. We have data for 21 experts located in as many countries who make SKU‐level forecasts for a variety of pharmaceutical products for October 2006 to September 2007. We study the behavior of the experts by comparing their forecasts with those from an automated statistical program, and we report the forecast accuracy over these 12 months. In September 2007 these experts were given feedback on their behavior and they received training at the headquarters office, where specific attention was given to the ins and outs of the statistical program. Next, we study the behavior of the experts for the 3 months after the training session, i.e. October 2007 to December 2007. Our main conclusion is that in the second period the experts’ forecasts deviated less from the statistical forecasts and that their accuracy improved substantially. Copyright © 2013 John Wiley & Sons, Ltd.     

Realized Volatility Forecast of Stock Index Under Structural Breaks

We investigate the realized volatility forecast of stock indices under the structural breaks. We utilize a pure multiple mean break model to identify the possibility of structural breaks in the daily realized volatility series by employing the intraday high‐frequency data of the Shanghai Stock Exchange Composite Index and the five sectoral stock indices in Chinese stock markets for the period 4 January 2000 to 30 December 2011. We then conduct both in‐sample tests and out‐of‐sample forecasts to examine the effects of structural breaks on the performance of ARFIMAX‐FIGARCH models for the realized volatility forecast by utilizing a variety of estimation window sizes designed to accommodate potential structural breaks. The results of the in‐sample tests show that there are multiple breaks in all realized volatility series. The results of the out‐of‐sample point forecasts indicate that the combination forecasts with time‐varying weights across individual forecast models estimated with different estimation windows perform well. In particular, nonlinear combination forecasts with the weights chosen based on a non‐parametric kernel regression and linear combination forecasts with the weights chosen based on the non‐negative restricted least squares and Schwarz information criterion appear to be the most accurate methods in point forecasting for realized volatility under structural breaks. We also conduct an interval forecast of the realized volatility for the combination approaches, and find that the interval forecast for nonlinear combination approaches with the weights chosen according to a non‐parametric kernel regression performs best among the competing models. Copyright © 2014 John Wiley & Sons, Ltd.     

Forecasting Errors,Directional Accuracy and Profitability of Currency Trading: The Case of EUR/USD Exchange Rate

We provide a comprehensive study of out‐of‐sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of deviation is limited. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting inflation using economic indicators: the case of France

In order to provide short‐run forecasts of headline and core HICP inflation for France, we assess the forecasting performance of a large set of economic indicators, individually and jointly, as well as using dynamic factor models. We run out‐of‐sample forecasts implementing the Stock and Watson (1999) methodology. We find that, according to usual statistical criteria, the combination of several indicators—in particular those derived from surveys—provides better results than factor models, even after pre‐selection of the variables included in the panel. However, factors included in VAR models exhibit more stable forecasting performance over time. Results for the HICP excluding unprocessed food and energy are very encouraging. Moreover, we show that the aggregation of forecasts on subcomponents exhibits the best performance for projecting total inflation and that it is robust to data snooping. Copyright © 2007 John Wiley & Sons, Ltd.     

Combining expert‐adjusted forecasts

It is well known that a combination of model‐based forecasts can improve upon each of the individual constituent forecasts. Most forecasts available in practice are, however, not purely based on econometric models but entail adjustments, where experts with domain‐specific knowledge modify the original model forecasts. There is much evidence that expert‐adjusted forecasts do not necessarily improve the pure model‐based forecasts. In this paper we show, however, that combined expert‐adjusted model forecasts can improve on combined model forecasts, in the case when the individual expert‐adjusted forecasts are not better than their associated model‐based forecasts. We discuss various implications of this finding.