Influence of differentiated roles on group forecasting accuracy

While behavioral research on forecasting has mostly examined the individual forecaster, organizationally-based forecasting processes typically tend to rely on groups with members from different functional areas for arriving at ‘consensus’ forecasts. The forecasting performance could also vary depending on the particular group structuring utilized in reaching a final prediction. The current study compares the forecasting performance of modified consensus groups with that of staticized groups using formal role-playing. It is found that, when undistorted model forecasts are given, group forecasts (whether they are arrived at through averaging or by a detailed discussion of the forecasts) contribute positively to the forecasting accuracy. However, providing distorted initial forecasts affects the final accuracy with varying degrees of improvement over the initial forecasts. The results show a strong tendency to favor optimistic forecasts for both the staticized and modified consensus group forecasts. Overall, the role modifications are found to be successful in eliciting a differential adjustment behavior, effectively mimicking the disparities between different organizational roles. Current research suggests that group discussions may be an efficient method of displaying and resolving differential motivational contingencies, potentially leading to group forecasts that perform quite well.     

The Delphi technique as a forecasting tool: issues and analysis

This paper systematically reviews empirical studies looking at the effectiveness of the Delphi technique, and provides a critique of this research. Findings suggest that Delphi groups outperform statistical groups (by 12 studies to two with two ‘ties’) and standard interacting groups (by five studies to one with two ‘ties’), although there is no consistent evidence that the technique outperforms other structured group procedures. However, important differences exist between the typical laboratory version of the technique and the original concept of Delphi, which make generalisations about ‘Delphi’ per se difficult. These differences derive from a lack of control of important group, task, and technique characteristics (such as the relative level of panellist expertise and the nature of feedback used). Indeed, there are theoretical and empirical reasons to believe that a Delphi conducted according to ‘ideal’ specifications might perform better than the standard laboratory interpretations. It is concluded that a different focus of research is required to answer questions on Delphi effectiveness, focusing on an analysis of the process of judgment change within nominal groups.     

Accuracy of German federal election forecasts, 2013 & 2017

The present study reviews the accuracy of four methods (polls, prediction markets, expert judgment, and quantitative models) for forecasting the two German federal elections in 2013 and 2017. On average across both elections, polls and prediction markets were most accurate, while experts and quantitative models were least accurate. However, the accuracy of individual forecasts did not correlate across elections. That is, the methods that were most accurate in 2013 did not perform particularly well in 2017. A combined forecast, calculated by averaging forecasts within and across methods, was more accurate than three of the four component forecasts. The results conform to prior research on US presidential elections in showing that combining is effective in generating accurate forecasts and avoiding large errors.     

Group-based forecasting?: A social psychological analysis

The potential of group (vs. individual) forecasting is analyzed from the perspective of the social psychology of groups. The social decision scheme theory (SDST) is summarized, and several simulations are presented to demonstrate the dependence of group aggregation accuracy upon factors such as group size, the accuracy and distribution of individual forecasts, and shared representations of the forecasting problem. Many advantages and disadvantages of group aggregation are identified and related to four generic methods of group aggregation (statistical aggregation, prediction markets, the Delphi method, and face-to-face discussion). A number of aspects of forecasting problems are identified which should govern whether or not group forecasting can be relied upon, and if so, what aggregation method should be used.     

Group diversity and decision quality: Amplification and attenuation of the framing effect

Do groups make better judgments and decisions than individuals? We tested the hypothesis that the advantage of groups over individuals in decision-making depends on the group composition. Our study used susceptibility to the framing effect as a measure of decision quality. Individuals were assigned to one of two perspectives on a choice problem. The individuals were asked to indicate their individual preference between a risky option and a risk-free option. Next, they were asked to consider the same (or a related) choice problem as a group. Homogeneous groups were composed of similarly framed individuals, while the heterogeneous ones were composed of differently framed individuals. In comparison to individual preferences, the homogeneous groups’ preferences were polarized, and thus the framing effect was amplified; in contrast, the heterogeneous groups’ preferences converged, and thus the framing effect was reduced to zero. The findings are discussed with regard to group polarization, the effects of heterogeneity on group performance, and the Delphi forecasting method.     

Forecasting (aggregate) demand for US commercial air travel

We analyze whether it is better to forecast air travel demand using aggregate data at (say) a national level, or to aggregate the forecasts derived for individual airports using airport-specific data. We compare the US Federal Aviation Administration’s (FAA) practice of predicting the total number of passengers using macroeconomic variables with an equivalently specified AIM (aggregating individual markets) approach. The AIM approach outperforms the aggregate forecasting approach in terms of its out-of-sample air travel demand predictions for different forecast horizons. Variants of AIM, where we restrict the coefficient estimates of some explanatory variables to be the same across individual airports, generally dominate both the aggregate and AIM approaches. The superior out-of-sample performances of these so-called quasi-AIM approaches depend on the trade-off between heterogeneity and estimation uncertainty. We argue that the quasi-AIM approaches exploit the heterogeneity across individual airports efficiently, without suffering from as much estimation uncertainty as the AIM approach.     

Decomposing the effects of crowd-wisdom aggregators: The bias–information–noise (BIN) model

Aggregating predictions from multiple judges often yields more accurate predictions than relying on a single judge, which is known as the wisdom-of-the-crowd effect. However, a wide range of aggregation methods are available, which range from one-size-fits-all techniques, such as simple averaging, prediction markets, and Bayesian aggregators, to customized (supervised) techniques that require past performance data, such as weighted averaging. In this study, we applied a wide range of aggregation methods to subjective probability estimates from geopolitical forecasting tournaments. We used the bias–information–noise (BIN) model to disentangle three mechanisms that allow aggregators to improve the accuracy of predictions: reducing bias and noise, and extracting valid information across forecasters. Simple averaging operates almost entirely by reducing noise, whereas more complex techniques such as prediction markets and Bayesian aggregators exploit all three pathways to allow better signal extraction as well as greater noise and bias reduction. Finally, we explored the utility of a BIN approach for the modular construction of aggregators.     

Is there an equilibrating relationship between house prices and replacement cost? Empirical evidence from Berlin

It is a feature of competitive markets with forward-looking participants that a good’s benefit and its production cost are equalized in equilibrium and that no resources are wasted during the adjustment process. For housing markets, there is mixed evidence whether they meet this standard of allocative efficiency. Based on a unique data set with rich information on prices and cost, we examine the market for single-family houses in Germany’s capital Berlin. At the aggregate market level, we find that prices and cost tend to equalize in the long run. Short-run adjustment appears to be sufficiently fast and properly anticipated to prevent systematic excess profit opportunities. At the cross sectional level of individual houses, we find support that resources are allocated efficiently between different market segments. Taken together, our results provide sufficient evidence that the market in Berlin is efficient.     

A comparison of open-ended and closed questions in the prediction of mental health

The statistical benefit of adding open-ended questions to closed questions was evaluated in a survey of 643 participants. The construct of coping was chosen as the measurement domain. Open and closed questions were used to predict mental health a year later. Verbatim responses to open questions were reliably coded (ICC = 0.92), but they did not increase the statistical prediction of measures of mental health beyond the contribution of closed questions. Open-ended questions provided more in-depth information than closed questions, but at the cost of more missing data and less degrees of freedom. The benefit of using open-ended in addition to standard closed questions was thus practically nil, hence questioning the use of qualitative information gathering in surveys for the purpose of statistical prediction.     

Do retail mergers affect competition? Evidence from grocery retailing

This study estimates the price effects of horizontal mergers in the U.S. grocery retailing industry. We examine fourteen regions affected by mergers, including mergers in highly concentrated and relatively unconcentrated markets. We identify price effects by comparing markets affected by mergers to unaffected markets using difference‐in‐difference estimation with three different comparison groups, propensity score weights, and by using the synthetic control method. Our results are robust to the choice of control group and estimation technique. We find that mergers in highly concentrated markets are most frequently associated with price increases, and mergers in less concentrated markets are most often associated with price decreases.     

Business ethics: A model to position a relative business ethics decision and a model to strengthen its application

The maxims of normative ethics are often in conflict. Thus business practitioners facing ethical questions often find themselves operating in the area of relative ethics. There are arguably two dimensions to this area. One lies on a spectrum from weak companies in highly competitive industries to strong companies in protected industries. The second dimension places the would-be ethical manager in awkward situations imposed from either the hierarchy or from corrupt markets. This article develops an analysis model to portray this relative ethics dilemma. Then, an argument is made that the more the individual manager practices good ethics, the higher the level of ethics the individual is able to maintain. This article proposes an adaptation of the 1950s feedback model of group dynamics known as the “Johari Window” to show this improvement in ethical behavior.     

Relative deprivation, relative satisfaction, and attitudes towards immigrants: Evidence from Ukraine

This paper applies the concept of relative deprivation/satisfaction to studying the formation of attitudes towards immigrants. It finds that the feeling of group relative deprivation, as opposed to individual relative deprivation or satisfaction, adversely affects the attitudes of native-born, even when its potential endogeneity is taken into account. Furthermore, relative deprivation matters only for native-born who subjectively underestimate their well-being, but not for those who overestimate it. When considering other forms of native-born individuals’ perceived disadvantage, such as in terms of employment, access to education or medical facilities, there is weak evidence that only a perceived disadvantage in obtaining medical aid negatively affects attitudes.     

If You Are So Smart,Why Aren't You an Entrepreneur? Returns to Cognitive and Social Ability: Entrepreneurs Versus Employees

How valuable are cognitive and social abilities for entrepreneurs’ relative to employees’ earnings? We answer three questions: (1) To what extent does a composite measure of ability affect an entrepreneur's earnings relative to wages earned by employees? (2) Do different cognitive abilities (e.g., math ability, language, or verbal ability) and social ability affect earnings of entrepreneurs and employees differently?, and (3) Does the balance in these measured ability levels affect an individual's earnings? Our (difference‐of‐difference) estimates of the returns to ability for spells in entrepreneurship versus wage employment account for selectivity into entrepreneurial positions insofar as they are determined by fixed individual characteristics. Our robust results provide the following answers to the three questions: General ability has a stronger impact on entrepreneurial incomes than on wages. Moreover, entrepreneurs and employees benefit from different sets of specific abilities: verbal and clerical abilities have a stronger impact on wages, whereas mathematical, social, and technical ability are more valuable for entrepreneurs. The balance in the various kinds of ability also generates a higher income, but only for entrepreneurs: This finding supports Lazear's Jack‐of‐all‐Trades theory.     

Judgmental aggregation strategies depend on whether the self is involved

We report the results of a novel experiment that addresses two unresolved questions in the judgmental forecasting literature. First, how does combining the estimates of others differ from revising one’s own estimate based on the judgment of another? The experiment found that participants often ignored advice when revising an estimate but averaged estimates when combining. This was true despite receiving identical feedback about the accuracy of past judgments. Second, why do people consistently tend to overweight their own opinions at the expense of profitable advice? We compared two prominent explanations for this, differential access to reasons and egocentric beliefs, and found that neither adequately accounts for the overweighting of the self. Finally, echoing past research, we find that averaging opinions is often advantageous, but that choosing a single judge can perform well in certain predictable situations.     

Exploring issues of market inefficiency by the role of forecasting accuracy in survivability

Whether markets are efficient or not has been broadly discussed in the empirical literature since the efficient markets hypothesis was proposed by Fama and others in the 1960s. Unfortunately, they did not come to a consistent conclusion. Besides, while these studies show whether a specific market is efficient or not, little has been done to explore the issues regarding the degree of market inefficiency. This paper attempts to resolve the puzzle of the inconsistent conclusions in the empirical literature by adopting a bottom-up approach which takes market participants’ interactions and coordination into consideration. By simulating an agent-based artificial stock market, this paper concludes with three main findings. First, agents’ survivability is mainly decided by risk preference, and not forecasting accuracy. Survivors may have diverse forecasting accuracy. Second, because market prices are not decided by agents based on accurate predictions, markets can not be efficient. What may exist is only the difference of the degree of inefficiency between markets. Third, the more relevant to survivability the forecasting accuracy in a market is, the less inefficient the market will be. Therefore, this paper suggests that it may be better to view the divergent empirical results regarding market efficiency as a fact that markets are inefficient to a variety of degrees.     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

Trends in local government in Europe

Abstract

The article explores which reform trends are expected to play a significant role in the further development of local government in European countries. Analysis is based on an adopted Delphi survey of (mainly academic) local government experts from thirty-two countries. Results include a list of trends structured by the perceived importance for the coming ten years. Redistributing responsibilities and duties across levels of government constitute the highest ratings out of all trends. Furthermore, results do not provide clear evidence to support a shift from public management to governance as has recently been proclaimed in academic debate and literature. Management topics continue to be of great importance. Last but not least, clusters extracted point to different reform groups, which highlight more reluctant or enthusiastic attitudes to the relevance of the suggested trends of the study.     

Group penalized logistic regressions predict up and down trends for stock prices

Stock prices are influenced by many economic factors, investors psychology and expectations, movement of other stock markets, political events, etc. Therefore, correctly predicting up and down trends for stock prices is an important puzzle in the financial field. In this paper we combine technical analysis with group penalized logistic regressions, and propose group SCAD/MCP penalized logistic regressions with technical indicators to predict up and down trends for stock prices. Firstly, we screen out 24 important technical indicators, divide them into the five different indicator groups, and construct group SCAD/MCP penalized logistic regressions for the three listed companies. Secondly, we apply the training set to learn the parameter estimators and the probability estimators for the two group penalized logistic regressions, adopt the test set to obtain confusion matrices and ROC(Receiver Operating Characteristic) curves to assess their prediction performances, and found that the AUC values to the three companies all exceed 0.78. Finally, we compare group SCAD/MCP penalized logistic regressions with SCAD/MCP penalized logistic regressions, and found that the two group penalized logistic regressions perform better than the two penalized logistic regressions in terms of prediction accuracy and AUC. Therefore, in this paper we develop a new prediction method by combining group SCAD/MCP penalized logistic regressions with technical indicators to improve the prediction accuracy and bring huge economic benefit for investors.     

Efficient Markets, Fundamentals, and Crashes

A bstract .Economists have struggled to characterize and model the dynamic evolution of economic phenomena throughout this century. For at least eight decades, American economists have faced the persistent choice between structural or formal models of evolving dynamics and those alternative portrayals that focused on historical narratives or qualitative features. In this article we compare the models and methods used by representative authors who have sought to address the swings in stock prices. Our rather terse comparison between the orthodox theorists and those we label heterodox demonstrates the wide divergence between the foci of approaches adopted. Their methods, conclusions, and implications differ markedly. Where the orthodox approaches focus all but exclusively on statistical issues, the heterodox group stresses the importance of incomplete information and markets in an institutional and historical context. Such fundamental differences prohibit any meaningful dialogue between proponents of the two basic approaches. Moreover, substantial deficiencies–technical and otherwise–of contributions within the two groups limit ability to discriminate between rival views within each group. The clearer identification of transparent roles for credit, technology, and institutions in the heterodox approaches, however, makes the awkward translation from theoretical model to social commentary much easier.