Biases of professional exchange rate forecasts: Psychological explanations and an experimentally based comparison to novices

The empirical performance of macroeconomic exchange rate models is more than disappointing. This dismal result is also reflected in the forecasting capabilities of professional analysts: all in all, analysts are not in a position to beat naïve random walk forecasts. The root for this deficient outcome stems from the fact that professional forecasts are to a large extend influenced by actual changes in exchange rates. A reasonable explanation for this behaviour can be taken from the behavioural finance literature. To test whether this characteristic tends to be general human behaviour in an uncertain environment, we analyse the forecasting behaviour of students experimentally, using a simulated currency series. Our results indicate that a topically oriented trend adjustment behaviour (TOTA) is a general characteristic of human forecasting behaviour. Additionally, we apply a simple model to explain professional and students forecasts. --Foreign exchange market,forecasting,behavioural finance,anchoring heuristics,judgement,expertise

Judgmental forecasting: Factors affecting lay people's expectations of inflation

In this thesis, laypeople’s judgmental forecasting about inflation is reviewed and experimentally explored in six chapters. Inflation is defined as the Consumer Price Index (CPI) across the whole thesis. In Chapter 1, I review work on the formation of inflation expectations, drawing mainly from the economic literature. In Chapter 2, I review research on judgmental forecasting, drawing mainly from the literature in cognitive psychology and management science. In Chapter 3, three experiments are presented that were designed to determine how and when people employ internal information of experienced price changes to form inflation expectations. In Chapter 4, three experiments are used to investigate the effects of providing within-series and across-series historical information (inflation rates, interest rates and unemployment rates) on inflation expectations. In Chapter 5, two experiments are reported that examine how training using simple outcome feedback increases the accuracy of inflation judgments and improves the calibration of confidence in those judgments. Chapter 6 reports experiments designed to examine the effects of using different elicitation methods (point forecasts, interval forecasts and density forecasts) on the accuracy of inflation judgments. Chapter 7 is a concluding chapter that summarises findings from these experiments and suggests avenues for future work

Forecasting the success of megaprojects with Judgmental methods

Forecasting the success of megaprojects is a very difficult and important task because ofthe complexity of such projects, as well as the large capital investment that is required forthe completion of these projects. One could argue that forecasting is not needed in thiscontext: the master Gantt chart of the tasks with assigned person-hours plus therespective Bill of Materials should suffice for an accurate estimation of the duration andcost of a project. If that was the case then every project would finish on time and on budget– but this is far from true as the numerous examples attest: HS2, Channel Tunnel, majorIT public projects in NHS, to name a few. In this research, we employ judgmentalforecasting methods to predict the success of megaprojects in as series of forecastingexperiments. In the first experiment,the participants forecast for one megaproject ('spaceexploration') with Unaided Judgment (UJ), Structured Analogies (SA) and InteractionGroups (IG) with IG showing the best results since IG>SA>SA. In the second experiment,we use a second megaproject ('a major recreational facility in the very city centre of amajor cosmopolis') and see separately the success in terms of excesses in the budget andthe duration of the project. Furthermore, the participants forecast the extent to which thesocio-economic benefits are realised. We do analyse three different stakeholderperspectives: that of the a) project manager, b) funder(s), and c) the public. We do controlfor two levels of expertise – novices, and semi-experts, and the participants use UJ, SA, IGand Delphi (D) as well, resulting IG>D>SA>UJ. In the third and final experiment, wequalitatively explore the use of scenarios in forecasting the success of megaprojects

Order effects in judgmental forecasting

In two experiments, forecasters produced a sequence of five forecasts from different types of time series, either from the nearest horizon to the most distant one (1, 2, 3, 4, 5) or in one of two other orders, both of which required the forecast for the most distant horizon to be made first (‘end-anchoring’). These latter two orders differed in terms of the direction of the remaining forecasts: either a horizon-increasing order (1, 2, 3, 4) or a horizon-decreasing one (4, 3, 2, 1). End-anchoring improved the forecast accuracy, especially for more distant horizons, and resulted in the trajectory of the forecast sequence being closer to the optimal one. The direction of forecasting after end-anchoring affected the forecast quality only when the optimal trajectory of the forecast sequence displayed a strong nonlinearity. End-anchoring provides a simple means of enhancing judgmental forecasts when predictions for a number of horizons are being produced from each series

Use of expert knowledge to anticipate the future : issues, analysis and directions

Unless the anticipation problem is routine and short-term, and objective data are plentiful, expert judgment will be needed. Risk assessment is analogous to anticipation of the future in that models need to be developed and applied to data. Since objective data are often scanty, expert knowledge elicitation (EKE) techniques have been developed for risk assessment that allow model development and parametrization using expert judgments with minimal cognitive and social biases. Here, we conceptualize how EKE can be developed and applied to support anticipation of the future. Accordingly, we first define EKE as an entire process, that involves considering experts as a source of data, and that comprises various methods for ensuring the quality of this data, including – selecting the best experts, training experts in normative aspects of anticipation, and combining judgments of several experts – as well as eliciting unbiased estimates and constructs from experts. We detail aspects of the papers that constitute the Special Issue and analyse these in terms of the stages within the EKE future-anticipation process that they address. We identify the remaining gaps in our knowledge. Our conceptualization of EKE to support anticipation of the future is compared and contrasted with the extant research effort into judgmental forecasting

Accuracy in design cost estimating

The level of achieved accuracy in design cost estimating is generally accepted by researchers as being less than desirable. Low accuracy has been attributed to the nature of historical cost data, estimating method and the expertise of the estimator. Previous researchers have suggested that the adoption of resource based estimating by designers could eliminate data and method-related problems. The work in this thesis has shown that this will not solve the problem of inaccuracy in estimating. A major problem in assessing accuracy in design cost estimating has been the absence of a generally agreed definition of the'true cost' of a construction project. Hitherto, studies of accuracy in design cost estimating have relied solely on the assessment of errors using the low bid as a datum. Design cost estimators do not always focus on predicting the low bid. Rather, they may focus on the lowest, second lowest, third lowest or any other bid, mean/median of bids, or sometimes, on just being'within the collection'. This has resulted in designers and researchers having different views on the level of achieved accuracy in estimating. To resolve this problem, an analysis package, ACCEST (ACCuracy in ESTimating), was developed to facilitate 'fair' assessment of accuracy in design cost estimates. Tests - using cost data from 7 offices, the ACCEST package and the OPEN ACCESS II package on an IBM PS/2 - have shown that error in design cost estimating (averaging 3.6% higher than the predicted parameter) is much lower than portrayed in construction literature (averagel3% higher than the low bid). Also, false associations between project environment factors (such as geographical location, market conditions, number of bidders, etc.) and the level of achieved accuracy has been developed by researchers through using the low bid as a datum. Previous researches have also demonstrated that design estimators do not learn sufficiently from experience on past projects. A controlled experiment on design cost estimating information selection was designed to explain this occurrence. Failure to learn, and the persistent use of information on one project for estimating, has been shown to result from the method of information storage in design offices, the illusion of validity of inaccurate rules and over-confidence resulting from inaccurate assessment of individual expertise. A procedure for aiding learning from experience in design cost estimating has been suggested. Finally, the work has shown that by distinguishing between different trades, and selectively applying different estimating strategies, based on the objective evaluation of the uncertainty associated with cost prediction for ear h trade, error in design cost estimating could be further reduced. Two formulae for predicting tender prices using data generated from historical cost estimating experience are represented