روش دلفی در پیش بینی بازارهای مالی؛ مطالعه تجربی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|14154||2014||15 صفحه PDF||سفارش دهید||11520 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Forecasting, Volume 30, Issue 2, April–June 2014, Pages 313–327
Experts were used as Delphi panellists and asked to present forecasts on financial market variables in a controlled experiment. We found that the respondents with the least accurate or least conventional views were particularly likely to modify their answers. Most of these modifications were in the right direction but too small, probably because of belief-perseverance bias. This paper also presents two post-survey adjustment methods for Delphi method based forecasts. First, we present a potential method to correct for the belief perseverance bias. The results seem promising. Secondly, we test a conditional forecasting process, which unexpectedly proves unsuccessful.
The Delphi method was introduced at the RAND Corporation in the 1950s. It aims to maintain the advantages of an interacting group without potentially counterproductive group dynamics, such as dominant individuals who may not be the best experts. In short, the traditional version of the method is based on a multi-round survey. Respondents are asked to answer a number of questions in writing. Answering is anonymous; other respondents do not know who answered what. In most cases the answers are numeric estimates, ratings on a scale, or yes/no. Often, the respondents also have the opportunity to write comments on the issues raised in the questionnaire. Statistics on answers and the related comments are subsequently distributed to the respondents, but this information is anonymous and no respondent can identify who answered what. Each respondent is allowed to modify his own answers, and possibly to add more comments. After a few rounds, some convergence in answers is normally observed due to a group opinion building process, leading to less variance in the answers and more agreement within the panel. The number of rounds can be either predetermined or dependent on the criteria of consensus and stability. In the papers reviewed by Rowe and Wright (1999), the number of respondents in Delphi panels varied from 3 to 98. Ideally, the respondents will all be experts in the same field, but with somewhat different backgrounds. Linstone and Turoff (2011) emphasised the role of communication in judgemental forecasting, and argued that the Internet will have a major impact on the way in which comparable methods will be used in the future, since the number of potential participants will be much larger than in traditional Delphi panels. In recent years, some studies have used the real-time version of the method (see Gordon & Pease, 2006), which is normally an online application that allows respondents to modify their answers at any time, up until the end of the answering process (for the validation of the method, see Gnatzy, Warth, von der Gracht, & Darkow, 2011). The final answer of the group is defined as the mean or median of the individual answers. In many cases, even the questions to be answered are proposed and selected by group members themselves before the first answering round. The forecasting accuracy of the group normally improves over Delphi rounds, and the Delphi method works better than staticized groups, i.e., simple one-round surveys; this finding has been reported by Dalkey (1968), Graefe and Armstrong (2011), Helmer (1964), Parenté et al. (2005) and Song, Gao, and Lin (2013), as well as in various studies reviewed by Rowe and Wright (1999). In some experiments, respondent groups have been asked to provide estimates on almanac events, i.e. issues related to the past and present (see for example Graefe & Armstrong, 2011); whereas in other cases they have been asked to forecast the future (Parenté et al., 1984 and Parenté et al., 2005). More detailed descriptions are provided by Linstone and Turoff (1975), Parenté and Anderson-Parenté (1987) and Rowe, Wright, and Bolger (1991). In light of most of the earlier experimental research, the Delphi method seems to be either substantially (Basu and Schroeder, 1977 and Riggs, 1983) or somewhat (Graefe & Armstrong, 2011) better than Face-to-Face (FTF) meetings, although some authors have found the differences to be negligible (Brockhoff, Kaerger, & Rehder, 1975). Findings by previous authors have been summarised by Rowe and Wright (1999, Table 4) and Woudenberg (1991, Table 3); according to both surveys, most previous contributions had found that, with some exceptions, the Delphi method had outperformed traditional meetings. So is it reasonable to use sophisticated, structured processes if there is no unambiguous evidence that they yield significantly better forecasts than a simple FTF meeting? As will be seen in this paper, structured techniques have at least one clear advantage: they can be improved and the forecasts honed. In our study, the FTF meeting is used as the benchmark case. This is the simplest and probably the most commonly used method; group members sit in the same room and discuss the issues until they reach a consensus, or at least until the majority backs a view. According to Kerr and Tindale (2011), such meetings are good for pooling information, mutual error checking and motivation enhancement. On the other hand, they may be particularly vulnerable to the ‘tyranny of the majority’, the dominance of powerful individuals, inattention to unshared information, or group overconfidence. Other potential problems of FTF meetings include the bandwagon effect (the tendency of ideas to spread among people like fads), the underdog effect (the tendency of some people to vote for losing candidates or views), and the halo effect (the tendency to weight an opinion according to a general impression of the person who expresses it). According to Ang and O’Connor (1991, p. 142), the Delphi method combines mathematical and behavioural approaches, with an ‘aim to improve behavioural aggregation by substituting the dysfunctional aspects of achieving consensus with a mathematical process of achieving the final group judgement’. In the best case, the method helps to eliminate a number of problems with FTF meetings, such as the influence of dominant individuals and the unwillingness of many people to defend unorthodox views, even well-founded ones. Different biases in the Delphi method have also been studied. For example, Ecken, Gnatzy, and von der Gracht (2011) discuss the desirability bias: the general tendency of respondents to over-estimate the probability of events that they consider to be desirable. Unfortunately, no direct test of the ability of the proposed correction to improve the forecast accuracy was presented, but evidence was given on the existence of the bias. This paper has two main objectives. First, we address the development of individual answers during the process. Secondly, we try to develop the Delphi method further, using observations on panellists’ behaviours and findings from existing research in psychology. The use of post survey methods to increase the accuracy of forecasts is not a new idea. Armstrong (2006) listed and evaluated evidence on numerous post hoc methods. However, our methods differ from those presented by Armstrong. Instead of assigning different weights to the panellists depending on, for example, previous forecasting performances, we correct the assumed undersized adjustments which the panellists had made between the first and last rounds. This paper is organised as follows. In the next section we provide a number of hypotheses that are subsequently tested empirically. Section 3 details the experiment. Section 4 focuses on forecasting accuracy, and Section 5 examines the dynamics of individual forecasts in the Delphi context. Section 6 examines post-forecast correction methods, and the last section concludes.
نتیجه گیری انگلیسی
This paper presents the findings from a controlled forecasting experiment. Expert groups were asked to make quantitative forecasts for different variables related to Finnish financial markets. The Delphi method was tested and the results were compared with the performance of a Face-to-Face meeting of a reference group. The forecasting horizon was relatively short, only a few months. The Delphi method and FTF meeting seemed to be roughly equally reliable. In preliminary calculations (which are not presented in detail), both methods clearly outperformed simple trend extrapolations based on the assumption that the growth rates observed in the past will continue in the future. Because the Delphi respondents used pseudonyms when answering, we were able to see how the answers of individual respondents changed from round to round, even though their responses were essentially anonymous. The accuracy of individual answers tended to improve during the process, and the improvements tended to be substantial if the initial forecast was either remarkably imprecise or not in line with the rest of the group. In most cases, the respondents changed their answers in the right direction but by too little. Experimental results, such as those of Brockhoff et al. (1975) and Graefe and Armstrong (2011), and the findings presented above, indicate that the Delphi method as such may not be significantly more reliable than FTF meetings. However, structured processes have one major advantage. When there are large numbers of individual answers from different rounds, it is easier to apply objectively testable techniques to correct for known cognitive biases. The output from a traditional FTF meeting consists of one answer per question, which provides much less data for use as inputs to post-hoc calculations. Two potential ways of improving the forecasting accuracy were tested. First, we tried to eliminate the belief perseverance bias reported by psychologists. This was only possible with the Delphi method. Each individual answer was moved further in the direction which it had already been moved by the respondent. These corrections led to significant improvements in the forecasts. However, further research and experimentation is needed before we can say how general this result is and how stable the optimal parametrisation is. Second, the respondents were asked to evaluate how their answers would depend on the 12-month money market rate. Using this estimated interest rate-sensitivity, the forecasts were adjusted post hoc according to the difference between the forecasted interest rate and the outcome. Perhaps surprisingly, this experiment failed, suggesting that knowing the interest rate outcome many months in advance would not have helped the respondents to make accurate forecasts of other variables. Attempts at improving judgemental forecasting processes should take maximal advantage of the findings reported in the psychological literature. By definition, judgemental forecasting is based on the workings of the human mind, and is known to be affected by cognitive biases. Indeed, it is difficult to see why being a Delphi panellist would render one immune to psychological phenomena. Delphi panels in real-world forecasting tasks often consist of experts, and, perhaps paradoxically, experts seem to be affected more than laymen by belief perseverance bias at least; they are less likely to change their original answers (Hussler et al., 2011). An obvious candidate for a future forecasting exercise is the subadditivity effect, the tendency to misperceive the probability of an event as being less than the sum of the probabilities of two mutually exclusive versions of the event (see Tversky & Koehler, 1994). Taking such cognitive biases into account in the interpretation and processing of survey results would seem to be a promising area for future research.