金融paper格式：Overview of the Judgemental Forecasting Method

本文是金融專業的paper范例，題目是“Overview of the Judgemental Forecasting Method（判斷預測方法概述）”，預測是許多不同行業的重要工具，因為它通過查看歷史數據、當前數據和分析趨勢來預測未來。然而，一些業務預測并沒有在一個良好的水平上完成，因為一些業務人員將其與目標和計劃混淆。預測，目標和規劃，這三種方法有很大的不同。預測是通過使用歷史數據，當前數據和趨勢分析，盡可能地計算未來的具體情況。業務目標是指業務希望在不久的將來發生。目標通常是在缺乏任何計劃或預測的情況下完成的，因為企業看著他們的競爭對手，他們要么想在市場上趕上他們，要么想超過他們。計劃是查看預測和目標，并決定使業務預測符合其目標的最佳行動。隨著商業世界越來越多地轉向數據分析，預測現在是、將來也將是管理團隊決策的重要組成部分，因為預測可以幫助進行短期、中期和長期預測。

Forecasting is a significant tool for many different sectors as it makes predictions on the future by looking at historical data, present data and the analysing of trends. However, some business forecasting is not done at a good level, as some business people confuse it with goals and planning. Forecasting, Goals and Planning, these three differ significantly, Forecasting is trying to calculate the future a specific as possible, by using historical data, present data and the analysing of trend, Goals for business is that the business would like to happen for them in the near future. Goals are usually done with lacking any planning or forecasting, as the business looks at their competitors and they either want to match them or exceed them in the market. Planning is looking at the forecasting and goals and deciding the best action that will make the business forecasting match their goals. As the business world is moving more into analysing data, forecasting is and will be a vital part of decision-making for the management team, as the forecasting can help with short term, medium term and long term forecasting.

When a business has a lack of past data or the business is launching a new product, the business can still use forecasting, and they will use Judgement forecasting. Judgement forecasting is the use of opinion, intuitive judgment and subjective probability estimates. Judgment forecasting has few methods that can be used to get the best statistical analysis and there are Statistical surveys, Scenario building, Delphi methods, Technology forecasting and forecast by analogy.

當一個企業缺乏過去的數據或該企業正在推出一個新產品時，該企業仍然可以使用預測，他們將使用判斷預測。判斷預測是利用意見、直覺判斷和主觀概率估計。判斷預測的方法很少，可以得到最好的統計分析，有統計調查、情景構建、德爾菲法、技術預測和類比預測。

The Judgement forecasting has increasingly been recognised as a science, and over the years the quality of Judgement forecasting has been improving as the approach has been well structured and efficient. But it is important to understand that Judgement forecasting has not been perfected as it still has limitations. Judgment forecast depend on human cognition which has limitations, “For example, a limited memory may render recent events more important than they actually are and may ignore momentous events from the more distant past; or a limited attention span may result in important information being missed, or a misunderstanding of causal relationships may lead to erroneous inference.”1 This example shows that human memory can affect the judgment forecast in a negative way, and misunderstanding can lead to wishful thinking or optimistic view which can lead to faulty forecast, and in the case of launching a new product, the marketing and salesman teams will have an optimistic view for their lunch so they will not forecast its failure. Beware of the enthusiasm of your marketing and sales colleagues 2.

In the case of judgment forecasting without any domain knowledge and only a set of time series data is used, getting a forecast will be very hard, as in the Hogath and makridais (1981) in their paper, where they have examined around 175 papers where there was judgment forecasting, they have approached a result of that “quantitative models outperform judgmental forecasts”3, in their research they have seen that judgment has been linked with systematic biases and errors, as some people were looking for patterns and linking together clues where there was none as the process was random.

在沒有任何領域知識和只使用一組時間序列數據的判斷預測的情況下，獲得預測將是非常困難的，就像Hogath和makridais(1981)在他們的dissertation中，他們檢查了大約175篇有判斷預測的dissertation，他們得出了“定量模型優于判斷預測”的結論3，在他們的研究中，他們發現判斷與系統性偏差和錯誤有關，因為有些人在尋找模式，并將沒有的線索聯系起來，因為這個過程是隨機的。

Judgment forecasting has been compared to many different kinds of forecasting such as statistical methods, and many different types of research conclude different findings of the accuracy of the two methods. In the paper of Lawrence (1985) and (1986) where the paper compares the accuracy of quantities model and judgment forecasting, the paper has come to a conclusion that demonstrated judgmental forecasting to be at least as accurate as statistical techniques”4, also in the paper show that the standard deviation of the error of the statistical method was greater than the judgment forecast error. The paper also shows that if judgment forecasting was added in the statistical method, better sets of forecasting can be predicted and the level of error would decrease. In the study by Makridakis S and Winkler R (1983) it shows that there are few ways to combine the judgement and statistical forecasting. In the study it says that there is two way to join the two forecasting methods, the first is “Concurrent Incorporation” where to get the final forecasting both methods will have to be used to get the averaging procedure. The second way is a “Posterior Incorporation” “which includes the judgmental revision of statistically derived forecasts”5 Acirc;  this second way tries to improve forecasting by allowing the judgement forecasting to see and access the results of the statistical forecasting.

判斷預測與統計方法等多種不同的預測方法進行了比較，許多不同類型的研究得出了不同的結論，對兩種方法的準確性。在Lawrence(1985)和(1986)的dissertation中，dissertation比較了數量模型和判斷預測的準確性，dissertation得出了一個結論，證明判斷預測至少與統計技術一樣準確。同時也表明，統計方法的標準差誤差大于判斷預測誤差。同時表明，在統計方法中加入判斷預測，可以預測出較好的預測集，降低誤差水平。Makridakis S和Winkler R(1983)的研究表明，將判斷與統計預測相結合的方法很少。在研究中，它說有兩種方法來加入兩種預測方法，第一種是“并發合并”，在得到最終的預測，兩種方法將必須使用得到平均程序。第二種方法是“后驗合并”，包括統計推導預測的判斷修正“5 Acirc;第二種方法試圖通過允許判斷預測查看和訪問統計預測的結果來改進預測。

After many years of research in the area of forecasting, Judgment forecasting improves when greater domain knowledge and more up to date information included, therefore by using this new information, judgment approach can then be adjusted and producing an improved forecast. By using a well structured and systematic approach, it helps to decrease the undesirable effects of the limitations of the forecast. By well structuring the approach it will make the forecasting task clear, and a good understanding of the structure is important to avoid unclear and uncertain terms. The method that is well structured that can be used for the judgment forecasting is the Delphi methods. The Delphi method is the use of experts’ opinions and judgment in the specific field to predict the expectation in that field. The Delphi method is respect method as it only looks at the opinions of the experts in their field and allows them to be anonymous at all time, therefore there is not influenced by their social and political pressure in their prediction, and all experts opinions are weighted equally so no one prediction is superior to another. But like any other approach, the Delphi method also has its limitations, the method is time-consuming, therefore, the experts might be discouraged to join the study or they will not contribute fully at all time of the approach.

經過多年的預測領域的研究，當更多的領域知識和更多的最新信息被包含進來時，判斷預測會得到改進，因此通過使用這些新的信息，判斷方法可以被調整并產生一個改進的預測。通過使用結構良好和系統的方法，它有助于減少預測局限性的不良影響。通過很好地構建方法，它將使預測任務清晰，并且很好地理解結構是重要的，以避免不明確和不確定的術語。德爾菲法是一種結構化較好、可用于判斷預測的方法。德爾菲法是利用專家在特定領域的意見和判斷來預測該領域的期望。德爾菲法是尊重的方法,因為它只看他們的領域的專家的意見,允許他們是匿名的,因此沒有受到他們的社會和政治壓力預測,和所有專家的意見是加權平均所以沒有人預測優于另一種。但是像任何其他方法一樣，德爾菲方法也有它的局限性，這種方法是耗時的，因此，專家可能不被鼓勵加入研究，或者他們不會在任何時候都貢獻充分的方法。

Adding domain knowledge to the judgement forecasting can be used fully for the prediction. The knowledge of the time series and further information which explains the historical performance of the series can have a minor influence on the forecast or a huge impact on the variable of the data. The domain knowledge represents the un-modelled module of the series. The un-modelled module is very important as it can be included into the statistical forecast to reach better results for the forecast. Many studies have been looking at judgement forecasting with the addition of domain knowledge, a study by Brown (1996) which looked at earning per share forecasting. The study shows that the forecasting of the management team was more accurate than the analysts’ predictions and the statistical model forecasting. In the study, it shows that the inside information which is the domain knowledge of the firm lead to the accuracy of the management team forecast. In the study, it showed that it did not matter if the statistical model was complex or simple as the management team and analysts got a higher accuracy level because of the domain knowledge the management team holds.

在判斷預測中加入領域知識，可以充分利用領域知識進行預測。時間序列的知識和解釋該序列歷史表現的進一步信息可能對預測有較小的影響，也可能對數據的變量有很大的影響。領域知識代表了該系列的未建模模塊。未建模模塊非常重要，因為它可以被納入統計預測，以達到更好的預測結果。許多研究都在考慮加上領域知識的判斷預測，Brown(1996)的一項研究關注每股收益預測。研究表明，管理團隊的預測比分析師的預測和統計模型的預測更準確。研究表明，內部信息是企業的領域知識，有助于提高管理層預測的準確性。在研究中，它表明，不管統計模型是復雜還是簡單，因為管理團隊和分析師得到更高的準確性水平，因為管理團隊擁有領域知識。

In a study by Sanders (1992) where it compared the preference of judgement methods to statistical forecasting, the study compared both methods by the use of an artificial time series. The study looked at 38 business students, the students were thought some different ways of statistical and judgement forecasting and every student had two-time series and past data. The task for the students was to use all the information they had to forecast the next 12 steps ahead. The students were given one week to produce their judgement forecasting, then they were given statistical forecasting of the series, and then they were asked to review their forecast and do any adjustment if needed. The study has used the mean absolute percentage error to assess the forecasting results, and the mean percentage error was applied to calculate the level of bias in the forecast. The results of the study have similar results as the past studies did, as statistical methods outperformed judgment forecasting in all-time series but not the low noise step function. And the more complex the data pattern got the worse the judgement forecast became. The study clearly shows that the statistical methods had better forecasting in the high noise level data, and an increase in noise level has worsened off the judgement forecasting, the study says this is due because as the high noise increases it becomes harder for an individual to detect any kind of patterns. While judgement forecasting didn’t perform well during a high noise, it did significantly well in the low noise function. Looking at the bias in the study, it shows that at a low noise series the judgement revision bias is low in the series, while for a high noise series it increases the bias in the series. The main point of the study by Sanders (1992) is that judgement amendments with statistical methods can have great advantage for a low noise series with a specific data patterns, and it will do better when statistical method are applied blindly to a time series, also at a low noise series the judgement revision bias is low in the series, but in a high noise series the judgment forecasting is not the right approach comparing to a statistical forecasting and in some instances the bias level in the judgement forecasting was greater than the statistical forecasting in a high noise series.

Sanders approach of the judgement forecasting is not overwhelm approved in the forecasting filed, as it has many critics wondering about its efficiency, as the sanders approach for judgment forecasting does not use the experts opinions on the field that is going to be forecasted but uses the opinion and judgment of normal people who may have not have studied the field and have a small knowledge about it, therefore, there judgement would not be the best to use to create a prediction from it.

桑德斯的判斷預測方法并沒有得到預測領域的廣泛認可，因為它的有效性受到了許多批評。作為判斷預測的桑德斯方法不使用專家意見是預測領域,但使用的意見和判斷正常的人沒有研究領域和有一個小知識,因此,判斷不是最好的使用來創建一個預測。

Judgemental forecasting is an important tool in the business today but it has to be used right, as some business people confuse it with goals and planning. When doing a judgmental forecasting the aims and the purpose of the forecasting have to be clear and well structured to get better results. But like any forecasting method, Judgemental forecasting has its limitations and it is up to the person who is performing the forecast to make sure they are at a minimum. To get a better prediction it is important to try and increase the domain knowledge of the series as it has been shown in the Brown (1996) study, as the management team outperformed the statistical analysis due to the inside information of the firm and because they are the experts in that field. Also to improve the judgement forecasting as it has been shown in the Sanders (1992) have found if judgment forecasting is done with a revision of statistical methods, the forecast can be more accurate in a low noise series and with a less level of bias. Judgmental forecasting is not a perfect method to predict the outcome of a specific time series but it is a good point to start.

判斷性預測在今天的商業中是一個重要的工具，但是它必須被正確地使用，因為一些商業人士混淆了它與目標和計劃。在進行判斷性預測時，預測的目標和目的必須明確，并且結構良好，以獲得更好的結果。但是，像任何預測方法一樣，判斷預測也有其局限性，這取決于預測的執行者，以確保他們是最小的。為了更好地預測是很重要的試著增加系列的領域知識,因為它已被證明在布朗(1996)的研究中,管理團隊比統計分析由于公司的內部信息,因為他們是這個領域的專家。也改進了判斷預測，正如Sanders(1992)發現的那樣，如果判斷預測是通過修正統計方法進行的，預測可以在低噪聲序列中更準確，偏差水平更低。判斷預測并不是預測特定時間序列結果的完美方法，但它是一個很好的起點。

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