金融paper格式：Overview of the Judgemental Forecasting Method

本文是金融專業(yè)的paper范例，題目是“Overview of the Judgemental Forecasting Method（判斷預(yù)測方法概述）”，預(yù)測是許多不同行業(yè)的重要工具，因?yàn)樗ㄟ^查看歷史數(shù)據(jù)、當(dāng)前數(shù)據(jù)和分析趨勢來預(yù)測未來。然而，一些業(yè)務(wù)預(yù)測并沒有在一個良好的水平上完成，因?yàn)橐恍I(yè)務(wù)人員將其與目標(biāo)和計(jì)劃混淆。預(yù)測，目標(biāo)和規(guī)劃，這三種方法有很大的不同。預(yù)測是通過使用歷史數(shù)據(jù)，當(dāng)前數(shù)據(jù)和趨勢分析，盡可能地計(jì)算未來的具體情況。業(yè)務(wù)目標(biāo)是指業(yè)務(wù)希望在不久的將來發(fā)生。目標(biāo)通常是在缺乏任何計(jì)劃或預(yù)測的情況下完成的，因?yàn)槠髽I(yè)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(jì)劃是查看預(yù)測和目標(biāo)，并決定使業(yè)務(wù)預(yù)測符合其目標(biāo)的最佳行動。隨著商業(yè)世界越來越多地轉(zhuǎn)向數(shù)據(jù)分析，預(yù)測現(xiàn)在是、將來也將是管理團(tuán)隊(duì)決策的重要組成部分，因?yàn)轭A(yù)測可以幫助進(jìn)行短期、中期和長期預(yù)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(dāng)一個企業(yè)缺乏過去的數(shù)據(jù)或該企業(yè)正在推出一個新產(chǎn)品時，該企業(yè)仍然可以使用預(yù)測，他們將使用判斷預(yù)測。判斷預(yù)測是利用意見、直覺判斷和主觀概率估計(jì)。判斷預(yù)測的方法很少，可以得到最好的統(tǒng)計(jì)分析，有統(tǒng)計(jì)調(diào)查、情景構(gòu)建、德爾菲法、技術(shù)預(yù)測和類比預(yù)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(lǐng)域知識和只使用一組時間序列數(shù)據(jù)的判斷預(yù)測的情況下，獲得預(yù)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(yù)測的dissertation，他們得出了“定量模型優(yōu)于判斷預(yù)測”的結(jié)論3，在他們的研究中，他們發(fā)現(xiàn)判斷與系統(tǒng)性偏差和錯誤有關(guān)，因?yàn)橛行┤嗽趯ふ夷Ｊ剑]有的線索聯(lián)系起來，因?yàn)檫@個過程是隨機(jī)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(yù)測與統(tǒng)計(jì)方法等多種不同的預(yù)測方法進(jìn)行了比較，許多不同類型的研究得出了不同的結(jié)論，對兩種方法的準(zhǔn)確性。在Lawrence(1985)和(1986)的dissertation中，dissertation比較了數(shù)量模型和判斷預(yù)測的準(zhǔn)確性，dissertation得出了一個結(jié)論，證明判斷預(yù)測至少與統(tǒng)計(jì)技術(shù)一樣準(zhǔn)確。同時也表明，統(tǒng)計(jì)方法的標(biāo)準(zhǔn)差誤差大于判斷預(yù)測誤差。同時表明，在統(tǒng)計(jì)方法中加入判斷預(yù)測，可以預(yù)測出較好的預(yù)測集，降低誤差水平。Makridakis S和Winkler R(1983)的研究表明，將判斷與統(tǒng)計(jì)預(yù)測相結(jié)合的方法很少。在研究中，它說有兩種方法來加入兩種預(yù)測方法，第一種是“并發(fā)合并”，在得到最終的預(yù)測，兩種方法將必須使用得到平均程序。第二種方法是“后驗(yàn)合并”，包括統(tǒng)計(jì)推導(dǎo)預(yù)測的判斷修正“5 Acirc;第二種方法試圖通過允許判斷預(yù)測查看和訪問統(tǒng)計(jì)預(yù)測的結(jié)果來改進(jìn)預(yù)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(jīng)過多年的預(yù)測領(lǐng)域的研究，當(dāng)更多的領(lǐng)域知識和更多的最新信息被包含進(jìn)來時，判斷預(yù)測會得到改進(jìn)，因此通過使用這些新的信息，判斷方法可以被調(diào)整并產(chǎn)生一個改進(jìn)的預(yù)測。通過使用結(jié)構(gòu)良好和系統(tǒng)的方法，它有助于減少預(yù)測局限性的不良影響。通過很好地構(gòu)建方法，它將使預(yù)測任務(wù)清晰，并且很好地理解結(jié)構(gòu)是重要的，以避免不明確和不確定的術(shù)語。德爾菲法是一種結(jié)構(gòu)化較好、可用于判斷預(yù)測的方法。德爾菲法是利用專家在特定領(lǐng)域的意見和判斷來預(yù)測該領(lǐng)域的期望。德爾菲法是尊重的方法,因?yàn)樗豢此麄兊念I(lǐng)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(yù)測,和所有專家的意見是加權(quán)平均所以沒有人預(yù)測優(yōu)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(xiàn)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(yù)測中加入領(lǐng)域知識，可以充分利用領(lǐng)域知識進(jìn)行預(yù)測。時間序列的知識和解釋該序列歷史表現(xiàn)的進(jìn)一步信息可能對預(yù)測有較小的影響，也可能對數(shù)據(jù)的變量有很大的影響。領(lǐng)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?yàn)樗梢员患{入統(tǒng)計(jì)預(yù)測，以達(dá)到更好的預(yù)測結(jié)果。許多研究都在考慮加上領(lǐng)域知識的判斷預(yù)測，Brown(1996)的一項(xiàng)研究關(guān)注每股收益預(yù)測。研究表明，管理團(tuán)隊(duì)的預(yù)測比分析師的預(yù)測和統(tǒng)計(jì)模型的預(yù)測更準(zhǔn)確。研究表明，內(nèi)部信息是企業(yè)的領(lǐng)域知識，有助于提高管理層預(yù)測的準(zhǔn)確性。在研究中，它表明，不管統(tǒng)計(jì)模型是復(fù)雜還是簡單，因?yàn)楣芾韴F(tuán)隊(duì)和分析師得到更高的準(zhǔn)確性水平，因?yàn)楣芾韴F(tuán)隊(duì)擁有領(lǐng)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(yù)測方法并沒有得到預(yù)測領(lǐng)域的廣泛認(rèn)可，因?yàn)樗挠行允艿搅嗽S多批評。作為判斷預(yù)測的桑德斯方法不使用專家意見是預(yù)測領(lǐng)域,但使用的意見和判斷正常的人沒有研究領(lǐng)域和有一個小知識,因此,判斷不是最好的使用來創(chuàng)建一個預(yù)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(yù)測在今天的商業(yè)中是一個重要的工具，但是它必須被正確地使用，因?yàn)橐恍┥虡I(yè)人士混淆了它與目標(biāo)和計(jì)劃。在進(jìn)行判斷性預(yù)測時，預(yù)測的目標(biāo)和目的必須明確，并且結(jié)構(gòu)良好，以獲得更好的結(jié)果。但是，像任何預(yù)測方法一樣，判斷預(yù)測也有其局限性，這取決于預(yù)測的執(zhí)行者，以確保他們是最小的。為了更好地預(yù)測是很重要的試著增加系列的領(lǐng)域知識,因?yàn)樗驯蛔C明在布朗(1996)的研究中,管理團(tuán)隊(duì)比統(tǒng)計(jì)分析由于公司的內(nèi)部信息,因?yàn)樗麄兪沁@個領(lǐng)域的專家。也改進(jìn)了判斷預(yù)測，正如Sanders(1992)發(fā)現(xiàn)的那樣，如果判斷預(yù)測是通過修正統(tǒng)計(jì)方法進(jìn)行的，預(yù)測可以在低噪聲序列中更準(zhǔn)確，偏差水平更低。判斷預(yù)測并不是預(yù)測特定時間序列結(jié)果的完美方法，但它是一個很好的起點(diǎn)。

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