本文關(guān)鍵詞：獎(jiǎng)勵(lì)式眾籌融資績(jī)效的影響因素及預(yù)測(cè)研究　出處：《吉林財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 獎(jiǎng)勵(lì)式眾籌 最優(yōu)尺度模型 SOM算法 預(yù)測(cè)

【摘要】：2009年,第一個(gè)獎(jiǎng)勵(lì)式眾籌平臺(tái)kickstarter在美國(guó)成立。在此之后,獎(jiǎng)勵(lì)式眾籌平臺(tái)迅速在全球范圍內(nèi)發(fā)展,在歐洲和美洲逐漸走向成熟并發(fā)展到了亞洲、中南美洲和非洲等各個(gè)地區(qū)。2011年7月,我們國(guó)家第一個(gè)獎(jiǎng)勵(lì)式眾籌平臺(tái)“點(diǎn)名時(shí)間”成立,這象征著我國(guó)眾籌平臺(tái)方面的開(kāi)始。在接下來(lái)的幾年里,很多不同模式的眾籌平臺(tái)迅速成立。據(jù)鳴金網(wǎng)數(shù)據(jù)中心不完全統(tǒng)計(jì),截止到2015年11月底,被鳴金網(wǎng)收錄的眾籌網(wǎng)站有265個(gè),其中獎(jiǎng)勵(lì)式眾籌網(wǎng)站105個(gè),約占39.6%。在項(xiàng)目的量上,獎(jiǎng)勵(lì)式眾籌的數(shù)目最多。因此,本文利用眾籌網(wǎng)(http://www.zhongchou.com)中的3468個(gè)獎(jiǎng)勵(lì)式眾籌項(xiàng)目對(duì)影響其融資績(jī)效的因素做分析,并且對(duì)獎(jiǎng)勵(lì)式眾籌項(xiàng)目融資績(jī)效進(jìn)行預(yù)測(cè)研究,為我國(guó)獎(jiǎng)勵(lì)式眾籌的發(fā)展提供理論和實(shí)證基礎(chǔ)。本文采用眾籌網(wǎng)平臺(tái)中的交易數(shù)據(jù)對(duì)獎(jiǎng)勵(lì)式眾籌融資績(jī)效的影響因素做分析,并預(yù)測(cè)獎(jiǎng)勵(lì)式眾籌的融資績(jī)效。通過(guò)最優(yōu)尺度模型分析獎(jiǎng)勵(lì)式眾籌的顯著影響因素和非顯著影響因素,探索這些影響因素之所以顯著和非顯著的原因。再運(yùn)用SOM算法將融資績(jī)效離散化,目的是運(yùn)用C5.0決策樹(shù)算法、支持向量機(jī)算法和TAN貝葉斯網(wǎng)絡(luò)算法對(duì)融資績(jī)效進(jìn)行預(yù)測(cè)分析。對(duì)比分析C5.0決策樹(shù)算法、支持向量機(jī)算法和TAN貝葉斯網(wǎng)絡(luò)這三種預(yù)測(cè)方法,得出C5.0決策樹(shù)的預(yù)測(cè)最優(yōu)。研究結(jié)果表明:項(xiàng)目名稱(chēng)長(zhǎng)度、籌到的金額、項(xiàng)目所屬的行業(yè)、項(xiàng)目所在的城市和評(píng)論數(shù)對(duì)融資績(jī)效的影響較大。然而,籌資天數(shù)、發(fā)起人信息、是否有視頻、關(guān)注數(shù)、支持?jǐn)?shù)、項(xiàng)目更新、項(xiàng)目回報(bào)方式和項(xiàng)目回報(bào)時(shí)間是對(duì)籌資績(jī)效的影響較小。C5.0決策樹(shù)算法、支持向量機(jī)算法和TAN貝葉斯網(wǎng)絡(luò)算法,這三種算法中,C5.0決策樹(shù)的預(yù)測(cè)效果最好。另外,對(duì)于除了1類(lèi)績(jī)效以外的預(yù)測(cè),C5.0的預(yù)測(cè)結(jié)果比支持向量機(jī)和TAN貝葉斯網(wǎng)絡(luò)的預(yù)測(cè)準(zhǔn)確度要高�；诶碚摵蛯�(shí)際的研究,對(duì)于項(xiàng)目籌資人,本文建議籌資項(xiàng)目名稱(chēng)命名要合理和有吸引力,目標(biāo)金額要根據(jù)實(shí)際情況來(lái)設(shè)置,籌資人要及時(shí)回復(fù)投資人的評(píng)論和建議,多增加互動(dòng)。對(duì)于項(xiàng)目投資人,要注重中后續(xù)項(xiàng)目的發(fā)展以及獎(jiǎng)勵(lì)式眾籌回報(bào)的方式。對(duì)于獎(jiǎng)勵(lì)式眾籌行業(yè),建議相關(guān)部門(mén)的監(jiān)管需要進(jìn)一步明確思路,該行業(yè)也要逐步走向規(guī)范。獎(jiǎng)勵(lì)式眾籌平臺(tái)需要探索新的模式,獎(jiǎng)勵(lì)式眾籌可以擴(kuò)展到一些新的行業(yè)。
[Abstract]:In 2009, the first reward crowdfunding platform kickstarter was founded in the United States. Since then, the reward crowdfunding platform has developed rapidly around the world. In Europe and America gradually matured and developed to Asia, Central and South America and Africa and other regions. July 2011, our country's first incentive crowdfunding platform "roll call time" was established. This symbolizes the beginning of our crowdfunding platform. In the next few years, many different models of crowdfunding platform were quickly established. There are 265 crowdfunding websites included by Ming Jin. Among them, 105 are reward crowdfunding websites, which account for 39.6. in the number of projects, the number of award crowdfunding is the largest. Therefore. This paper uses 3468 incentive crowdfunding projects in http: / / www.zhongchou.comto analyze the factors that affect its financing performance. And the incentive crowdfunding project financing performance prediction research. To provide theoretical and empirical basis for the development of incentive crowdfunding. This paper uses the transaction data in crowdfunding platform to analyze the factors affecting the performance of incentive crowdfunding. And forecast the financing performance of incentive crowdfunding. Through the optimal scale model to analyze the significant and non-significant factors of incentive crowdfunding. To explore the reasons why these factors are significant and non-significant, and then use the SOM algorithm to discretize the financing performance, the purpose is to use C5.0 decision tree algorithm. Support vector machine (SVM) algorithm and TAN Bayesian network algorithm are used to predict the financing performance. Three forecasting methods, C5.0 decision tree algorithm, support vector machine algorithm and TAN Bayesian network, are compared and analyzed. The results show that the length of the project name, the amount of money raised, the industry the project belongs to, the city where the project is located and the number of comments have great influence on the financing performance. Funding days, sponsor information, whether there is video, attention, support, project update, project return mode and project return time are the smaller impact on funding performance. C5.0 decision tree algorithm. Support vector machine algorithm and TAN Bayesian network algorithm, these three algorithms, C5.0 decision tree prediction effect is the best. In addition, for the prediction of performance other than class 1. The prediction accuracy of C5.0 is higher than that of support vector machine and TAN Bayesian network. This paper suggests that the name of the fund-raising project should be reasonable and attractive, the target amount should be set according to the actual situation, the fundraiser should respond to the comments and suggestions of the investors in time, and increase the interaction. We should pay attention to the development of the follow-up projects and the way of reward crowdfunding. For the reward crowdfunding industry, it is suggested that the supervision of the relevant departments need to further clarify the thinking. The industry also needs to be standardized. The reward crowdfunding platform needs to explore new models, and the reward crowdfunding can be extended to some new industries.
【學(xué)位授予單位】：吉林財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F724.6;F832.39

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