Determinants of success of fundraising campaign of blockchain project
Promotor(s) : Gautier, Axel
Date of defense : 5-Sep-2022/10-Sep-2022 • Permalink :
|Determinants of success of fundraising campaign of blockchain project
|Date of defense :
|Committee's member(s) :
|Number of pages :
|Business & economic sciences > Macroeconomics & monetary economics
|Target public :
|Professionals of domain
|Université de Liège, Liège, Belgique
|Master en sciences économiques, orientation générale, à finalité spécialisée en macroeconomics and finance
|Master thesis of the HEC-Ecole de gestion de l'Université de Liège
[fr] We start our journey from the invention of the bitcoin and the blockchain by Satoshi Nakamoto, which leads to the creation of Ethereum who enable other developers to create their cryptocurrency by using “smart contracts” on top of the Ethereum blockchain. From the invention of smart contract, the cryptocurrency market went crazy and its capitalization skyrocket until the burst of the bubble in 2018.
From 2018 researchers have tried to understand why some project can attract investor as the project are only idea or at really early stage of development, researcher had to find variable that explain the investment decision from what information is available to the public.
Researcher looked at variable regarding the offering characteristics, the team, the white paper and social media. Our research was aiming to test if assumption during the 2018 bubble still hods in our current context, where ICO shift to IDO and where habits have change for instance new social media such as Discord have emerged but some of the previous research like reddit or GitHub are not used anymore, furthermore we also accounted for sector that were not really delimited back then such as the DeFi, GameFi and the Blockchain project, hence we have accounted for that in our model.
Our model is a multiple regression of 16 variables, we estimate our regressor with an OLS estimation, from the assumption checking we had to make some transformation of our data before having a regression ready for interpretation.
After fitting our data with the right transformation, we found that some variable where in line with the theory and we also found the effect of our new variable on the total amount raised during the public offering.
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