stats201-PS2-Exlanation-Prediction-Xintong
The Daily Volume of OpenSea platform: Security Vulnerabilities in the NFT Marketplace
Project information
- Author: Xintong Wu, Computation and Design / digital media, Class of 2025, Duke Kunshan University
- Instructor: Prof. Luyao Zhang, Duke Kunshan University
- Disclaimer: Submissions to the Problem Set 2 for STATS201 Introduction to Machine Learning for Social Science, 2023 Spring Term (Seven Week - First) instructed by Prof. Luyao Zhang at Duke Kunshan University.
- Acknowledgments: I would like to express my sincere gratitude to Prof. Luyao Zhang for her guidance in machine learning methods for data interpretation and prediction. Prof. Luyao Zhang provided constructive comments on the feasibility of my research questions and data collection, which prompted me to continuously improve and enhance.
- Project Summary: In 2022, the global NFT market was dismal and a bear market in the crypto market arrived. In June, the global market traded for only $1.04 billion, down 74% MoM from the $4 billion traded in May, marking the largest drop in global NFT trading. (“Marketplaces Archives” 2023). On June 1, Nate Chastain, a former product manager at the world’s largest NFT trading platform, OpenSea, was arrested by U.S. law enforcement authorities on charges of wire fraud and money laundering related to insider trading in NFT. (U.S. Attorney’s Office 2022). This significant event reflects the widespread security vulnerabilities in the NFT trading market. The impact of NFT security events on the daily trading volume of Ethereum is the focus of this study. This study processes and analyzes the daily trading volume (in Ethereum) on OpenSea 2022 on Dune Analytic through a machine learning method of prediction. (“OpenSea Daily Volume (Ethereum) in ETH” 2023). The study found that the security breach hit the platform’s trading volume, leading to a market downturn for at least the next two months. The research highlights the significant impact of long-standing security breaches on industry growth and contributes to the analysis of NFT’s market direction. It also provides future directions for improvement and optimization in the Blockchain and crypto-sphere market. The criteria for NFT security attributes should be more accurately defined in the future.
Table of Contents
Data
Prediction Data Source: https://dune.com/queries/2004005
Meta Data Infomation
| Data Files | Data Type | Data Content |
|---|---|---|
| literature.csv | Queried | 15 “NFT Challenge” journal’s topic and abstract |
| title_bigram.csv | Processed | 15 “NFT Challenge” journal’s title bigram |
| abstract_bigram.csv | Processed | 15 “NFT Challenge” journal’s abstract bigram |
| queried_data.csv | Queried | 2022 OpenSea Daily Volume (Ethereum) in ETH |
| Regression_Train.csv | Processed | Training data for regression |
| Regression_Test.csv | Processed | Testing data for regression |
Data Dictionary
| File Name | Variable Name | Description | Frecuency | Unit | Type |
|---|---|---|---|---|---|
| literature.csv | Title | titles of 15 journals | None | None | str |
| Abstract | abstract of 15 journals | None | None | str | |
| title_bigram.csv | bigram | title’s bigram | None | None | tuple |
| counts | counts of each bigram | None | None | int | |
| abstract_bigram.csv | bigram | abstract’s bigram | None | None | tuple |
| counts | counts of each bigram | None | None | int | |
| queried_data.csv | Date | 365 day’s in 2022 | daily | a day | datetime |
| Daily Volume | 2022 OpenSea daily volume (Ethereum) in ETH | None | eth | float | |
| Regression_Train.csv | volume | daily volume | block | eth | float |
| volume_past_ma10 | past 10 days’ volume | block | eth | float | |
| Regression_Test.csv | volume | daily volume | block | eth | float |
| volume_past_ma10 | past 10 days’ volume | block | eth | float |
Code
Explaination Code Source: https://github.com/sunshineluyao/design-principle-blockchain
Prediction Code Source: https://github.com/Rising-Stars-by-Sunshine/stats201-tutorial-prediction
| Explaination | Prediction |
|---|---|
| NLP NFT Challenge | Process Data |
| Analyze Data |
Spotlight
Figure No.1. “NFT Challenge” journal topic’s word cloud.
In Google Scholar, by searching for relevant articles with the keywords: “NFT”, and “Challenge”, I got the titles and abstracts of 15 journals. The word cloud was generated by NLP, and we found that “Blockchain”, “Risk”, and “Secure” appeared more frequently. This indicates that the security and privacy issues regarding blockchain are the main factors that cause NFT challenges at present. (insightsoftware 2022).
Figure No.1. data source: literature created by design-principle-blockchain/code explain source: insightsoftware. 2022. “Visualizing Text Analysis Results with Word Clouds.” Insightsoftware. May 4, 2022. https://insightsoftware.com/blog/visualizing-text-analysis-results-with-word-clouds/.
Figure No.2. Linear regression prediction histogram of 2022 OpenSea Daily Volume (Ethereum) in ETH
| This histogram was obtained by collecting data on the daily volume of OpenSea Ethereum in 2022, using the linear regression prediction method in machine learning. The blue part represents the real data and the green part represents the data predicted by linear regression. It can be noticed that the predicted data in the latter part of the horizontal coordinate is larger than the actual data. (“Regression Analysis-ArcGIS Insights | Documentation” 2022). In other words, the forecasted volume based on the first half of 2022 is larger than the actual volume; the actual volume was affected by an event that led to a significant decrease. The June 2022 event affected at least the June, July, and August volumes. |
Figure No.2. data source: OpenSea daily volume (Ethereum) in ETH created by Linear Regression explain source:“Regression Analysis—ArcGIS Insights | Documentation.” 2022. Doc.arcgis.com. January 2022. https://doc.arcgis.com/en/insights/2022.1/analyze/regression-analysis.htm.
Figure No.3. Causal inference research design.
Figure No.3. shows the causal inference design for the impact of security incidents in the NFT market on OpenSea’s daily trading volume. In 2022, the global NFT market was dismal and a bear market in the crypto market arrived. In June, the global market traded for only $1.04 billion, down 74% MoM from the $4 billion traded in May, marking the largest drop in global NFT trading. (“Marketplaces Archives” 2023). On June 1, Nate Chastain, a former product manager at the world’s largest NFT trading platform, OpenSea, was arrested by U.S. law enforcement authorities on charges of wire fraud and money laundering related to insider trading in NFT. (U.S. Attorney’s Office 2022). This significant event reflects the widespread security vulnerabilities in the NFT trading market. This study will use the regression discontinuity algorithm to search “How does the major security incident regarding NFT affect the OpenSea daily volumes on Ethereum?” Then, there are two hypothesises based on the research question. Hypothesis one: A major security incident regarding NFT could negatively impact the OpenSea daily volumes on Ethereum. Hypothesis two: A major security incident regarding NFT could increase the decline in OpenSea daily volumes on Ethereum.
We don’t foresee other confounding factors that could significantly affect OpenSea daily volume on Ethereum at the cutoff date when the major security incident is happened.
Figure No.3. created by Whimsical
References
Data Source
Code Source
- Explaination design-principle-blockchain/code
- Prediction stats201-tutorial-prediction/code
Articles
Former Employee of NFT Marketplace Charged in First Ever Digital Asset Insider Trading Scheme
Literature
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