School of Business and Management Department of Information Systems, Business Statistics and Operations Management 193 On Identifying Web3 Business Opportunities and Challenges Supervisor: LEE Dongwon / ISOM Student: KUAN Ho Man / SBM Course: UROP1100, Summer Non-fungible tokens (NFT) are indisputably one of the most absurd asset classes that break out in 2021-2022. In short, NFT is a form of digital asset leveraging the properties of smart contracts and blockchain, allowing them to distinguish themselves from each other, therefore non-fungible. NFTs can be tied to physical assets as a certification of ownership, yet it is not hard to observe that in the past year, the increasing activeness of the market does not have much to do with physical assets but with virtual arts and game-related collectibles, things that traditionally don’t hold much tangible value. In this paper, I hope to provide and analyze different events that occurred in the past few years and provide various metrics that can help evaluate how and whether these treatments affect the nature of different NFT projects and Web3 infrastructures. On Identifying Web3 Business Opportunities and Challenges Supervisor: LEE Dongwon / ISOM Student: ZHOU Xiaoyu / RMBI Course: UROP1000, Summer With the development of digital assets, the realistic importance of digital asset pricing on financial reporting, taxation, and investment considerations has grown with time. In this paper, we identify the current digital asset pricing models and compare them with traditional asset pricing models and startup valuation models. We also discuss the factors driving the price of digital assets and the dynamic valuation factors yet to be considered in pricing models. There is a migration of the idea of traditional valuation methods on digital asset pricing based on the security or commodity nature of the assets, as well as completely new methods or factors revealing the value of digital assets with utility nature. Apart from the traditionally considered factors, there are also digital-related factors that affect digital asset pricing and crypto factors to consider in valuation. Deep Learning NLP in finance Supervisor: YANG Yi / ISOM Student: HU Chenxi / COGBM Course: UROP1100, Summer Sentiment analysis has been widely adopted in finance fields as it facilitates price predictions and risk management in quantitative measures. The introduction of deep learning models such as BERT has also increased the accuracy thus boosting the performance in various tasks such as stock price predictions. However, deep learning models inherently suffered from data shifts and spurious correlations, which are prevalent in the stock market. In this paper, we present how spurious correlations and data shifts hinder the performance of the deep learning models on sentiment analysis in stock markets and demonstrate how we may mitigate this.
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