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In Search Of Cryptocurrency Failure, Donglian Ma, Jun Tu, Zhaobo Zhu
In Search Of Cryptocurrency Failure, Donglian Ma, Jun Tu, Zhaobo Zhu
Research Collection Lee Kong Chian School Of Business
This paper explores the determinants of cryptocurrency failure and the pricing of crypto failure risk. We document different significant market- and characteristic-based predictors for coin and token failures. The introduction of Bitcoin futures and the outbreak of COVID19 affect the importance of many predictors. Investors require extra return for bearing high failure risk of crypto assets. The return difference across high and low failure risk crypto assets is not explained by the market, size and momentum factors in the cryptocurrency market. Finally, investors benefit from diversifying into high failure risk crypto assets that is little correlated with the stock market.
The Economics Of Financial Scams: Evidence From Initial Coin Offerings, Kenny Phua, Bo Sang, Chi Shen Wei, Yang Yu
The Economics Of Financial Scams: Evidence From Initial Coin Offerings, Kenny Phua, Bo Sang, Chi Shen Wei, Yang Yu
Research Collection Lee Kong Chian School Of Business
We examine the economics of financial scams by analyzing the market for initial coin offerings (ICOs). Using data snapshots of 5,873 ICOs, we find that irregularities in ICO characteristics across listing websites predict higher scam risk and are likely intentional. These patterns are consistent with a model where malicious issuers maximize profits by using irregularities to screen for naïve investors. Almost half of the ICOs in our sample may be scams, amounting to more than U.S. $6 billion in losses. Our results draw attention to the frequent use of screening mechanisms in financial scams.
The Financialization Of Cryptocurrencies, Lei Huang, Tse-Chun Lin, Fangzhou Lu, Jian Sun
The Financialization Of Cryptocurrencies, Lei Huang, Tse-Chun Lin, Fangzhou Lu, Jian Sun
Research Collection Lee Kong Chian School Of Business
We show that change in Grayscale Bitcoin Trust premium is the single most significant predictor of Bitcoin daily return. This sentiment measure is similar to the closed-end fund discount measure as in Baker and Wurgler (2006), but more likely to reflect the excess demand from traditional investors than from blockchain specialists. Although there is a substantial variation in Bitcoin price quotes worldwide, this Grayscale premium and discount predict Bitcoin daily return for the most liquid Bitcoin exchanges. Using K-means clustering and LDA analysis, we find that this predictability is especially significant when there is a large variation in bullish and …