**3. Data**

Two sets of data were used in our analysis: price data of cryptocurrencies and data capturing network effects in cryptocurrencies. Closing prices of cryptocurrencies were retrieved from CoinMarketCap (coinmarketcap.com) [32] and Coin Metrics (coinmetrics.io). Daily price data denominated in USD were collected from 1 January 2018 to 28 February 2022, and converted into weekly returns (weekly returns were used to mitigate any inconsistency in time for computing daily closing price). The analysis focused on the crypto market since 2018 because cryptocurrency funds reveal distinct characteristics in the post-ICO (initial coin offering) bubble period [33] and the market has generally become more mature following the ICO bubble [34]. Crypto markets starting from 2018 can be distinguished as the post-ICO bubble period [33], and ICOs, along with ICO-related events, such as regulatory bans, are observed to cause sensitivity in the market [35].

Empirical tests were performed with seven different sets of weekly returns that ended in different days of the week (i.e., the first set of weekly returns end every Monday, the second set of weekly returns end every Tuesday, and so on). We focused on the top 40 currencies with the largest market capitalization, which account for over 90% of market capitalization valued in USD of the top 500 cryptocurrencies (as of 27 February 2022). Filtering the cryptocurrencies with price data available since the beginning of 2018 results in 15 currencies: Bitcoin (BTC), Ethereum (ETH), Tether (USDT), BNB, XRP, Cardano (ADA), Dogecoin (DOGE), Litecoin (LTC), Chainlink (LINK), Tron (TRX), Bitcoin Cash (BCH), Decentraland (MANA), Stellar (XLM), Ethereum Classic (ETC), and Filecoin (FIL). Since the crypto market contains many more cryptocurrencies, we also used the CCi30 index in our analysis, which is an index of the top 30 cryptocurrencies (the CCi30 index has been used as a representative index of the crypto market for analyzing liquidity [36], herding behavior [37,38], and dynamics of cryptocurrencies [39]).

Based on the analysis in [20], four factors of network effect in cryptocurrencies were collected: number of active addresses (address), number of transactions (transaction), number of transfers (transfer), and number of unique wallets (wallet). The number of wallets was obtained from Blockchain.com for its users, and the other three factors were obtained from data for Bitcoin from Coin Metrics (coinmetrics.io). Weekly growth of these four metrics were computed to match the weekly return periods of cryptocurrencies (the descriptive statistics of the 15 cryptocurrencies and the four network factors are included in the Appendix A, and further details, such as the effect of days of the week, are presented in [20]).
