Bitcoin Gold, Litecoin Silver:An Introduction to Cryptocurrency's Valuation and Trading Strategy

TL;DR

This paper explores the analogy of Bitcoin as gold and Litecoin as silver by analyzing on-chain metrics and developing trading strategies based on the Price-to-Utility ratio, confirming Bitcoin's superior store-of-value.

Contribution

It introduces a novel framework using on-chain metrics and the Price-to-Utility ratio for valuing cryptocurrencies, validating the gold-silver analogy with empirical data.

Findings

Bitcoin shows a stronger store-of-value characteristic than Litecoin.

Trading strategies based on PU ratio are effective for crypto valuation.

Open datasets and code promote transparency and future research.

Abstract

Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including unspent transaction outputs (UTXO), spent transaction outputs (STXO), Weighted Average Lifespan (WAL), CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.