Currency Trading

What are the return behaviors of non-fungible tokens (NFT), which employ blockchain technology to convey ownership of unique digital or physical items? In their March 2022 paper entitled “The Economics of Non-Fungible Tokens”, Nicola Borri, Yukun Liu and Aleh Tsyvinski assemble a comprehensive dataset of NFT transactions (including digital art/media and objects related to virtual worlds) and create NFT overall market and sector indexes based on a repeat sales method. They then test:

• NFT market exposure to cryptocurrency market, size, value, momentum and attention factors.
• NFT market exposure to traditional equity, commodity and currency market factors.
• NFT market return predictability based on NFT market volatility, index-to-transaction valuation ratio, volume, momentum and attention factors.
• Individual NFT return predictability based on size and momentum/reversal.

Using blockchain-validated weekly data from major NFT exchanges during January 2018 through December 2021, encompassing about 1.3 million repeat sales, they find that: Keep Reading

Can machine learning usefully inform daily crypto-asset trading? In their August 2021 paper entitled “Boosting Cryptocurrency Return Prediction”, Ilias Filippou, David Rapach and Christoffer Thimsen apply the XGBoost algorithm to decision trees to generate next-day forecasts of bitcoin and Ethereum excess returns. They consider 39 potential predictor inputs, including: valuation ratios (network value-to-transactions, addresses-to-network value and fee-to-price); return volatilities over the past one, two or three months; deviations of prices from moving averages for four lookback intervals; cumulative excess returns (momentum) for three lookback intervals; and, sentiment indicators based on Google Trends searches, Reddit comments or Factiva articles. They further explore the relative importance of individual predictors. Their benchmark predictors are inception-to-date mean past returns. Using daily prices from CoinMetrics for bitcoin since since mid-July 2010 and for Ethereum since early August 2015, both through January 2021, along with the contemporaneous risk-free rate and data needed to calculate non-price input predictors, they find that:

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Does over $1 trillion in crypto-assets (having no fundamental value and paying no interest, but trading freely) now held by Americans represent new wealth? In his brief June 2021 paper entitled “Bubble Wealth”, Bradford Cornell addresses the apparent paradox between the view of economic theory (no new wealth) and the fact that those holding crypto-assets can use them for consumption (yes new wealth). He defines “bubble wealth” as the difference between wealth convertible to future consumption (real assets) and perceived wealth (not founded on real assets). Using a simple numerical example involving bubble wealth creation and destruction, he concludes that: Keep Reading

Are there plausible ways to forecast the price of bitcoin? In their March 2021 paper entitled “Bitcoin Price Forecast Using Quantitative Models”, Daniele Bernardi and Ruggero Bertelli examine fundamental bitcoin value from four perspectives:

1. Stock-to-Flow modeling that addresses evolving scarcity based on quantity of bitcoin already present in the world (Stock) and quantity of bitcoin extracted each year (Flow).
2. How bitcoin price bubbles relate to the halving rule (planned 50% reductions in the reward for successfully mining bitcoins).
3. Demand modeling based on the rate of bitcoin adoption.
4. Supply modeling based on costs and revenues of bitcoin production, emphasizing the role of hash rate (evolving system security).

Based on Bitcoin design and bitcoin price data spanning 2009 through 2020, they conclude that: Keep Reading

Speculator level of interest (attention) is plausibly key to bitcoin price behavior. Does the level of online searches for “bitcoin” as a proxy for attention usefully predict bitcoin return? To investigate, we examine interactions between monthly worldwide search intensity for “bitcoin” as measured by Google Trends to represent speculator attention and monthly bitcoin returns. Using monthly Google Trends data starting September 2014 (to coincide with inception of source price tracking) as retrieved on 9/6/2021 and end-of-month bitcoin prices during September 2014 through August 2021, we find that: Keep Reading

Does bitcoin (BTC) price predictably exhibit momentum or reversion? To investigate, we try three tests:

1. Calculate autocorrelations (serial correlations) between daily, weekly and monthly (4-week) BTC returns and BTC returns for the next five respective intervals (for example, correlation of daily return with returns the next five days). Positive and negative correlations suggest momentum and reversion, respectively.
2. Calculate correlations between next-week BTC return and current BTC price relative to its high or low over the last 13 weeks. A positive correlation for closeness to the recent high (low) suggests momentum (reversion).
3. Calculate average next-week BTC returns by ranked fifth (quintile) of BTC price relative to its high or low over the last 13 weeks.

Using daily, weekly and monthly (4-week) BTC closing prices during September 14, 2014 (the earliest available from the source) through August 31, 2021, we find that:

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Is Bitcoin beginning to displace gold as a U.S. dollar hedge? To investigate, we look at rolling correlations of returns for the following pairs of exchange-traded funds (ETF):

1. Grayscale Bitcoin Trust (GBTC) and SPDR Gold Shares (GLD). This relationship should perhaps trend negative if investors are shifting from gold to Bitcoin.
2. GBTC and Invesco DB US Dollar Index Bullish Fund (UUP). This relationship should perhaps trend negative if investors are hedging currency weakness with Bitcoin.
3. GLD and UUP. This relationship should perhaps trend less negative if investors are shifting away from gold as a currency hedge.

Using daily and monthly adjusted prices for these three ETFs during May 2015 (limited by GBTC) through mid-August 2021, we find that: Keep Reading

The National Bank Act of 1863 created a national currency backed by U.S. Treasury bonds and curtailed the era of Free Banking (wildcat private currencies), which resembles the current environment of stablecoins (such as Tether and Diem). Subsequent legislation taxed private currencies out of existence. Does this history offer lessons for the future of stablecoins? In their July 2021 paper entitled “Taming Wildcat Stablecoins”, Gary Gorton and Jeffery Zhang explore the feasibility of stablecoins as a medium of exchange, with focus on acceptance at par and susceptibility to runs. They also present proposals to address  systemic stablecoin risks. Based on lessons from history, they conclude that: Keep Reading

What Decentralized Finance (DeFi) issues may dampen associated interest in crypto-assets by undermining its promises of lower costs and risks compared to traditional, centralized financial intermediaries? In their June 2021 book chapter entitled “DeFi Protocol Risks: the Paradox of DeFi”, Nic Carter and Linda Jeng discuss five sources of DeFi risk:

1. Interconnections with the traditional financial system.
2. Blockchain-related operational issues.
3. Smart contract vulnerabilities.
4. Other governance and regulatory concerns.
5. Scalability challenges.

A general objective of DeFi is automating rules for behavior in a publicly available financial system, eliminating human discretion from financial transactions/contracts. In practice, however, core DeFi protocols retain some human oversight to address unpredictable problems as they emerge, but such retention allows incompetent or malicious governance, administration and validation (see the figure below). Based on review of the body of research and opinion, they conclude that:

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How uncertain are investors about cryptocurrencies, and what drives their collective uncertainty? In their March 2021 paper entitled “The Cryptocurrency Uncertainty Index”, Brian Lucey, Samuel Vigne, Larisa Yarovaya and Yizhi Wang present a Cryptocurrency Uncertainty Index (UCRY) based on news coverage, with two components defined as follows:

1. UCRY Policy -weekly rate of cryptocurrency policy uncertainty news minus average weekly observed rate, divided by standard deviation of weekly observed rate, plus 100.
2. UCRY Price – weekly rate of cryptocurrency price uncertainty news minus average weekly observed rate, divided by standard deviation of weekly observed rate, plus 100.

They distinguish between these two types of cryptocurrency uncertainty to understand differences in behaviors between informed (policy-sensitive) and amateur (price-sensitive) investors. Using 726.9 million relevant date/time-stamped news stories during December 2013 through February 2021, they find that: Keep Reading