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Currency Trading

Currency trading (forex or FX) offers investors a way to trade on country or regional fiscal/monetary situations and tendencies. Are there reliable ways to exploit this market? Does it represent a distinct asset class?

SACEVS and SACEMS from a European Perspective

A European subscriber asked about the effect of the dollar-euro exchange rate on the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we each month adjust the gross returns for these strategies for the change in the dollar-euro exchange rate that month. We consider all strategy variations: Best Value and Weighted for SACEVS; and, Top 1, equally weighted (EW) Top 2 and EW Top 3 for SACEVS. We focus on SACEVS Best Value and SACEMS EW Top 3. We consider effects on four gross performance metrics: average monthly return; standard deviation of monthly returns; compound annual growth rate (CAGR); and, maximum drawdown (MaxDD). Using monthly returns for the strategies and monthly changes in the dollar-euro exchange rate since August 2002 for SACEVS and since August 2006 for SACEMS, both through April 2018, we find that: Keep Reading

Diversify with Crypto-assets?

Should investors consider adding crypto-assets to portfolios of traditional assets? In their April 2018 paper entitled “Cryptocurrencies as an Asset Class?”, Sinan Krueckeberg and Peter Scholz investigate whether cryptocurrencies (crypto-assets) qualify as a distinct asset class, attractively diversifying portfolios of traditional asset classes. They distinguish between cryptographic coins (with their own blockchains) and tokens (using third party blockchains). They focus on the 10 coins and tokens with the largest market capitalizations as of December 8, 2017 that have at least three months of prices. Their investigation involves:

  • Computing three types of pairwise correlations between crypto-assets and between crypto-assets and traditional assets to measure crypto-asset uniqueness.
  • Comparing trading volumes and ratios of trading volume to market capitalization to those for several large stocks to assess liquidity.
  • Measuring frequencies of crypto-asset market circuit breaker trips and limit up/down triggers to assess stability.
  • Adding crypto-assets to traditional portfolios with quarterly rebalancing to test impact on Sharpe ratio with ex-post (perfect foresight) optimization and three approaches to ex-ante allocation (the simplest a fixed 1% allocation to crypto-assets). Traditional portfolios consist of stocks and bonds, plus (progressively) real estate, gold and oil.

Using daily price data for the top 10 coins/tokens and for traditional asset class proxies, and tick-by-tick crypto-asset price data to assess stability, during late April 2013 through early November 2017, they find that:

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Interplay of the Dollar, Gold and Oil

What is the interplay among investable proxies for the U.S. dollar, gold and crude oil? Do changes in the value of the dollar lead those in hard assets? To investigate, we relate the return series of three exchange-traded funds: (1) the futures-based PowerShares DB US Dollar Index Bullish (UUP); (2) the spot-based SPDR Gold Shares (GLD); and, (3) the spot-based United States Oil (USO). Using monthly, weekly and daily prices for these funds during March 2007 (limited by inception of UUP) through April 2018 (134 months), we find that: Keep Reading

Valuation of Crypto-assets

Is there a way to predict the value of a crypto-asset like Bitcoin? In their March 2018 paper entitled “An Equilibrium Valuation of Bitcoin and Decentralized Network Assets”, Emiliano Pagnotta and Andrea Buraschi model the value of Bitcoin and similar blockchain network tokens via a model that characterizes:

  • Demand, described by current/future number of users and their strength of preference for privacy (trustworthiness), reflecting the economic strength of the network and its tokens.
  • Supply, described by number of miners in the associated proof-of-work competition and cost of mining, reflecting network trustworthiness.

They consider a quantitative version of the model calibrated to the properties of the Bitcoin network at the end of 2017. Based on mathematical derivation/interpretation and the properties of Bitcoin at the end of 2017 (price $14,200), they find that:

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Commodity, Equity Index and Currency Popular Pairs Trading

Are technical rules applied to pairs trading attractive after correcting for data snooping bias? In their March 2018 paper entitled “Pairs Trading, Technical Analysis and Data Snooping: Mean Reversion vs Momentum”, Ioannis Psaradellis, Jason Laws, Athanasios Pantelous and Georgios Sermpinis test a variety of technical trading rules for long-short trading of 15 commodity futures, equity indexes and currency pairs (all versus the U.S. dollar) frequently used on trading websites or offered by financial market firms. Specifically, they test 18,412 trend-following/momentum and contrarian/mean-reversion rules often applied by traders to past daily pair return spreads. They consider average excess (relative to short-term interest rate) return and Sharpe ratio as key metrics for rule selection and performance measurement. They use False Discovery Rate (FDR) to control for data snooping bias, such that 90% of the equally weighted best rules in FDR-corrected portfolios significantly outperform the benchmark. Most tests are in-sample. To test robustness of findings, they: (1) account for one-way trading frictions ranging from 0.02% to 0.05% across assets; (2) consider five subperiods to test consistency over time; and, (3) perform out-of-sample tests using the first part of each subperiod to select the best rules and roughly the last year to measure performance of these rules out-of-sample. Using daily prices of specified assets and daily short-term interest rates for selected currencies during January 1990 (except ethanol starts late March 2006) through mid-December 2016, they find that:

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Crypto-asset Research Survey

What is the body of academic research on crypto-assets? In their March 2018 paper entitled “Cryptocurrencies as a Financial Asset: A Systematic Analysis”, Shaen Corbet, Brian Lucey, Andrew Urquhart and Larisa Yarovaya review available research on cryptocurrencies as financial assets. They define crypto-assets as peer-to-peer electronic transaction systems which allow payment by one party directly to another without an intermediary. Such assets are therefore infinitely divisible and have no physical representation or association with higher authority. Theses assets derive value from the security of an algorithm that records all transactions. The authors segment research into five areas: (1) bubble dynamics, (2) regulation, (3) cybercriminality, (4) diversification, and (5) market efficiency. Based on 87 papers published during 2013 through early 2018 (accelerating in frequency), they conclude that: Keep Reading

Bitcoin, Sustainable or Transitional?

Does Bitcoin have a bright future, or is it only a transitional proof of concept? In their March 2018 paper entitled “Bitcoin: A Revolution?”, Guillaume Haeringer and Hanna Halaburda review incentive mechanisms that make Bitcoin work and discuss current and potential uses of Bitcoin and related technologies. They view Bitcoin as the first successful digital medium of exchange without a trusted third party based on combining cryptographic tools and incentive systems that prevent double-spending. They define a Bitcoin user as any entity holding or receiving Bitcoins and a Bitcoin miner as any entity recording and validating transactions. From the perspective of users, the Bitcoin system is similar to an online banking system that supports only deposits and transfers. From the perspective of miners, the Bitcoin system is a source of rewards from adding new blocks to the blockchain (the only source of new Bitcoins) and from transaction validation fees within their blocks. Based on the body of information about Bitcoin, they conclude that: Keep Reading

Cryptocurrency Primer

How do cryptocurrencies work, and how can investors acquire and hold them? In their January 2018 paper entitled “Crypto-Assets Unencrypted”, Seoyoung Kim, Atulya Sarin and Daljeet Virdi survey cryptocurrency history and technology. They summarize cryptocurrency market sizes, trading volumes and volatilities, with comparisons to major fiat currencies and commodities. They further discuss crypto-asset valuation, regulation and the mechanics of investing in them. Based on cryptocurrency data for January 2013 through early December 2017, with focus on the 20 largest cryptocurrencies, they find that: Keep Reading

Timing Bitcoin with SMAs

Are simple moving averages (SMA) useful for timing difficult-to-value Bitcoin? In their January 2018 paper entitled “Bitcoin: Predictability and Profitability Via Technical Analysis”, Andrew Detzel, Hong Liu, Jack Strauss, Guofu Zhou and Yingzi Zhu investigate the use of 5-day, 10-day, 20-day, 50-day or 100-day SMAs to predict Bitcoin returns. Specifically, they test a trading strategy that holds Bitcoins (cash) when current Bitcoin price is above (below) a selected SMA. They assume cash earns the U.S. Treasury bill (T-bill) yield. Using daily Bitcoin prices and T-bill yield, along with data for other variables/assets for comparison, during July 18, 2010 through December 12, 2017, they find that: Keep Reading

Bitcoin Return Based on Supply and Demand Model

Does the increase in number of Bitcoin wallets at a rate that far exceeds growth in number of Bitcoins explain the dramatic rise in Bitcoin price? In the December revision of his paper entitled “Metcalfe’s Law as a Model for Bitcoin’s Value”, Timothy Peterson models Bitcoin price according to Metcalfe’ Law, which posits that the value of a network (Bitcoin) is a function of the number of possible pair connections (among Bitcoin wallets, assuming all are equal) and is therefore proportional to the square of the number of participants. Said differently, he models Bitcoin value based on supply (number of Bitcoins) and demand (number of Bitcoin wallets). Per Metcalfe’s Law, Bitcoin return is proportional to twice the growth rate of Bitcoin wallets. He tests the model via a least squares regression of actual Bitcoin price on modeled price with adjustment for inflation due to new Bitcoin creation. He applies the model to investigate claims of Bitcoin price manipulation during 2013-2014. Using number of Bitcoins and number of Bitcoin wallets at 60-day intervals during December 31, 2011 through September 30, 2017, he finds that:

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