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Value Investing Strategy (Strategy Overview)

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Momentum Investing Strategy (Strategy Overview)

Allocations for February 2023 (Final)
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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?

Are Currency Carry Trade ETFs Working?

Is the currency carry trade, as implemented by exchange-traded funds/notes (ETF/ETN), attractive? To investigate, we consider two currency carry trade ETF/ETNs, one live (with low trading volume) and one dead:

  • PowerShares DB G10 Currency Harvest Fund (DBV) – tracks changes in the Deutsche Bank G10 Currency Future Harvest Index. This index consists of futures contracts on certain G10 currencies with up to 2:1 leverage to exploit the tendency that currencies with relatively high interest rates tend to appreciate relative to currencies with relatively low interest rates, reconstituted annually in November.
  • iPath Optimized Currency Carry (ICITF) – provides exposure to the Barclays Optimized Currency Carry Index, which reflects the total return of a strategy that holds high-yielding G10 currencies financed by borrowing low-yielding G10 currencies. This fund stopped trading July 2018, but an indicative value is still available.

We focus on monthly return statistics, plus compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). For reference (not benchmarking), we compare results to those for SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT). Using monthly total returns for the two currency carry trade products, SPY and TLT as available through October 2020, we find that: Keep Reading

Evolution of Bitcoin as an Investment

How are attitudes toward Bitcoin as an investment evolving? In their October 2020 survey report entitled “Comparing Public Bitcoin Adoption Rates in 2020 vs 2017”, the Tokenist summarizes findings from a survey using the same questions as three of the most widely cited past surveys on Bitcoin adoption from October 2017, July 2018 and April 2019. Respondents to the new survey include 4,111 via an online multiple choice questionnaire and conducted via Google Surveys and 741 via an email campaign to Tokenist readers. Using data from these 4,852 respondents received September-October 2020 and results of the past surveys, they find that:

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Bitcoin Price Repeatedly Manipulated?

Benford’s law states that the probability of the value of the first digit in many naturally occurring samples of numbers, including asset prices, varies inversely with digit magnitude. For example, the number 1 (9) appears as the leading digit about 30% (less than 5%) of the time. For asset prices, deviations from this law typically indicate some kind of fraud. In his September 2020 paper entitled “To the Moon: A History of Bitcoin Price Manipulation”, Timothy Peterson applies Benford’s law to daily bitcoin prices over a long sample and by calendar year to identify price manipulations. He requires that each bin [0 through 9] in the price distribution histogram have at least eight observations. If this condition is not met for the first digit, he relies on the second digit. If not for the first and second digits, he relies on the third digit. To rule out speculative mania as the cause of unusual price activity, he uses daily ratios of price to three fundamental network size and activity metrics: (1) active addresses, (2) non-zero balance addresses and (3) transaction counts. Using daily closing Bitcoin price and activity data during July 2010 through May 2020, he finds that: Keep Reading

Asset Class ETF Interactions with the Yuan

How do different asset classes interact with the Chinese yuan-U.S. dollar exchange rate? To investigate, we consider relationships between WisdomTree Chinese Yuan Strategy (CYB) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for CYB and the asset class proxies during May 2008 (when CYB is first available) through August 2020 (147 months), we find that: Keep Reading

The BGSV Portfolio

How might an investor construct a portfolio of very risky assets? To investigate, we consider:

  • First, diversifying with monthly rebalancing of:
    1. Bitcoin Investment Trust (GBTC), representing a very long-term option on Bitcoins.
    2. VanEck Vectors Junior Gold Miners ETF (GDXJ), representing a very long-term option on gold.
    3. ProShares Short VIX Short-Term Futures (SVXY), to capture part of the U.S. stock market volatility risk premium by shorting short-term S&P 500 Index implied volatility (VIX) futures. SVXY has a change in investment objective at the end of February 2018 (see “Using SVXY to Capture the Volatility Risk Premium”).
  • Second, capturing upside volatility and managing drawdown of this portfolio via gain-skimming to a cash position.

We assume equal initial allocations of $10,000 to each of the three risky assets. We execute a monthly skim as follows: (1) if the risky assets have month-end combined value less than combined initial allocations ($30,000), we rebalance to equal weights for next month; or, (2) if the risky assets have combined month-end value greater than combined initial allocations, we rebalance to initial allocations and move the excess permanently (skim) to cash. We conservatively assume monthly portfolio reformation frictions of 1% of month-end combined value of risky assets. We assume accrued skimmed cash earns the 3-month U.S. Treasury bill (T-bill) yield. Using monthly prices of GBTC, GDXJ and SVXY adjusted for splits and dividends and contemporaneous T-bill yield during May 2015 (limited by GBTC) through June 2019, we find that:

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Asset Class ETF Interactions with the Yen

How do different asset classes interact with the Japanese yen-U.S. dollar exchange rate? To investigate, we consider relationships between Invesco CurrencyShares Japanese Yen (FXY) and the exchange-traded fund (ETF) asset class proxies used in “Simple Asset Class ETF Momentum Strategy” (SACEMS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for FXY and the asset class proxies since March 2007 as available through July 2019, we find that: Keep Reading

Cryptocurrency Factor Model

Do simple factor models help explain future return variations across different cryptocurrencies, as they do for stocks? In their April 2019 paper entitled “Common Risk Factors in Cryptocurrency”, Yukun Liu, Aleh Tsyvinski and Xi Wu examine performances of cryptocurrency (coin) counterparts for 25 price-related and market-related stock market factors, broadly categorized as size, momentum, volume and volatility factors. They first construct a coin market index based on capitalization-weighted returns of all coins in their sample. They then each week sort coins into fifths based on each factor and calculate average excess return for a portfolio that is long (short) coins in the highest (lowest) quintile. Finally, they investigate whether any small group of factors accounts for returns of all significant factors. Using daily prices in U.S. dollars and non-return variables (excluding top and bottom 1% values as potential errors/outliers) for all coins with market capitalizations over $1 million dollars from Coinmarketcap.com during January 2014 through December 2018 (a total of 1,707 coins, growing from 109 in 2014 to 1,583 in 2018), they find that:

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ICO Performance Tendencies

Are Initial Coin Offerings (ICO), also called token sales or token offerings, typically good investments? ICOs are smart contracts on a blockchain (usually Ethereum) that enable firms to raise money directly from investors. The median time for listing a successful ICO on a token exchange is 42 days. In the May 2019 revision of his paper entitled “The Pricing and Performance of Cryptocurrency”, Paul Momtaz examines the performance of ICOs for horizons of one day to three years after initial listing. He also investigates whether there are robust predictors of initial pricing and longer term performance. His sample consists of all tokens tracked by coinmarketcap.com during January 2013 through April 2018, less confirmed errors and outliers in extreme 1% tails because they are unverifiable. His benchmark for calculating abnormal returns is the market capitalization-weighted return of cryptocurrencies (dominated by Bitcoin and Ethereum). Using daily high, low and closing prices, market capitalizations and trading volumes of 1,403 ICOs and daily closes of major cryptocurrencies during the specified period, he finds that: Keep Reading

Number of Users as Bitcoin Price Driver

How should investors assess whether the market is fairly valuing cryptocurrencies such as Bitcoin? In his March 2019 paper entitled “Bitcoin Spreads Like a Virus”, Timothy Peterson offers a way to value Bitcoin based on Metcalf’s Law (network economics) and  a Gompertz function (often used to describe biological activity). The former model estimates fair price based on number of active users, and the latter model estimates the growth rate of active users. Using findings from prior research plus daily Bitcoin price and active account data from coinmetrics.io and blockchain.info during July 2010 through February 2019, he finds that: Keep Reading

Net Speculators Position as Futures Return Predictor

Should investors rely on aggregate positions of speculators (large non-commercial traders) as indicators of expected futures market returns? In their November 2018 paper entitled “Speculative Pressure”, John Hua Fan, Adrian Fernandez-Perez, Ana-Maria Fuertes and Joëlle Miffre investigate speculative pressure (net positions of speculators) as a predictor of futures contract prices across four asset classes (commodity, currency, equity index and interest rates/fixed income) both separately and for a multi-class portfolio. They measure speculative pressure as end-of-month net positions of speculators relative to their average weekly net positions over the past year. Positive (negative) speculative pressure indicates backwardation (contango), with speculators net long (short) and futures prices expected to rise (fall) as maturity approaches. They measure expected returns via portfolios that systematically buy (sell) futures with net positive (negative) speculative pressure. They compare speculative pressure strategy performance to those for momentum (average daily futures return over the past year), value (futures price relative to its price 4.5 to 5.5 years ago) and carry (roll yield, difference in log prices of  nearest and second nearest contracts). Using open interests of large non-commercial traders from CFTC weekly legacy Commitments of Traders (COT) reports for 84 futures contracts series (43 commodities, 11 currencies, 19 equity indexes and 11 interest rates/fixed income) from the end of September 1992 through most of May 2018, along with contemporaneous Friday futures settlement prices, they find that: Keep Reading

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