Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2020 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for September 2020 (Final)
1st ETF 2nd ETF 3rd ETF

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?

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:

Keep Reading

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

Predicting Crypto-asset Returns with Past Returns-Volume

Do crypto-asset trading volumes usefully predict returns? In the August 2018 draft of their paper entitled “Trading Volume in Cryptocurrency Markets”, Daniele Bianchi and Alexander Dickerson investigate the power of crypto-asset trading volumes to predict future returns. They calculate volumes and returns based on either 12-hour or 24-hour intervals. They process these inputs as follows:

  • To detect volume abnormalities, they estimate its log deviation from trend over a rolling 21-interval window. To put different crypto-assets on an equal footing, they then standardized by dividing by its log standard deviation over the same window.
  • They measure past returns over the same interval, denominated in bitcoins, (thereby including Bitcoin only indirectly). To emphasize the most liquid exchanges, they weight returns by volume when aggregating.

To assess economic significance of findings, they double-sort crypto-assets first into two to four groups ranked by the return metric and then within each group into three or four subgroups ranked by the volume metric. Using intraday (10-minute) price and volume data for 26 crypto-assets from over 150 exchanges (90% of total crypto-asset market capitalization), each denominated in bitcoins, during January 1, 2017 through May 10, 2018, they find that:

Keep Reading

Crypto-asset Risks and Returns

How do the major crypto-assets (Bitcoin, Ripple, and Ethereum) stack up against conventional asset classes? In their August 2018 paper entitled “Risks and Returns of Cryptocurrency”, Yukun Liu and Aleh Tsyvinski apply standard tools of asset pricing to measure crypto-asset exposures to:

  • 160 equity factors.
  • Macroeconomic factors (non-durable consumption growth, durable consumption growth, industrial production growth, and personal income growth).
  • Major non-U.S. currencies (Australian Dollar, Canadian Dollar, Euro, Singapore Dollar and UK Pound).
  • Precious metals (gold, platinum and silver).

They also investigate potential predictors for cryptocurrency returns analogous to those of traditional asset classes (momentum, investor attention, price-to-“dividend” ratio, realized volatility and supply). Finally, they measure exposures of various industries to crypto-asset returns. Using daily crypto-asset prices for Bitcoin since January 2011 and for Ripple and Ethereum since early August 2013, all through May 2018, along with contemporaneous data for other variables as outlined above, they find that: Keep Reading

Bitcoin a Safe Haven Candidate?

Should investors consider Bitcoin as a safe haven from turbulent financial markets? In their June 2018 paper entitled “Bitcoin as a Safe Haven: Is It Even Worth Considering?”, Lee Smales and Dirk Baur assess the potential for Bitcoin as a safe haven, focusing on considerations beyond its low return correlations with other assets during times of market stress. Their comparison set of assets consists of gold (GLD) and bonds (10-year U.S. Treasury futures) as traditional safe havens, a proxy for the U.S. stock index (SPY) and mature (Apple) and immature (Twitter) individual stocks. They match samples by removing Bitcoin data for weekends and holidays. Using daily returns for Bitcoin and the comparison set of assets during August 2011 through May 2018, they find that: Keep Reading

Big Reward for Risk in Initial Coin Offerings?

Should investors pursue initial coin offerings (ICO), special-purpose crypto-tokens? In their May 2018 paper entitled “Digital Tulips? Returns to Investors in Initial Coin Offerings”, Hugo Benedetti and Leonard Kostovetsky study the market for crypto-tokens, focusing on: initial pricing; returns from buying at ICO and selling at date of listing on an exchange; and, returns from buying at listing date and holding for various fixed intervals. ICOs typically originate with an offeror’s prospectus detailing a goal, plan, team and offering schedule. Interested parties then register for the offering, with execution typically in stages over several months, some restricted to preferred users, angel investors, venture capitalists and/or accredited investors. The authors also employ Twitter accounts of ICO offerors to test the relationship between Twitter activity and price and to measure post-ICO attrition rate of offerors. Using data for 2,390 ICOs completed by May 2018, including offeror Twitter histories as of May 8, 2018, they find that:

Keep Reading

Currency Exchange Style Factors for Incremental Diversification

Do currency exchange factor strategies usefully diversify a set of conventional asset classes? In their May 2018 paper entitled “Currency Management with Style”, Harald Lohre and Martin Kolrep investigate the systematic harvesting of currency exchange carry, value and momentum strategies, specified as follows and applied to the G10 currencies:

  • Carry – buy (sell) the three equally weighted currency forwards with the highest (lowest) short-term interest rates, reformed monthly.
  • Momentum – buy (sell) the three equally weighted currency forwards with the greatest (least) appreciation over the past three months, reformed monthly.
  • Value (long-term reversion) – buy (sell) the three equally weighted currency forwards with the lowest (highest) change in their real exchange rates, based on purchasing power parity, over the past 60 months, reformed monthly.

They examine in-sample (full-sample) mean-variance relationships for these strategies to assess their value as diversifiers of five conventional asset classes (U.S. stocks, commodities, U.S. Treasury bonds, U.S. corporate investment-grade bonds and U.S. corporate high-yield bonds). They also look at potential out-of-sample benefits of these strategies based on information available at the time of each monthly rebalancing as additions to a risk parity portfolio of the five conventional assets from the perspective. For this out-of-sample test, they consider both minimum variance (tail risk hedging) and mean-variance optimization (return seeking) for aggregating the three currency strategies. Using monthly data for the selected assets from the end of January 1999 through December 2016, they find that: Keep Reading

Benefits of Volatility Targeting Across Asset Classes

Does volatility targeting improve Sharpe ratios and provide crash protection across asset classes? In their May 2018 paper entitled “Working Your Tail Off: The Impact of Volatility Targeting”, Campbell Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto Van Hemert examine return and risk effects of long-only volatility targeting, which scales asset and/or portfolio exposure higher (lower) when its recent volatility is low (high). They consider over 60 assets spanning stocks, bonds, credit, commodities and currencies and two multi-asset portfolios (60-40 stocks-bonds and 25-25-25-25 stocks-bonds-credit-commodities). They focus on excess returns (relative to U.S. Treasury bill yield). They forecast volatility using realized daily volatility with exponentially decaying weights of varying half-lives to assess sensitivity to the recency of inputs. For most analyses, they employ daily return data to forecast volatility. For S&P 500 Index and 10-year U.S. Treasury note (T-note) futures, they also test high-frequency (5-minute) returns transformed to daily returns. They scale asset exposure inversely to forecasted volatility known 24 hours in advance, applying a retroactively determined constant that generates 10% annualized actual volatility to facilitate comparison across assets and sample periods. Using daily returns for U.S. stocks and industries since 1927, for U.S. bonds (estimated from yields) since 1962, for a credit index and an array of futures/forwards since 1988, and high-frequency returns for S&P 500 Index and 10-year U.S. Treasury note futures since 1988, all through 2017, they find that:

Keep Reading

Login
Daily Email Updates
Filter Research
  • Research Categories (select one or more)