New strategies:
#378 – Momentum Factor in Cryptocurrencies
Period of rebalancing: weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Simple strategy
Bactest period: 2013 – 2017
Indicative performance: 6.30% (10% allocation into cryptocurrencies)
Estimated volatility: 8.10% (10% allocation into cryptocurrencies)
#379 – Carry Factor in Cryptocurrencies
Period of rebalancing: weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Simple strategy
Bactest period: 2013 – 2017
Indicative performance: 16.40% (10% allocation into cryptocurrencies)
Estimated volatility: 7.50% (10% allocation into cryptocurrencies)
#380 – Value Factor in Cryptocurrencies
Period of rebalancing: weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Simple strategy
Bactest period: 2013 – 2017
Indicative performance: 8.00% (10% allocation into cryptocurrencies)
Estimated volatility: 7.10% (10% allocation into cryptocurrencies)
#381 – Blended Factors in Cryptocurrencies
Period of rebalancing: weekly
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Simple strategy
Bactest period: 2013 – 2017
Indicative performance: 38.30% (10% allocation into cryptocurrencies)
Estimated volatility: 13.20% (10% allocation into cryptocurrencies)
Source paper for all mentioned strategies:
Hubrich, Stefan: 'Know When to Hodl 'Em, Know When to Fodl 'Em': An Investigation of Factor Based Investing in the Cryptocurrency Space
https://ssrn.com/abstract=3055498
Abstract:
It has been known since at least the groundbreaking work of Fama and French (1992) that there are specific attributes, so called factors, that can help predict the returns of individual assets above the return of the broader market. Since these predictive characteristics arise out of sample (with currently observable factor values predicting future returns), investors can earn excess returns with portfolios that are constructed to align with the factors. First introduced in the cross section of returns and focusing on individual equity securities, the efficacy of such factors has since been demonstrated at the asset class level as well, and found to work not only in the cross section but also longitudinally (for individual assets, through time). Factors like value, momentum, and carry have been found to work so broadly across different asset classes, security universes, countries, and time periods, that Asness et al. simply titled their influential 2013 Journal of Finance paper “Value and Momentum Everywhere”. Our paper provides a first application of momentum, value, and carry based factor investing to the cryptocurrencies. We show that these same factors are effective in this relatively new and unexplored asset class, permitting the construction of portfolios that can earn excess returns over the cryptocurrency “market” as a whole.
New research paper related to existing strategies:
#14 – Momentum Effect in Stocks
#68 – Combining Earnings, Revenue and Price Momentum
#199 – ROA Effect within Stocks
#224 – Profitability Factor Combined with Value Factor
Liang, Tang, Xu: Uncertainty, Momentum, and Profitability
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3020334
Abstract:
In this article, the authors argue that momentum and profitability factors share a common source in uncertainty. Specifically, the authors find that uncertainty subsumes price momentum and operating profitability; it also accounts for the majority of the profits associated with earnings momentum and return on equity, especially in large firms. Further, the profits of these four aforementioned momentum/profitability strategies concentrate in periods of negative market returns, consistent with high uncertainty stocks’ greater vulnerability to bad market states documented in recent literature. The market-state dependence of momentum/profitability strategies has significant implications to portfolio managers who attempt to profit from these strategies. Understanding the sources of the profits also helps portfolio managers better employ these factors in constructing investment portfolios.
Two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
#12 – Pairs Trading with Stocks
da Silva, Ziegelman, Caldeira: Pairs Trading: Optimizing via Mixed Copula versus Distance Method for S&P 500 Assets
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3070950
Abstract:
We carry out a study to evaluate and compare the relative performance of the distance and mixed copula pairs trading strategies. Using data from the S&P 500 stocks from 1990 to 2015, we find that the mixed copula strategy is able to generate a higher mean excess return than the traditional distance method under different weighting structures when the number of tradeable signals is equiparable. Particularly, the mixed copula and distance methods show a mean annualized value-weighted excess returns after costs on committed and fully invested capital as high as 3.98% and 3.14% and 12.73% and 6.07%, respectively, with annual Sharpe ratios up to 0.88. The mixed copula strategy shows positive and significant alphas during the sample period after accounting for various risk-factors. We also provide some evidence on the performance of the strategies over different market states.
#5 – FX Carry Trade
#8 – FX Momentum
#9 – FX Value – PPP Strategy
Fratzcher, Menkhoff, Sarno, Schmeling, Stoehr: Systematic Intervention and Currency Risk Premia
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3119907
Abstract:
Using data for the trades of 19 central banks intervening in currency markets, we show that leaning against the wind by individual central banks leads to "systematic intervention" in the aggregate central banking sector. This systematic intervention is driven by and impacts on the same factors that drive currency excess returns: carry, momentum, value, and a dollar factor. The sensitivity of an individual central bank's intervention to these factors differs markedly across countries, with developed countries making a profit from intervention and emerging markets incurring large losses.
