New strategies:
#388 – Implied Skewness Strategy in Commodities
Period of rebalancing: daily
Markets traded: futures, CFDs
Instruments used for trading: commodities
Complexity: Complex strategy
Bactest period: 2008-2016
Indicative performance: 17.21%
Estimated volatility: 28.00%
Source paper:
Finta, Marinela Adriana and Ornelas, Jose Renato Haas: Commodity Return Predictability: Evidence from Implied Variance, Skewness and their Risk Premia
https://ssrn.com/abstract=3134310
Abstract:
This paper examines the performance of time-series momentum across 65 futures markets from all major asset classes, including equity indices, fixed income, currencies and commodities, for the period between January 1975 and December 2016. We find that the basis between spot and futures contracts explains approximately 36% of the performance of time-series momentum indicating that time-series momentum and carry are related. Conditioning trading signals on the sign of the basis improves the Sharpe ratio of time-series momentum by approximately 0.17 and is robust across sub-periods, choice of position-sizing in the implementation of time-series momentum and the lookback period used in the calculation of the basis. The improvement in performance is particularly strong during the early stages of recessions that tend to exhibit very poor stock market performance. Therefore, our strategy can substantially improve investors' welfare. We investigate whether time-series momentum and carry are related to hedging premium by examining the positions of hedgers in the Commitment of Traders (COT) reports. We find strong evidence that indicates that time-series momentum is capturing hedging premium whereas the carry trade is only weakly related to hedging premium. Thus, time-series momentum and carry are related because both strategies benefit from the time-series and cross-sectional variability in basis, and yet they are distinct because time-series momentum alone is linked to hedging premium.
#389 – Cryptomarket Discounts
Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex strategy
Bactest period: 2015 – 2018
Indicative performance: 17.82%
Estimated volatility: 3.82%
Source paper:
Borri, Nicola and Shakhnov, Kirill: Cryptomarket Discounts
https://ssrn.com/abstract=3124394
Abstract:
This paper studies fiat-crypto currency investment strategies across exchanges around the globe from the perspective of US investors. We take Bitcoin as representative cryptocurrency and consider exchanges where investors can trade different fiat and crypto currency pairs (i.e., US dollar for Bitcoin). We treat each currency pairs as a different asset. First, we document large and persistent deviations in the bitcoin prices, converted in U.S. dollars, across the different exchanges. Second, we show that an investment strategy based on information on past cross exchanges price deviations generate large excess returns. We provide evidence that portfolios with the largest price deviations invest in exchanges with a higher probability of temporary shut downs; the smallest bitcoin supplies; the larger volume of transactions; and higher return volatility. These facts are consistent with the convenience yield hypothesis of cryptomarket discounts.
New research paper related to existing strategies:
#230 – Mean Variance Carry Trade Strategy
Ackermann, Pohl, Schmedders: On the Risk and Return of the Carry Trade
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2184336
Abstract:
The traditional carry trade has historically been highly profitable, but suffered from crash risk, the proverbial "up by the stairs and down by the elevator.'' This crash risk was realized in dramatic fashion in the wake of the Lehman bankruptcy, when an investor who was long the Australian dollar and short the yen would have lost 22% in October of 2008. In sharp contrast, a dynamic diversified portfolio constructed using mean-variance analysis performs well, even during the crash. A portfolio constructed using mean-variance analysis can identify opportunities that a more heuristic method will not detect. Once sufficiently diversified, the carry trade turns out to have been a surprisingly low-risk strategy over the last 20 years.
Two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
#38 – Accrual Anomaly
Detzel, Schaberl, Strauss: There are Two Very Different Accruals Anomalies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3069688
Abstract:
We document that several well known asset-pricing implications of accruals differ for investment and non-investment-related components. Exposure to an investment-accruals factor explains the cross-section of returns better than the accruals themselves, and this factor’s returns are negatively predicted by sentiment. The opposite results hold for non-investment accruals. Further tests show cash profitability only subsumes long-term non-investment accruals in the cross-section of returns and economy-wide investment accruals negatively predict stock-market returns while other accruals do not. These results challenge existing accruals-anomaly theories and help resolve mixed evidence by showing that the anomaly is two separate phenomena: a risk-based investment accruals premium and a mispricing of non-investment accruals.
Is Bitcoin a new gold – aka. a hedge or safe heaven asset during equity downturns? Short answer – No. Again, recommended read about cryptocurrencies … :
Klein, Hien, Walther: Bitcoin Is Not the New Gold: A Comparison of Volatility, Correlation, and Portfolio Performance
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3146845
Abstract:
Cryptocurrencies such as Bitcoin are establishing themselves as an investment asset and are often named the New Gold. This study, however, shows that the two assets could barely be more different. Firstly, we analyze and compare conditional variance properties of Bitcoin and Gold as well as other assets and nd differences in their structure. Secondly, we implement a BEKK-GARCH model to estimate time-varying conditional correlations. Gold plays an important role in financial markets with flight-to-quality in times of market distress. Our results show that Bitcoin behaves as the exact opposite and it positively correlates with downward markets. Lastly, we analyze the properties of Bitcoin as portfolio component and nd no evidence for hedging capabilities. We conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets. Our results hold for the broad cryptocurrency index CRIX. As of now, Bitcoin does not reflect any distinctive properties of Gold other than asymmetric response in variance.
