New strategies:
#441 – Front-Running S&P GSCI Index
Period of rebalancing: Daily
Markets traded: commodities
Instruments used for trading: futures, CFDs
Complexity: Simple strategy
Bactest period: 2004 – 2017
Indicative performance: 11.10% (with a leverage)
Estimated volatility: 18.05% (with a leverage)
Source paper:
Yan, Irwin, Sanders: Is the Supply Curve for Commodity Futures Contracts Upward Sloping?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3360787
Abstract:
Annual rebalancing of the S&P GSCI index provides a novel and strong identification to estimate the shape of supply curves for commodity futures contracts. Using the 24 commodities included in the S&P GSCI for 2004–2017, we show that cumulative abnormal returns (CARs) reach a peak of 59 basis points in the middle of the week following the rebalancing period, but the impact is temporary as it declines to near zero within the next week. The findings provide clear evidence that the supply curve for commodity futures contracts is upward sloping in the short-run but almost flat in the longer-run.
#442 – Intraday Momentum in Crude Oil ETF
Period of rebalancing: Intraday
Markets traded: commodities
Instruments used for trading: ETFs
Complexity: Simple strategy
Bactest period: 2006-2018
Indicative performance: 1.85%
Estimated volatility: not stated
Source paper:
Zhuzhu Wen, Ro Cho, Diandian Ma, Yahua Xu: Intraday Momentum: Evidence from the Crude Oil Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3416022
Abstract:
Our analysis of high-frequency United States Oil Fund (USO) data from 2006 to 2018 shows that intraday momentum exists in the crude oil market. The first half-hour return from the previous day’s market close positively predicts the last half-hour return both in-sample and out-of-sample. Predictability is stronger during crisis periods and on days with higher realized volatility, higher trading volume, higher overnight returns, and jumps. A market timing strategy, constructed by using the first half-hour return as a timing signal, outperforms two other benchmark strategies.
New research paper related to existing strategies:
#54 – Small Capitalization Stocks Premium (Size Effect)
Malitskaia: Momentum with Volatility Timing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3417360
Abstract:
The growing adoption of factor investing simultaneously prompted the active topic of factor timing approaches for the dynamic allocation of multi-factor portfolios. The trend represents a natural development of filling the gap between passive and active management. The paper addresses this direction by introducing the volatility-timed winners approach that applies past volatilities as a timing predictor to mitigate momentum factor underperformance for time intervals spanning the market downturn and post-crisis period. The proposed approach was confirmed with Spearman rank correlation and demonstrated in relation to different strategies including momentum volatility scaling, risk-based asset allocation, time series momentum and MSCI momentum indexes. The corresponding analysis generalized existing volatility scaling strategies and brought together the two branches of the smart-beta domain, factor investing and risk-based asset allocation.
And two short free blog posts about interesting related research papers have been published during last 2 weeks:
Related to #14 – Momentum Factor Effect in Stocks
Guo: Decomposing Momentum Spread
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3386828
Abstract:
Since momentum arbitrage activity, buying winners and selling losers, effectively enlarges the return spread between these two groups, I connect the momentum spread to future momentum performance. I find that the momentum spread (the difference of the formation-period recent 6-month returns between winners and losers) negatively predicts future momentum profit in the long-term, but not in the following month. I further decompose the momentum spread into the spreads of old or young momentum stocks based on whether a stock has been identified as a momentum stock for more than three months. I show that the negative predictability is mainly driven by the old momentum spread. For the top 20% of the sample period associated with the highest values of old momentum spread, the momentum reversals happen sooner (only six months after formation) and stronger (more than 120 basis points per month from month 7 to month 24 after formation), relative to negligible momentum reversals observed following the bottom 20% period with low old momentum spread. As these old momentum stocks are more likely to be exploited by arbitrageurs, these findings suggest that momentum is amplified by arbitrage activity and excessive arbitrage destabilizes the asset prices and generates strong reversals.
Hammed, Wu: Decomposing Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3401656
Abstract:
We decompose the momentum profits based on total stock returns into three components: a long-term average alpha component that reverses, a stock beta component that accounts for the dynamic market exposure (and momentum crash risk), and a residual return component that drives the momentum effect (and subsumes total-return momentum). The variation in total-return momentum across market states and business cycles is attributable to the time-varying performance of the long-term reversal component, while residual-return momentum is invariant over time. Hence, we establish a dichotomy between intermediate-term momentum and long-term reversal: stocks that experience momentum are different from the ones that reverse.
Slabchenko: Are Momentum Strategies Profitable? Recent Evidence from European Markets
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3397011
Abstract:
This paper examines the profitability of momentum strategies for a sample of Core and Peripheral European equity markets. More specifically, a large number of strategies with different combinations of ranking and holding periods are empirically evaluated for the period between December 1989 to January 2018 for the UK, Germany, French, Sweden, the Netherlands, Italy, Spain, Greece, and Portugal. The results indicate that both the profitability and the optimal combination of ranking and holding periods of momentum strategies vary across markets.
Cryptocurrencies are a new asset class, and researchers have just started to understand better fundamental forces which are behind their price action. A new research paper shows that Bitcoin’s price can be modeled by Metcalfe’s Law. Bitcoin (and other cryptocurrencies) are in this characteristic very similar to Facebook as their value depends on the number of active users – network size …
Peterson: Bitcoin Spreads Like a Virus
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3356098
Abstract:
We illustrate, by way of example, that Bitcoin’s long-term price is non-random and can be modeled as a function of the logistic growth of number of users n over time. Using observed data for both Facebook and Bitcoin, we derive the relationships between price, number of users, and time, and show that the resulting market capitalizations likely follow a Gompertz sigmoid growth function. This function, historically used to describe the growth of biological organisms like bacteria, tumors, and viruses, likely has some application to network economics. We conclude that the long-term growth rate in users has considerable effect on the long-term price of bitcoin.
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