New strategies:
#351 – Returns Signal Momentum
Period of rebalancing: monthly
Markets traded: equities, commodities, bonds, currencies
Instruments used for trading: futures, CFDs
Complexity: Simple strategy
Bactest period: 1985 – 2015
Indicative performance: 11.90%
Estimated volatility: 12.30%
Source paper:
Papailias, Liu, Thomakos: Returns Signal Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2971444
Abstract:
A new type of momentum based on the probability of returns signs is introduced. This is caused by sign dependence, which is positively related to mean returns and negatively related to individual security’s volatility. Empirical evidence in a large universe of commodity and financial futures supports this momentum factor. Investment strategies based on returns signal momentum result in higher returns, Sharpe ratio and lower drawdown when compared to time series momentum and other benchmark strategies. Overall, returns signal momentum can benefit investors as an effective strategy for speculation and hedging.
#352 – Time Series Reversal of Momentum on Futures
Period of rebalancing: monthly
Markets traded: equities, commodities, bonds, currencies
Instruments used for trading: futures, CFDs
Complexity: Moderately complex strategy
Bactest period: 1985 – 2015
Indicative performance: 24.40%
Estimated volatility: 20.90%
Source paper:
Liu, Jiadong and Papailias, Fotis : Time Series Reversal of Financial Assets
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2971875
Abstract:
A reversal pattern in the time series context from 12 to 24 months after the formation of trend following signals is observed. By decomposing the trend following strategy returns according to their performance, we find that instruments with sell signals in the trend following portfolio (i.e. "losers") contribute to this type of reversal, even if their profits are not realised. The instruments with buy signals in the trend following portfolio (i.e. "winners") contribute much less. More specifically, the sub-portfolio which consists of "loser" instruments in a trend following strategy which later shows positive returns, i.e. the "fault losers", continue to provide a significant alpha which is immune to momentum risks. A double-sorted strategy combining both time series continuation and reversal yield to an average return of 18% per annum, which is significantly higher compared to the trend following strategies.
New research papers related to existing strategies:
#20 – Volatility Risk Premium Effect
Israelov, Tummala: Which Index Options Should You Sell?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2990542
Abstract:
This paper explores historical return and risk properties of equity-hedged options across the S&P 500 option surface. We evaluate returns by estimating alpha to the S&P 500 index, and we quantify risk using three metrics: return volatility, losses under stress tests, and conditional value at risk. We show that analyzing option risk-adjusted alphas using different risk metrics leads to significantly different conclusions. We find that the most compensated options to sell on the S&P 500 surface per unit of stress-test loss are front-month options with strikes near-the-money and moderately below the index level. We apply these results to evaluate return expectations for short volatility strategies, potential added return from option selection, and implications for variance swaps.
Two additional related research papers have been included into existing free strategy reviews during last 2 week:
Interesting academic paper which analyzes different definitions of "Quality" factor:
Hsu, Kalesnik, Kose: Survey of Quality Investing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2971185
Abstract:
Factor investing has experienced a resurgence in popularity under the moniker “smart beta.” Several traditional factors, such as value, size, momentum, and low beta, are well defined and have been heavily researched in academia as return anomalies for many decades. These factors have also been exploited by practitioners as quantitative strategies for enhancing returns. Today, these factors each define a distinct smart beta category (think of style boxes for smart beta strategies) and are the foundational building blocks for the now-ubiquitous multi-factor products.
Interesting academic paper about REITs:
Kizer, Grover: Are REITs a Distinct Asset Class?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2965146
Abstract:
Real estate investment trusts (REITs) are often considered to be a distinct asset class. But, do REITs deserve this designation? While exact definitions for asset class may vary, a number of statistical methods can provide strong evidence either for or against the suitability of the designation. The authors step back from the established real estate and REITs literature and answer this broader question. Beginning with a set of asset class criteria, the authors then utilize a variety of statistical methods from the literature and factor-based asset pricing to evaluate REITs for their candidacy as a distinct asset class. REITs fail to satisfy almost all of the relevant criteria which leads the authors to conclude that REITs, in fact, are not a distinct asset class but do deserve a market capitalization weighted allocation in a diversified investment portfolio.
