New strategies:
#449 – Flight to Quality Factor in Fixed Income
Period of rebalancing: Daily
Markets traded: bonds
Instruments used for trading: futures, CFDs
Complexity: Simple strategy
Backtest period: 1969 – 2019
Indicative performance: 2.80%
Estimated volatility: 8.20%
#450 – Unemployment Gap Factor in Fixed Income
Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: futures, CFDs
Complexity: Simple strategy
Backtest period: 1969 – 2019
Indicative performance: 1.30%
Estimated volatility: 10.10%
#451 – Growth Gap Factor in Fixed Income
Period of rebalancing: Quarterly
Markets traded: bonds
Instruments used for trading: futures, CFDs
Complexity: Simple strategy
Backtest period: 1969 – 2019
Indicative performance: 1.00%
Estimated volatility: 9.99%
#452 – Reversal – Yield Change Factor in Fixed Income
Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: futures, CFDs
Complexity: Simple strategy
Backtest period: 1969 – 2019
Indicative performance: 1.70%
Estimated volatility: 10.00%
Source paper:
Gava, Jerome and Lefebvre, William and Turc, Julien: Beyond Carry and Momentum in Government Bonds
https://ssrn.com/abstract=3446653
Abstract:
This article revisits recent literature on factor investing in government bonds, in particular regarding the definition of value and defensive investing. Using techniques derived from machine learning, the authors identify the key drivers of government bond futures and the groups of factors that are most genuinely relevant. Beyond carry and momentum, they propose an approach to defensive investing that considers the safe-haven nature of government bonds. These two main styles may be complemented by value and a reversal factor in order to achieve returns independently from broad movements in interest rates.
New research papers related to existing strategies:
#7 – Low Volatility Factor Effect in Stocks – Long-Only Version
Blitz, van Vliet, Baltussen: The Volatility Effect Revisited
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3442749
Abstract:
High-risk stocks do not have higher returns than low-risk stocks in all major stock markets. This paper provides a comprehensive overview of this low-risk effect, from the earliest asset pricing studies in the nineteen seventies to the most recent empirical findings and interpretations since. Volatility appears to be the main driver of the anomaly, which is highly persistent over time and across markets, and which cannot be explained by other factors such as value, profitability, or exposure to interest rate changes. From a practical perspective we argue that low-risk investing requires little turnover, that volatilities are more important than correlations, that low-risk indices are suboptimal and vulnerable to overcrowding, and that other factors can be efficiently integrated into a low-risk strategy. Finally, we find little evidence that the low-risk effect is being arbitraged away, as many investors are either neutrally positioned, or even on the other side of the low-risk trade.
#26 – Value (Book-to-Market) Factor
Lev, Srivastava: Explaining the Demise of Value Investing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3442539
Abstract:
The business press claims that the long-standing and highly popular value investing strategy—investing in low-valued stocks and selling short high-valued equities—lost its edge since 2007. The reasons for this putative sudden demise of value investing elude investors and academics, making it a challenge to assess the likelihood of the return of value investing to its days of glory. Based on extensive data analysis we show that the strategy has, in fact, been unprofitable for almost 30 years, barring a brief resurrection following dotcom bust. We identify two major reasons for the demise of value: (1) accounting deficiencies causing systematic misidentification of value, and particularly of glamour (growth) stocks, and (2) fundamental economic developments which slowed down significantly the reshuffling of value and glamour stocks which drove the erstwhile gains from the value strategy. We end up by speculating on the likelihood of the resurgence of value investing, which seems low.
#14 – Momentum Factor Effect in Stocks
Teluja: Unraveling Momentum’s Moments
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3447702
Abstract:
I examine the momentum anomaly through the prism of higher order risk neutral moments of high and low momentum stocks. I use option prices from 2002 to 2013 to extract time varying ex ante estimates of variance, skewness and kurtosis of high and low momentum stocks. High momentum stocks have lower variance, less negative skewness and lower kurtosis. This is in contrast to studies that use static, ex post estimates of skewness to explain momentum returns as a function of exposure to the higher order moments of the underlying return distribution. These results highlight that momentum returns can not be explained by ex ante estimates of higher order moments and remain dicult to resolve empirically in a rational expectations framework.
And two short free blog posts have been published during last 2 weeks:
One blog related to an interesting financial research paper:
Financial academics have described so many equity factors that the whole universe of them is sometimes called “factor zoo”. Therefore, it is no surprise that there is a quest within an academic community to bring some order into this chaos. An interesting research paper written by Favilukis and Zhang suggests explaining a lot of equity factors with momentum anomaly. They show that very often, up to 50% of the equity factor returns can be linked to returns of momentum strategy. This link is especially prevalent in short legs of equity factors .
Favilukis, Zhang: One Anomaly to Explain Them All
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3444342
Abstract:
We argue that conditional on the existence of momentum, many other asset pricing anomalies are not particularly anomalous. First, empirically, we show that portfolios within which conditional momentum strategies (ie buying winners and selling losers) are unprofitable, tend to have significantly higher unconditional average returns than portfolios within which momentum strategies are profitable. Second, we rationalize this in a standard model to which we add momentum; the intuition is that assets with more conditional trading opportunities are bid up by speculators and tend to have higher prices and lower unconditional returns. Third, we show that for many asset pricing anomalies, a momentum strategy tends to be unprofitable within the long leg, but profitable within the short leg. Thus, according to our model, the long leg should earn higher unconditional average returns, which explains the anomaly. Once accounting for this effect, the average Fama French 3 factor alpha across 36 prominent anomalies falls by up to 47%. Finally, we show that although the CAPM beta is negatively related to the average unconditional return of a large set of portfolios, it is strongly positively related to the average conditional return of the same set of portfolios, which helps explain the apparent empirical failure of the CAPM.
Plus one short Quantpedia analysis:
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