New strategy:
#356 – The Dollar Ahead of FOMC Target Rate Changes
Period of rebalancing: daily
Markets traded: currencies
Instruments used for trading: futures, forwards, swaps, CFDs
Complexity: Simple strategy
Bactest period: 1994 – 2015
Indicative performance: 7.17%
Estimated volatility: 7.28%
Source paper:
Karnaukh: The Dollar Ahead of FOMC Target Rate Changes
https://www.nhh.no/contentassets/cb01b0ad6f1744ddb065e325e5b92184/nina-karnaukh_jmp.pdf
Abstract:
I find that the U.S. dollar appreciates over the two-day period before contractionary monetary policy decisions at scheduled Federal Open Market Committee (FOMC) meetings and depreciates over the two-day period before expansionary monetary policy decisions. The federal funds futures rate forecasts these dollar movements with a 22% R2. A high federal funds futures spread three days in advance of an FOMC meeting not only predicts the target rate rise, but also predicts a rise in the dollar over the subsequent two-day period. A simple trading strategy, which exploits this predictability, exhibits a 0.93 Sharpe ratio. My findings imply that information about monetary policy changes is reflected first in the fixed income markets, and only later becomes reflected in currency markets.
New research papers related to existing strategies:
#1 – Asset Class Trend Following
#210 – Adaptive Asset Allocation
Keller, Keuning: Breadth Momentum and Vigilant Asset Allocation (VAA): Winning More by Losing Less
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3002624
Abstract:
VAA (Vigilant Asset Allocation) is a dual-momentum based investment strategy with a vigorous crash protection and a fast momentum filter. Dual momentum combines absolute (trendfollowing) and relative (strength) momentum. Compared to the traditional dual momentum approaches, we have replaced the usual crash protection through trendfollowing on the asset level by our breadth momentum on the universe level instead. As a result, the VAA strategy is on average often more than 50% out of the market. We show, however, that the resulting momentum strategy is by no means sluggish. By using large and small universes with US and global ETF-like monthly data starting 1925 and 1969 respectively, we arrive out-of-sample at annual returns above 10% with max drawdowns below 15% for each of these four universes.
#17 – Momentum Effect in Anomalies/Trading Systems
#171 – Market Timing Filter Applied to a Classical Stock Anomalies
#293 – Momentum Effect in Anomalies v2
Ehsani: Factor Momentum and the Momentum Factor
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3014521
Abstract:
I document a strong factor momentum effect. Profits of time series and cross-sectional momentum strategies are large and significant across well known anomalies and factors. Positive autocovariance in factor returns is the essential component of factor momentum profits and plays a key role in producing equity momentum profits. Positive autocovariance carries on to the slope estimates of Fama and MacBeth (1973) regressions conducted on firm characteristics. Time-varying autocorrelation in factor returns is critical in producing the unique properties of equity momentum profits. Occasional but widespread negative autocorrelations in factor returns produce the unattractive features of equity momentum returns. A diversified portfolio of past-return-conditioned anomalies explains the equity momentum premium and accommodates decile portfolios sorted on momentum. Factor momentum and sentiment are distinct and improve anomaly return predictability when included jointly.
#112 – Acceleration Effect Combined with Momentum in Stocks
Chen, Yu, Wang: Evolution of Historical Prices in Momentum Investing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3009059
Abstract:
We find that the acceleration and deceleration patterns of historical prices are predictive of future expected returns in momentum investing in the U.S. equity market from 1962 to 2014. Winners with accelerated historical price increases deliver higher future expected returns and losers with accelerated historical price decreases perform more poorly in the future. Hence, the profit from buying past accelerated winners and shorting past accelerated losers is significantly higher than the momentum profit by 51.47%. Such profit cannot be subsumed by certain characteristics that have been considered to explain momentum. Possible explanations for our results include extrapolative bias and overreaction.
Two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
A new interesting research paper related to:
#12 – Pairs Trading with Stocks
Figuerolla-Ferretti, Serrano, Tang, Vaello-Sebastia: Supercointegrated
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3005358
Abstract:
This paper uses S&P100 data to examine the performance of pairs trading portfolios that are sorted by the significance level of cointegration between their constituents. We find that portfolios that are formed with highly cointegrated pairs, named as "supercointegrated", yield the best performance reflecting a positive relationship between the level of cointegration and pairs trading profitability. The supercointegrated portfolio also shows superior out-of-sample performance to the simple buy-and-hold investments on the market portfolio in terms of Sharpe ratio. We link the time-varying risk of the pairs trading strategy to aggregated market volatility. Moreover we report a positive risk-return relationship between the strategy and market volatility, which is enhanced during the bear market. Our results remain valid when applying the strategy to European index data.
And a new financial research paper related to:
#75 – Federal Open Market Committee Meeting Effect in Stocks
Cocoma: Explaining the Pre-Announcement Drift
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3014299
Abstract:
I propose a theoretical explanation for the puzzling pre-announcement positive drift that has been empirically documented before scheduled Federal Open Market Committee (FOMC) meetings. I construct a general equilibrium model of disagreement (difference-of-opinion) where two groups of agents react differently to the information released at the announcement and to signals available between two announcement releases. In contrast to traditional asset pricing explanations, this model matches key empirical facts such as (1) the upward drift in prices just before the announcement, (2) lower (higher) risk, price volatility, before (after) the announcement occurs, and (3) high trading volume after the announcement, while trading volume is low before the announcement occurs.
