New strategies:
#230 – Mean Variance Carry Trade Strategy
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: futures, forwards, swaps, CFDs
Complexity: Complex strategy
Bactest period: 1990 – 2012
Indicative performance: 10.88%
Estimated volatility: 6.97%
Source paper:
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.
#231 – Comomentum Strategy
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1965-2010
Indicative performance: 8.60%
Estimated volatility: 12.18%
Source paper:
Lou, Polk: Comomentum: Inferring Arbitrage Activity from Return Correlations
http://bus.miami.edu/docs/UMBFC-2012/sba-ecommerce-5040f23b23b6b/Comomentum_120512.pdf
Abstract:
We propose a novel measure of the amount of arbitrage activity in the momentum strategy to test whether arbitrageurs can have a destabilizing e ect in the stock market. Our measure, which we dub comomentum, aims to capture the extent to which momentum trades by arbitrageurs become crowded. Speci cally, we de ne comomentum as the high-frequency abnormal return correlation among stocks that a typical momentum strategy would speculate on. We show that during periods of low comomentum, momentum strategies are pro table and stabilizing, reecting an underreaction phenomenon that arbitrageurs correct. In contrast, during periods of high comomentum, these strategies become unpro table and tend to crash, reflecting prior overreaction due to the momentum crowd pushing prices away from fundamentals. Moreover, both rm-level and international versions of comomentum forecast returns in a manner consistent with our interpretation.
New research paper related to existing strategy:
#1 – Asset Class Trend Following
#2 – Asset Class Momentum – Rotational System
Keller, Van Putten: Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2193735
Abstract:
In this paper we extend the timeseries momentum (or trendfollowing) model towards a generalized momentum model, called Flexible Asset Allocation (FAA). This is done by adding new momentum factors to the traditional momentum factor R based on the relative returns among assets. These new factors are called Absolute momentum (A), Volatility momentum (V) and Correlation momentum (C). Each asset is ranked on each of the four factors R, A, V and C. By using a linearised representation of a loss function representing risk/return, we are able to arrive at simple closed form solutions for our flexible asset allocation strategy based on these four factors. We demonstrate the generalized momentum model by using a 7 asset portfolio model, which we backtest from 1998-2012, both in- and out-of-sample.
