New strategies:
#179 – Market Timing Using Sharpe Ratios
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs, futures, CFDs, funds
Complexity: Moderately complex strategy
Bactest period: 1953 – 2010
Indicative performance: 14.18%
Estimated volatility: 15.38%
Source paper:
Tang, Whitelaw: Time-Varying Sharpe Ratios and Market Timing
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1938613
Abstract:
This paper documents predictable time-variation in stock market Sharpe ratios. Predetermined financial variables are used to estimate both the conditional mean and volatility of equity returns, and these moments are combined to estimate the conditional Sharpe ratio, or the Sharpe ratio is estimated directly as a linear function of these same variables. In sample, estimated conditional Sharpe ratios show substantial time-variation that coincides with the phases of the business cycle. Generally, Sharpe ratios are low at the peak of the cycle and high at the trough. In an out-of-sample analysis, using 10-year rolling regressions, relatively naive market-timing strategies that exploit this predictability can identify periods with Sharpe ratios more than 45% larger than the full sample value. In spite of the well-known predictability of volatility and the more controversial forecast-ability of returns, it is the latter factor that accounts primarily for both the in-sample and out-of-sample results.
#180 – Simple NAV Arbitrage within Country ETFs
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Moderately complex strategy
Bactest period: 1998 – 2004
Indicative performance: 33.88%
Estimated volatility: not stated
Source paper:
Simon, Stenberg: Overreaction and Trading Strategies in European iShares
http://www.efmaefm.org/efma2005/papers/237-simon_paper.pdf
Abstract:
This paper examines the forecasting power of German, UK and French iShares for the next day returns of the underlying Morgan Stanley country equity indexes and assesses whether European iShares overreact to developments after the close of European trading. The findings indicate that although deviations of European iShare prices from net asset values (NAVs) at the close of US trading have significant forecast power for next day NAV returns, they overpredict. Deviations of closing iShare prices from their NAVs also lead to next day iShare price reversals that average roughly 3/8 of the size of the deviations. Finally, the paper demonstrates the profitability of trading rules that exploit the tendency of European iShares to overreact to late day US trading activity.
New research papers related to existing strategy:
#1 – Asset Class Trend Following
Marmi, Risso: Tactical Asset Allocation Using Daily Data
http://www.econ-pol.unisi.it/risso/opinions/PortfolioArt15072008.pdf
Abstract:
A portfolio combining different assets can produce larger return and less volatility. However, this is not a new idea; the Talmud even mentions the advantages of asset allocation (real estate, commodities and cash) approximately 2000 years ago. One can think about many strategies that combine these assets. Recently, Faber (2006) proposed a very simple quantitative market-timing model. In words, it consists in portfolio composed by US assets, foreign assets, commodities, real estate and bonds in equal parts. The strategy is to study the trend of each element, maintaining the position in the asset if the trend is growing. However, if the trend is going down we sell the asset and buy cash. The purpose of the present work is to apply the strategy developed in Faber (2006) using daily data of US stocks, European stocks, commodities, bond funds and cash for the period March 1st, 1994 and May 25, 2008.
Antonacci: Risk Premia Harvesting Through Momentum
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2042750
Abstract:
Momentum is the premier market anomaly. It is nearly universal in its applicability. Rather than focus on momentum applied to particular assets or asset classes, this paper explores momentum with respect to what makes it most effective. We do this first by introducing a hurdle rate filter before we can initiate long positions. This ensures that momentum exists on both an absolute and relative basis and allows momentum to function as a tactical overlay. We then explore the factor most rewarded by momentum – extreme past returns, i.e., price volatility. We identify high volatility through the paired risk premiums in foreign/U.S. equities, high yield/credit bonds, equity/mortgage REITs, and gold/Treasury bonds. Using modules of asset pairs as building blocks lets us isolate volatility related risk factors and use momentum to effectively harvest risk premium profits.
