New strategies:
#293 – Momentum Effect in Anomalies v2
Period of rebalancing: monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Bactest period: 1976-2013
Indicative performance: 16.52%
Estimated volatility: 12.76%
Source paper:
Avramov,Cheng, Schreiber, Shemer: Scaling up Market Anomalies
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2709178
Abstract:
This paper implements momentum among a host of market anomalies. Our investment universe consists of the 15 top (long-leg) and 15 bottom (short-leg) anomaly portfolios. The proposed active strategy buys (sells short) a subset of the top (bottom) anomaly portfolios based on past one-month return. The evidence shows statistically strong and economically meaningful persistence in anomaly payoffs. Our strategy consistently outperforms a naive benchmark that equal weights anomalies and yields an abnormal monthly return ranging between 1.27% and 1.47%. The persistence is robust to the post-2000 period, and various other considerations, and is stronger following episodes of high investor sentiment.
#294 – Seasonality Within Trend-Following Strategy in Commodities
Period of rebalancing: monthly
Markets traded: commodities
Instruments used for trading: futures, CFDs
Complexity: Moderately complex strategy
Bactest period: 1992-2015
Indicative performance: 7.52%
Estimated volatility: 9.32%
Source paper:
Baltas: Multi-Asset Seasonality and Trend-Following Strategies
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2710334
Abstract:
This paper investigates the seasonality patterns within various asset classes. We find that a strategy that buys the assets with the largest same-calendar-month past average returns (up to ten years) and sells the assets with the smallest same-calendar-month past average returns, earns statistically and economically significant premia within commodity and equity index universes. Capitalising these premia directly appears practically difficult, due to the high strategy turnover and associated costs. We therefore suggest a way to actively incorporate seasonality signals into a trend-following strategy by switching off long and short positions, when the respective seasonality signals argue otherwise. The seasonality-adjusted trend-following strategy constitutes a significant improvement to the raw strategy across both commodities and equity indices. The increased turnover can impact the performance pickup, but the relatively low trading costs of liquid futures contracts as well as methodological amendments that optimise position smoothing can render the improvement genuine.
New research paper related to existing strategies:
#216 – Active Collar Strategy
Israelov, Klein: Risk and Return of Equity Index Collars
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2704518
Abstract:
Equity index collar strategies are often perceived as a way for investors, at little to no cost, to exchange some upside exposure for reduced losses on the downside. That perception may be accurate if one considers only the net dollar cost of the strategy’s initial option trades, but it fails to account for the significant drag the collar may impose on returns. We decompose the equity index collar’s returns to show that it is expected to have lower returns than its underlying index, primarily because it earns less equity risk premium. Additionally, collars that are net long volatility exposure may further reduce expected returns because they pay out volatility risk premium. We then compare the collar to other ways of obtaining equity exposure with reduced downside risk. The analysis shows that not only has the collar strategy historically performed poorly relative to these alternatives, but investors should also expect it to continue to underperform in the future.
Three additional related research paper have been included into existing free strategy reviews during last 2 week:
#5 – FX Carry Trade
Abankwa, Blenman: FX Liquidity Risk and Carry Trade Returns
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2662955
Abstract:
We study the effects of FX liquidity risk on carry trade returns using a low-frequency market-wide liquidity measure. We show that a liquidity-based ranking of currency pairs can be used to construct a mimicking liquidity risk factor, which helps in explaining the variation of carry trade returns across exchange rate regimes. In a liquidity-adjusted asset pricing framework, we show that the vast majority of variation in carry trade returns during any exchange rate regime can be explained by two risk factors (market and liquidity risk) in the FX market. Our results are further corroborated when the hedge liquidity risk factor is replaced with a non-tradable innovations risk factor.
#26 – Value (Book-to-Market) Anomaly
Golubov, Konstantinidi: A Closer Look at the Value Premium: Evidence from a Multiples-Based Decomposition
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2702822
Abstract:
We use industry multiples-based market-to-book decomposition of Rhodes-Kropf, Robinson and Viswanathan (2005) to study the value premium. The market-to-value component drives all of the value strategy return, while the value-to-book component exhibits no return predictability in both portfolio sorts and firm-level return regressions controlling for other stock characteristics. Prior results in the literature linking value/glamor to expectational errors and limits to arbitrage hold due to the market-to-value component, whereas the results linking market-to-book to cashflow risk, exposure to investment-specific technology shocks, and analyst’s risk ratings hold only for the unpriced value-to-book. Overall, our evidence points towards the mispricing explanation for the value premium.
#77 – Beta Factor in Stocks
Buchner, Wagner: The Betting Against Beta Anomaly: Fact or Fiction?
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2703752
Abstract:
This paper suggests an alternative explanation for the recently documented betting against beta anomaly. Given that the equity of a levered firm is equivalent to a call option on firm assets and option returns are non-linearly related to underlying stock returns, linear CAPM-type regressions are generally misspecified. We derive theoretical expressions for the pricing error and analyze its magnitude using numerical examples. Consistent with the empirical findings of Frazzini and Pedersen (2014), our pricing errors are negative, increase with leverage, and become economically significant for higher levels of firm leverage.
