New strategies:
#359 – Connected-Stocks Momentum Portfolio
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Bactest period: 1983 – 2015
Indicative performance: 11.22%
Estimated volatility: 19.49%
Source paper:
Ali, Usman: Shared Analyst Coverage and Cross-Asset Momentum Effects
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3015582
Abstract:
I link stocks through shared analyst coverage and document a robust lead-lag relationship between connected stocks. A connected-stock (CS) momentum factor generates large alphas after controlling for previously documented cross-asset momentum effects. In spanning regressions, industry, geographic, customer, customer industry, supplier industry, and single- to multi-segment momentum factors have insignificant or negative alphas after controlling for the CS momentum effect. Similar results obtain in cross-sectional regressions and in a large sample of international markets. Sell-side analysts also have greater difficulty processing information about analyst linked firms. My results offer a unified explanation for seemingly distinct cross-asset momentum effects.
#360 – Trend Following Trading Strategies for Currencies
Period of rebalancing: daily
Markets traded: currencies
Instruments used for trading: futures, forwards, swaps, CFDs
Complexity: Complex strategy
Bactest period: 1975 – 2017
Indicative performance: 12.25%
Estimated volatility: 22.90%
Source paper:
Rohrbach, Janick and Suremann, Silvan and Osterrieder, Joerg: Momentum and Trend Following Trading Strategies for Currencies Revisited – Combining Academia and Industry
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2949379
Abstract:
Momentum trading strategies are thoroughly described in the academic literature and used in many trading strategies by hedge funds, asset managers, and proprietary traders. Baz et al. (2015) describe a momentum strategy for different asset classes in great detail from a practitioner’s point of view. Using a geometric Brownian Motion for the dynamics of the returns of financial instruments, we extensively explain the motivation and background behind each step of a momentum trading strategy. Constants and parameters that are used for the practical implementation are derived in a theoretical setting and deviations from those used in Baz et al. (2015) are shown. The trading signal is computed as a mixture of exponential moving averages with different time horizons. We give a statistical justification for the optimal selection of time horizons. Furthermore, we test our approach on global currency markets, including G10 currencies, emerging market currencies, and cryptocurrencies. Both a time series portfolio and a cross-sectional portfolio are considered. We find that the strategy works best for traditional fiat currencies when considering a time series based momentum strategy. For cryptocurrencies, a cross-sectional approach is more suitable. The momentum strategy exhibits higher Sharpe ratios for more volatile currencies. Thus, emerging market currencies and cryptocurrencies have better performances than the G10 currencies. This is the first comprehensive study showing both the underlying statistical reasons of how such trading strategies are constructed in the industry as well as empirical results using a large universe of currencies, including cryptocurrencies.
New research paper related to existing strategies:
#14 – Momentum Effect in Stocks
#38 – Accrual Anomaly
#52 – Asset Growth Effect
#68 – Combining Earnings, Revenue and Price Momentum
#122 – Momentum Combined with Asset Growth Effect
#127 – Accrual Anomaly ver.2
#130 – Investment Factor
#138 – Repurchase/New issue Effect
#199 – ROA Effect within Stocks
#208 – Share Issuance Effect
#224 – Profitability Factor Combined with Value Factor
Lu, Stambaugh, Yuan: Anomalies Abroad: Beyond Data Mining
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3012923
Abstract:
A pre-specified set of nine prominent U.S. equity return anomalies produce significant alphas in Canada, France, Germany, Japan, and the U.K. All of the anomalies are consistently significant across these five countries, whose developed stock markets afford the most extensive data. The anomalies remain significant even in a test that assumes their true alphas equal zero in the U.S. Consistent with the view that anomalies reflect mispricing, idiosyncratic volatility exhibits a strong negative relation to return among stocks that the anomalies collectively identify as overpriced, similar to results in the U.S.
Two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
If an investor wants to build multi-factor portfolio then he should look around and build a diversified global portfolio:
Binstock, Kose, Mazzoleni: Diversification Strikes Again: Evidence from Global Equity Factors
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3036423
Abstract:
The benefits of country diversification are well established. This article shows that the same benefits extend to equity factors, such as value, size, momentum, investment, and profitability. Specifically, country factor portfolios reflect both common variation, which we define as the global factor, and local variation. On average, a US investor could enjoy a 30% reduction in portfolio volatility by investing globally. We also document three other properties of equity factors. Like major asset classes, greater market integration is associated with greater factor co-movement, and factor portfolios of different countries tend to be more correlated during bear stock markets. However, unlike asset classes, the correlations of factor portfolios across countries have not been increasing over the last two decades, making global equity factors a particularly desirable addition to a portfolio.
An interesting insight into problems associated with an attempts to implement machine learning in trading:
de Prado: The 7 Reasons Most Machine Learning Funds Fail
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3031282
Abstract:
The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this presentation. Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are 7 critical mistakes underlying most of those failures.
