New strategies:
#354 – ETF Creation/Redemption Activity and Return Predictability
Period of rebalancing: monthly
Markets traded: equities, commodities, bonds
Instruments used for trading: ETFs
Complexity: Complex strategy
Bactest period: 2007 – 2015
Indicative performance: 13.35%
Estimated volatility: 14.11%
Source paper:
Brown, David C. and Davies, Shaun William and Ringgenberg, Matthew: ETF Arbitrage and Return Predictability
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2872414
Abstract:
Many finance models assume the existence of noise traders who push asset prices away from fundamental values. Yet empirically, these "animal spirits" are challenging to observe because fundamental values are inherently unobservable. We examine a novel database of trades by ETF authorized participants who specifically trade to correct violations of the law of one price. These trades allow us to measure arbitrage activity. We show that noise traders do not cancel each other out and arbitrage activity is associated with predictable price distortions. Our analysis indicates that noise traders exert a non-fundamental impact on market outcomes even when arbitrageurs are active. Thus, noise traders are not simply noise, they impact prices.
#355 – The Crisis Alpha Portfolio
Period of rebalancing: monthly
Markets traded: bonds
Instruments used for trading: ETFs
Complexity: Simple strategy
Bactest period: 1962 – 2014
Indicative performance: 7.29%
Estimated volatility: 5.04%
Source paper:
Faber, Nathan: The Search for Crisis Alpha: Weathering the Storm Using Relative Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2739520
Abstract:
Tactical strategies are becoming more prevalent in the marketplace, especially for downside protection. While many of these strategies “go-to-cash” for protection during times of market turmoil, a number of asset classes and strategies, such as long volatility assets, managed futures, equity exposure management, and low volatility equities, have been able to deliver crisis alpha – strong performance over a risk-free asset during market crises – allowing investors to increase returns despite broad market losses. In this paper, we introduce an easily accessible strategy, using relative momentum on U.S. Treasury investments (constant maturity indices and liquid, low-cost ETFs), to increase crisis alpha in a portfolio. We demonstrate over the period from 1962-2014 that the tactical methodology added significant crisis alpha relative to static fixed income investments (i.e. each individual asset and an equal weight portfolio). Next, we analyze the strategy separately over periods of rising and falling interest rates. We then utilize this strategy as the “safety asset” in a popular tactical investment strategy to increase both absolute and risk adjusted returns versus simply going to cash. Finally, we look at a current investable version of the strategy using liquid fixed income ETFs and discuss some practical aspects of the implementation and possible improvements to the methodology.
New research paper related to existing strategy:
#26 – Value (Book-to-Market) Anomaly
Asness, Frazzini, Israel, Moskowitz: Fact, Fiction, and Value Investing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2595747
Abstract:
Value investing has been a part of the investment lexicon for at least the better part of a century. In particular the diversified systematic “value factor” or “value effect” has been studied extensively since at least the 1980s. Yet, there are still many areas of confusion about value investing. In this article we aim to clarify many of these matters, focusing in particular on the diversified systematic value strategy, but also exploring how this strategy relates to its more concentrated implementation. We highlight many points about value investing and attempt to prove or disprove each of them, referencing an extensive academic literature and performing simple tests based on easily accessible, industry-standard public data.
Two additional related research papers have been included into existing free strategy reviews during last 2 weeks:
Out of curiosity, what is benchmark return for each active trader/investor …
Doeswijk, Lam, Swinkels: Historical Returns of the Market Portfolio
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2978509
Abstract:
Using a newly constructed unique dataset, this study is the first to document returns of the market portfolio for a long period and with a high level of detail. Our market portfolio basically contains all assets in which financial investors have invested. We analyze nominal, real, and excess return and risk characteristics of this global multi-asset market portfolio and the asset categories over the period 1960 to 2015. The global market portfolio realizes a compounded real return of 4.38% with a standard deviation of 11.6% from 1960 until 2015. In the inflationary period from 1960 to 1979, the compounded real return of the GMP is 2.27%, while this is 5.57% in the disinflationary period from 1980 to 2015. The reward for the average investor is a compounded return of 3.24%-points above the saver’s. We also compare the performance of an investor who holds the market portfolio with an investor who uses simple heuristics for the portfolio allocation. Our results suggest that the market portfolio is close to the mean-variance frontier, but our heuristic allocations achieve a significantly higher reward for risk.
Related to multiple strategies, mainly to Carry, Volatility Selling and Trend-Following strategies …
Sepp: Diversifying Cyclicality Risk of Quantitative Investment Strategies (Presentation Slides)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2980708
Abstract:
What is the most significant contributing factor to the performance of a quantitative fund: its signal generators or its risk allocators? Can we still succeed if we have good signal generators but poor risk management?
We consider the risk of the skewness and the cyclicality of the key quantitative strategies:
1. Carry strategies
2. Volatility strategies
3. Trend-following strategies
We then present the two approaches for diversification of the cyclicality risk for a master portfolio of these strategies using:
1. Top-down allocation
2. Bottom-up allocation
We illustrate a few examples using back-tested data using systematic quantitative strategies with risk-based allocators.
