Portfolio Strategy and Liquid Alternative Mutual Funds Versus Hedge Funds
Jonathan S. Hartley (University of Pennsylvania)
February 1, 2017
Despite the rapid rise of the number of liquid alternative mutual funds (LAMFs) available to retail investors in recent years, few studies have compared how their return and risk characteristics differ from their hedge fund counterparts across their entire history. Being among the ﬁrst omprehensive studies to look at over two decades of LAMF performance, we use risk based factors to compare the performance of LAMFs to hedge funds both in aggregate and broken down by investment styles including equity long-short, market neutral, multi-strategy and managed futures. Overall, we ﬁnd that LAMFs underperform hedge funds on average by 1-2% per year on a net-of-fee basis when controlling for standard risk factors. These ﬁndings provide important implications for investors seeking hedge fund-like returns while considering the importance of liquidity, transparency, and fees as well as for policymakers who have recently proposed imposing derivative position limits on 1940 Act investment vehicles.
Asset Allocation Strategies, the 1/N Rule, and Data Snooping
Po-Hsuan Hsu (University of Hong Kong), et al.
December 23, 2016
Using a series of tests based on White’s (2000) “Reality Check,” we assess the out-of-sample performance of a large class of portfolio strategies relative to the naive 1/N rule [equal-weight] and control for data-snooping bias. We examine if 23 basic strategies and more than 5,000 extended strategies outperform the 1/N rule in terms of the Sharpe ratio and the certainty-equivalent return (CEQ). Our empirical results based on various stock portfolios suggest that only a few strategies out-perform the 1/N rule after we control for data-snooping bias and transaction costs. Thus, this paper has implications for market efficiency and asset allocation.
Undiversifying During Crises: Is It a Good Idea?
Margherita Giuzio and S. Paterlini (EBS Universität für Wirtschaft und Recht)
December 2, 2016
High levels of correlation among financial assets, as well as extreme losses, are typical during crisis periods. In such situations, quantitative asset allocation models are often not robust enough to deal with estimation errors and lead to identifying underperforming investment strategies. It is an open question if in such periods, it would be better to hold diversified portfolios, such as the equally weighted, rather than investing in few selected assets. In this paper, we show that alternative strategies developed by constraining the level of diversification of the portfolio, by means of a regularization constraint on the sparse lq-norm of portfolio weights, can better deal with the trade-off between risk diversification and estimation error. In fact, the proposed approach automatically selects portfolios with a small number of active weights and low risk exposure. Insights on the diversification relationships between the classical minimum variance portfolio, risk budgeting strategies, and diversification-constrained portfolios are also provided. Finally, we show empirically that the diversification-constrained-based lq-strategy outperforms state-of-art methods during crises, with remarkable out-of-sample performance in risk minimization.
Beating the Market: Dynamic Asset Allocation with a Market Portfolio Benchmark
Ali Al-Aradi and Sebastian Jaimungal (University of Toronto)
January 30, 2017
Beating the market portfolio is a problem faced by many investors. Here, we formulate and solve a dynamic allocation problem that maximizes the utility of relative wealth of the investor’s portfolio to that of the market portfolio. We also allow the investor to control the deviation of the optimal allocation from the market portfolio itself, and using stochastic control techniques, provide explicit closed form expressions for the optimal allocation. In addition, we demonstrate how the optimal portfolio can be factored into five passive rule-based portfolios: (i) global minimum variance portfolio; (ii) high-growth portfolio; (iii) high-cash-flow portfolio; (iv) equal-weight portfolio; and (v) risk-parity portfolio. Finally, some numerical experiments based on calibration to real-world data are presented to illustrate the risk-reward profile of the optimal allocation in comparison to these and other commonly used strategies.
The Impact of Global Uncertainty on the Global Economy, and Large Developed and Developing Economies
Wensheng Kang (Kent State University), et al.
January 30, 2017
Global uncertainty shocks are associated with a sharp decline in global inflation, global growth and in the global interest rate. Over 1981 to 2014 global financial uncertainty forecasts 18.26% and 14.95% of the variation in global growth and global inflation respectively. Global uncertainty shocks have more protracted, statistically significant and substantial effects on global growth, inflation and interest rate than U.S. uncertainty shocks. U.S. uncertainty lags global uncertainty by one month. When controlling for domestic uncertainty, the decline in output following a rise in global uncertainty is statistically significant in each country, with the exception of the decline for China. The effects for the U.S. and for China are also relatively small. For most economies, a positive shock to global uncertainty has a depressing effect on prices and official interest rates. Exceptions are Brazil, Mexico and Russia, economies with large capital outflows during financial crises. Decomposition of global uncertainty shocks shows that global financial uncertainty shocks are more important than non-financial shocks.
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Factor Investing: The Rocky Road from Long Only to Long Short
Marie Briere (Amundi Asset Mgt) and A. Szafarz (U. Libre de Bruxelles)
January 30, 2017
The performances of factor investing rely heavily on short sales, not only for building the initial long-short strategy, but also for regularly rebalancing the positions. Since short selling is subject to both legal restrictions and substantial costs, this paper examines how severely restrictions on short positions affect the financial attractiveness of factor investing. To fill the gap between unconstrained long-short allocations and restricted long-only portfolios, we consider two in-between strategies: the first imposes that only the market can be shorted, and the second is the so-called “130/30” scenario that caps total short exposure at 30%. The takeaways are twofold. First, any infringement to the long-short strategy can harm significantly the mean-variance performances of efficient factor-based portfolios. This is linked to the fact that the total short exposure of optimal long-short portfolios can reach figures around 400% and above. Second, the factor portfolios built originally by Fama and French (1992) with the purpose of developing asset pricing are impressively clear-sighted when it comes to portfolio management. Indeed, combining these portfolios generates mean-variance performances similar to those of optimized long-short portfolios, except for low levels of volatility.
Systematic Tail Risk
Richard D. F. Harris (University of Exeter), et al.
December 16, 2016
We propose new systematic tail risk measures constructed using two different approaches. The first extends the canonical downside beta and co-moment measures, while the second is based on the sensitivity of stock returns to innovations in market crash risk. Both tail risk measures are associated with a significantly positive risk premium after controlling for other measures of downside risk, including downside beta, co-skewness and co-kurtosis. Using these measures, we examine the relevance of the tail risk premium for investors with different investment horizons.
Managing Risks in Institutional Portfolios
Andrea Malagoli (Independent Consultant)
November 27, 2016
Conventional risk management frameworks for investment portfolios rely on a set of mostly mathematical methodologies that make strong assumptions about the long term behavior of asset prices. These assumptions are based on the modern finance belief that there exists a long term, albeit stochastic, equilibrium in the markets and in the economy. The recent recurring market downturns (1999 and beyond) have exposed clear problem with the conventional approach.
This article takes a critical look at several anecdotal aspects of the real markets and attempts to define a number of useful characteristics that a modern risk management framework should possess. One particular distinction is made between ‘risk management’ vs ‘risk measurement’. The main conclusion of this article is that developing an effective risk management process for investment portfolios is less about developing sophisticated mathematical ‘risk measurement’ tools and more about making honest assessments about the assumptions used to build the portfolio and creating processes to deal with purely unknowable future events.