Strategic Rebalancing
Nicolas Granger (Man AHL), et al.
April 3, 2019
A mechanical rebalancing strategy, such as a monthly or quarterly reallocation towards fixed portfolio weights, is an active strategy. Winning asset classes are sold and losers are bought. During crises, when markets are often trending, this can lead to substantially larger drawdowns than a buy-and-hold strategy. Our paper shows that the negative convexity induced by rebalancing can be substantially mitigated, taking the popular 60-40 stock-bond portfolio as our use case. One alternative is an allocation to a trend-following strategy. The positive convexity of this overlay tends to counter the impact on drawdowns of the mechanical rebalancing strategy. The second alternative we call strategic rebalancing, which uses smart rebalancing timing based on trend-following signals – without a direct allocation to a trend-following strategy. For example, if the trend-following model suggests that stock markets are in a negative trend, rebalancing is delayed.

Tail Risk Management for Multi-Asset Multi-Factor Strategies
David Chambers (University of Cambridge), et al.
January 8, 2019
Multi-asset multi-factor portfolio allocation is typically centred around a risk-based allocation paradigm, often striving for maintaining equal volatility risk budgets. Given that the common factor ingredients can be highly skewed, we specifically incorporate the notion of tail risk management into the construction of multi-asset multi-factor portfolios. Indeed, we find that the minimum CVaR concentration approach of Boudt, Carl and Peterson (2013) effectively mitigates the dangers of tail risk concentrations. Yet, diversifying across multiple assets and style factors can be in and of itself a good means of tail risk management, irrespective of the risk-based allocation technique employed.

The Endowment Model and Modern Portfolio Theory
Stephen G. Dimmock (Nanyang Technological University), et al.
February 7, 2019
We develop a dynamic portfolio-choice model with illiquid alternative assets to analyze conditions under which the “Endowment Model,” used by some large institutional investors such as university endowments, does or does not work. The alternative asset has a lock-up, but can be voluntarily liquidated at any time at a cost. Quantitatively, our model’s results match the average level and cross-sectional variation of university endowment funds’ spending and asset allocation decisions. We show that asset allocations and spending crucially depend on the alternative asset’s expected excess return, risk unspanned by public equity, and investors’ preferences for inter-temporal spending smoothing.

Measuring Risk Preferences and Asset-Allocation Decisions: A Global Survey Analysis
Andrew W. Lo (Massachusetts Institute of Technology), et al.
February 4, 2019
We use a global survey of over 22,400 individual investors, 4,892 financial advisors, and 2,060 institutional investors between 2015 and 2017 to elicit their asset allocation behavior and risk preferences. We find substantially different behavior among these three groups of market participants. Most institutional investors exhibit highly contrarian reactions to past returns in their equity allocations. Financial advisors are also mostly contrarian; a few of them demonstrate passive behavior. However, individual investors tend to extrapolate past performance. We use a clustering algorithm to partition individuals into five distinct types: passive investors, risk avoiders, extrapolators, contrarians, and optimistic investors. Across demographic categories, older investors tend to be more passive and risk averse.

Deep Learning for Global Tactical Asset Allocation
Gaurav Chakravorty (Qplum), et al.
October 19, 2018
We show how one can use deep neural networks with macro-economic data in conjunction with price-volume data in a walk-forward setting to do tactical asset allocation. Low cost publicly traded ETFs corresponding to major asset classes (equities, fixed income, real estate) and geographies (US, Ex-US Developed, Emerging) are used as proxies for asset classes and for back-testing performance. We take dropout as a Bayesian approximation to obtain prediction uncertainty and show it often deviates significantly from other measures of uncertainty such as volatility. We propose two very different ways of portfolio construction – one based on expected returns and uncertainty and the other which obtains allocations as part of the neural network and optimizes a custom utility function such as portfolio sharpe. We also find that adding a layer of error correction helps reduce drawdown significantly during the 2008 financial crisis. Finally, we compare results to risk parity and show that the above deep learning strategies trained in totally walk-forward manner have comparable performance.

What Is the Optimal Weight for Gold in a Portfolio?
Brian M. Lucey (Trinity College Dublin), et al.
November 28, 2018
We show that the statistical properties of gold are negatively correlated with equities and that including Gold in a portfolio will provide diversification benefits. As there is no consensus on the proportion of gold that should be included in a strategic portfolio allocation we propose a visual tool that associates a performance metric with a range of possible asset weighting schemes; a Sharpe ratio response surface. This very surface shows that a target performance metric can be achieved with a large number of different allocations. We further argue that the rebalancing approach based on the surface closest to the benchmark surface under the Hausdorrf distance metric should be selected. Using a data sample between 1990 and 2018, we find that annual rebalancing with a 44 week lookback period achieves the minimum distance from the benchmark surface.

Dynamic Hedging of Currency Risk in Investment Strategies
Gaurav Chakravorty and Ankit Awasthi (Qplum)
November 22, 2018
Often, investors fully hedge their portfolios for currency risk. This can lead to significant drag in performance for currencies with negative carry. However, not hedging the foreign currency exposure can lead to significant drawdowns, especially for conservative investments. In this paper, we consider a conservative, global tactical asset allocation strategy implemented in US dollar denominated securities for a hypothetical, European investor and highlight the benefits of dynamic currency hedging over static hedging. Using a parsimonious model for hedge ratio based on multiple features of merit and an explicit check for maximum allowed under-hedging, we show that a cost aware, dynamic hedging strategy can reduce the hedging costs substantially while keeping the portfolio risk within mandate specifications.