Global Tactical Asset Allocation Research Articles

The cross-section of industry equity returns and global tactical asset allocation across regions and industries

This study investigates which index characteristics predict returns in the cross-section of local industry indexes in six regions. The results show that geographical origin and market capitalization of indexes critically determine the predictive ability of characteristics. We find that industry indexes of any market capitalization with high earnings-to-price ratio yield higher expected returns in the US, Europe, and Asia-Pacific. Recent winner (loser) portfolios in Europe have a tendency to outperform (underperform) recent loser (winner) portfolios in the near future for all groups of market capitalization. Small portfolios with high idiosyncratic volatility in Asia-Pacific earn an idiosyncratic volatility premium. Dividend yield is positively related to future returns of small European portfolios. These results are robust to the inclusion of transaction costs and control variables and have implications for portfolio managers following a global tactical asset allocation policy.

Dynamic Asset Allocation Based on Valuation and Momentum Applied to a Global All-Equity Portfolio

There has been considerable research into dynamic global tactical asset allocation (GTAA) strategies driven by simple measures of Valuation and Momentum applied to a baseline balanced portfolio of equities and fixed income (see Blitz and van Vliet 2008, Wang and Kochard 2011, Gnedenko and Yelnik 2014, and Dewey and Haghani 2016). In sum, this research has found that historically, these Valuation-and-Momentum-driven investment approaches have produced higher returns and more attractive risk-adjusted returns than a static-weight approach. However, the above-cited research has been mostly silent on the question of how such an approach could be implemented and would have performed in the context of an All-Equity portfolio, where the Valuation and Momentum signals would drive a dynamic allocation between different regional equity markets. This note explores this question. It finds that such a dynamic approach would have produced higher absolute returns, and higher risk-adjusted returns, than a static approach would have produced. However, the improvement in simulated historical returns is less than the improvement that Valuation and Momentum deliver when applied to a balanced baseline portfolio of equities and fixed income. For example, simulated historical returns for the All-Equity strategy described below over the past roughly 40 years had about 50% more variability but only 20% higher returns than did a similar dynamic Global Balanced strategy. We examine a form of the strategy that can be followed by any investor with a simple brokerage account, using low-cost, liquid and widely available ETFs or index funds, and without use of leverage or shorting. The implementation we explore in this note trades monthly, with turnover averaging about 50% per annum. It is be based on data that is freely available in the public domain.

Tactical and Tax Aware GTAA

Global tactical asset allocation (GTAA) has rarely been associated with tax-aware investing. Although GTAA can improve returns over a buy-and-hold strategy, more tactical trading (when profitable) typically leads to higher tax bills. If investors are careless about tax management, the GTAA alpha can easily be swamped by increased taxes. In response, many investors have been quick to embrace passive buy-and-hold strategies for which low turnover results in a lower tax bill. However, rather than abandoning GTAA in favor of lower taxes, investors can apply well-documented tax management methods of loss harvesting and tax-lot selection in their tactical trading. The authors show that investors can capture most of the benefits of GTAA, and even provide a boost to their after-tax returns, if they view the proactive management of tax consequences as an important part of the quest for asset allocation alpha.

Dynamic Hedging of Currency Risk in Investment Strategies

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.

Momentum and Covered Calls Almost Everywhere

This article examines times series momentum and covered call strategies through conventional representations across ten asset classes. The performance of the two strategies generally outperform static buy and hold investments and are classified as positive and negative autocorrelation factors. The tactical overlay of time series momentum and covered call strategies onto asset classes are considered asset transformations. The two transformed replacements of the underlying asset are incorporated into well-established risk-based allocation heuristics, such as maximum diversification and equal risk contribution. The resulting portfolios show enhanced risk-adjusted performance compared with corresponding buy and hold investments or individual strategy portfolios. The authors designate this global tactical asset allocation framework as autocorrelation factor allocation (ACFA). <b>TOPICS:</b>Options, statistical methods, performance measurement, portfolio construction

Deep Learning for Global Tactical Asset Allocation

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.

Developing an Effective Dynamic Beta Overlay

Many institutions – both multi-asset managers and asset owners – have developed Global Tactical Asset Allocation (GTAA) strategies. These strategies could be either long-only or long-short, but are typically fully funded. In an environment where the future expectations of asset and portfolio returns are low, and cash is valuable for pension funds that are often paying more in benefits than they receive in contributions, asset owners need to be creative and re-purpose certain strategies. Many of these GTAA strategies trade assets that are not in the client’s Strategic Asset Allocation (SAA) and GTAA strategies could potentially be adding to total fund risk to the extent they take bets similar to those embedded in the SAA. This paper is an adapted case study to demonstrate how an innovative asset owner that had crowdsourced best ideas from strategic partners to create their own unique GTAA, was able to adapt the GTAA model into a futures-based Dynamic Beta/SMART Rebalancing overlay to not only improve the Sharpe ratio, but also greatly enhance the risk management properties of the overall fund. More importantly, cash utilization was improved because futures overlays need only be financed with limited cash if designed appropriately.

Momentum and Covered Calls Almost Everywhere

We examine times series momentum and covered call strategies through conventional representations across 10 asset classes. The performance of the two strategies generally outperform static buy and hold investments and they are classified as positive and negative autocorrelation factors. The tactical overlay of time series momentum and covered call strategies onto asset classes are considered beta transformations (of previous alpha). The two “beta” replacements of the underlying asset are incorporated into well established risk based allocation heuristics such as maximum diversification and equal risk contribution. The resulting portfolios show enhanced risk adjusted performance compared to the corresponding buy and hold investments. We designate this global tactical asset allocation framework as autocorrelation factor allocation, ACFA.

Practical Applications of Risk-versus Trend-Driven Global Tactical Asset Allocation

Institutional investors who seek protection during bearish market environments should balance risk-based with trend-driven GTAA strategies, advises Benoit Guilleminot in this report. Guilleminot, Director of Research and Innovation at Riskelia, co-wrote Risk- Versus Trend-Driven Global Tactical Asset Allocation for The Journal of Portfolio Management. His co-authors are Jean-Jacques Ohana, President and Co-Founder of Riskelia, and Steve Ohana, Professor of Finance at ESCP Europe in Paris. They compared the performance of risk- and trend-driven investment strategies on equities, commodities and bonds between 1993 and 2012. “The relative weight of a trend-driven strategy should be increased during a major crisis, but when there is no real crisis investors should keep more diversification with a risk-driven strategy,” says Guilleminot.

Global Tactical Asset Allocation for Institutional Investment Management

The Alberta Investment Management Corporation (AIMCo) was established in 2008 to manage approximately C$70 billion on behalf of 26 pension, endowment, and government reserve funds in the Province of Alberta. The fund’s over-arching goal is to earn incremental return on risk over what our clients could achieve by passively implementing their policy asset mix with equity and fixed income index funds. One way to add return in excess of passive portfolios is through our Global Tactical Asset Allocation (GTAA) strategy. In this brief note, we provide a description of the ways in which AIMCo has developed and executed its GTAA process over the past few years. In so doing, we hope to offer some insights to other institutional investors that might be considering or preparing to launch a similar GTAA or tilting policy. We conclude that executing a successful GTAA requires developing world-class talent, systems, process and governance.

Global Tactical Cross-Asset Allocation: Applying Value and Momentum Across Asset Classes

Global tactical asset allocation (GTAA) strategies across a broad range of asset classes are explored in this article. In contrast to market timing for single asset classes and tactical allocation across similar assets, this topic has received little attention in the existing literature. The main finding documented in this article is that momentum and value strategies applied to GTAA across 12 asset classes deliver statistically and economically significant abnormal returns. For a long top-quartile and short bottom-quartile portfolio based on a combination of momentum and value signals, the authors report a return of 12% per annum over the 1986–2007 period. Performance is stable over time, is also present in an out-of-sample period, and is sufficiently high to overcome transaction costs in practice. The return cannot be explained by potential structural biases toward asset classes with high risk premiums, nor by the Fama–French and Carhart hedge factors. The authors argue that financial markets may be macroinefficient due to insufficient “smart money” being available to arbitrage away mispricing effects. <b>TOPICS:</b>VAR and use of alternative risk measures of trading risk, equity portfolio management, analysis of individual factors/risk premia

The Changing Nature and Role of Tactical Asset Allocation

Tactical asset allocation (TAA) has evolved over the last two decades from a purely domestic, single-decision strategy to a complex, global, multistrategy approach known as “global TAA” or “global macro.” It produces uncorrelated alpha and is scalable. As a consequence, global macro is attracting pension funds and other institutional assets in increasing numbers.

Global Tactical Asset Allocation

We provide a framework for using conditioning information in the process of global asset allocation. While we discuss strategies in a global setting, the same reasoning can be applied to other asset allocation programs with different investment objectives. We examine three levels of asset allocation: unconditional or benchmark allocation, strategic asset allocation, and tactical asset allocation. We show how to use conditioning information in the process of global tactical asset allocation. An important lesson emerges. There is considerable work documenting predictability of returns using past information variables. Many of these variables are related to the stage of the business cycle, and suggest that much of the predictability in these assets, or markets, are common. The fact that returns are predictable means that active asset allocation strategies can outperform passive strategies. Uncovering the predictability is challenging as it but allows managers to beat traditional benchmarks.