Winners Repeat, Losers Repeat
This article provides a tactical asset allocation (hereafter referred to as “TAA”) demonstration portfolio, one that shows proof-of-concept. It also offers practical evidence that TAA, when properly executed, is a purely backward-looking, reactive approach to investment management, and to the extent that it eschews forecasting or prediction, could be deemed a form of passive management, but one that makes richer use of the time series properties of asset class returns. The empirical results presented herein, suggest that TAA is an investment management approach that warrants serious consideration. Moreover, this article suggests that a necessary condition for TAA success lies in correctly specifying its rather differentiated investment objective – one that may be unrelated to comparisons with popular third-party index benchmarks. Our industry has done a remarkably good job of mischaracterizing, mis-selling, and over-promising all-things TAA. And doing so with a profound willingness to compare or “evaluate” TAA portfolios using inappropriate and/or dysfunctional comparative measures – serving to guarantee inevitable dissatisfaction. This article attempts to correct these misspecifications.
Tactical Asset Allocation in the Age of ETFs
After the financial crisis of 2007-2008, there was renewed interest in Tactical Asset Allocation (TAA) as investors searched for better downside risk management solutions. A new class of TAA managers, often referred to as “tactical ETF strategists,” emerged. They develop tactical strategies aiming to provide downside protection while maximizing the upside potential using exchange-traded-funds (ETFs). Driven by investors’ demand, the tactical ETF strategy segment grew dramatically from 2009 to 2014. In this whitepaper, we compare this new segment to the old-style pre-crisis TAA strategies and examine current issues and trends. Further, we discuss how TAA strategies can offer efficient solutions to two of the most pressing issues investors face today: downside protection in a bear market and income generation in a low interest rate environment.
Adaptive Investment Approach
During the last decade, two deep bear markets, as results of tech bubble and mortgage crisis, have challenged the conventional wisdoms such as modern portfolio theory (MPT) and Efficient Market Hypothesis (EMH). As an alternative, the Adaptive Markets Hypothesis (AMH), proposed by Lo (2004, 2005, 2012), in which intelligent but fallible investors constantly adapt to changing market conditions, helps explain the importance of macro factors and market sentiment in driving asset returns. In this paper, I examine some of the shortcomings of the MPT and EMH, as well as the drawbacks in their applications. More importantly, I introduce the framework of adaptive investment approach, under which investors can adjust their investments to reflect economic regimes, ongoing market return or market volatility. Some of the investment strategies such as regime-based investing, momentum strategy, trend following or risk parity fall into the framework. This approach has the potential to deliver consistent returns in any market environments, by dynamically positioning in the financial assets perceived to have best return potential under the ongoing market and economic condition. For example, in the risk-seeking (“risk on”) environment, the strategy allocates to risky assets such as equities, commodities, real estates or high yield bonds; in the risk-avoidance (“risk off”) environment, the strategy invests in safe assets such as Treasuries or cash. Instead of forecasting future returns under the traditional active investment framework, the adaptive approach focuses more on identifying the market regimes and conditions and adjusting the investment strategies accordingly. In the end, I show that this approach can help enhance returns and diversify risks in the context of asset allocation.
Tilt Nickels to Diamonds
We propose a simple two-stage portfolio tilting strategy to address the long-standing “risk factors eating alpha” problem. Fundamental factors are projected onto risk factors to obtain a set of risk-adjusted fundamental factors. Then we tilt portfolio weights of any selected index based on the risk-adjusted fundamental factors to construct more efficient index portfolios. We illustrate the efficiency of the strategy using a hypothetical index anchored to Russell 1000. The new index outperforms Russell 1000 in raw and risk-adjusted returns. It has greater information and Sharpe ratios while maintaining a lower tracking error than other alternative indices. Further, the index fares better in bearish and turbulent markets than in bull markets.