Research Review | 31 March 2017 | Managing Portfolio Risk

Bubbles for Fama
Robin M. Greenwood (Harvard Business School), et al.
February 2017
We evaluate Eugene Fama’s claim that stock prices do not exhibit price bubbles. Based on U.S. industry returns 1926–2014 and international sector returns 1985–2014, we present four findings: (1) Fama is correct in that a sharp price increase of an industry portfolio does not, on average, predict unusually low returns going forward; (2) such sharp price increases predict a substantially heightened probability of a crash; (3) attributes of the price run-up, including volatility, turnover, issuance, and the price path of the run-up, can all help forecast an eventual crash and future returns; and (4) some of these characteristics can help investors earn superior returns by timing the bubble. Results hold similarly in U.S. and international samples.

Risk Minimization in Multi-Factor Portfolios: What is the Best Strategy?
Philipp J. Kremer (EBS University of Business and Law), et al.
March 2017
Exposures to risk factors, as opposed to individual securities or bonds, can lead to an ex-ante improved risk management and a more transparent and cheaper way of developing active asset allocation strategies. This paper provides an extensive analysis of eight state-of-the-art risk-minimization schemes and compares risk factor performance in a conditional performance analysis, contrasting good and bad states of the economy. The investment universe spans a total of 25 risk factors, including size, momentum, value, high profitability and low investments, from five nonoverlapping regions (i.e., USA, UK, Japan, Developed Europe ex. UK, and Asia ex. Japan). Considering as investment period the interval from May 2004 to June 2015, our results show that each single factor yields positive premia in exchange for risk, which can lead to considerable underperformance and extensive recovery periods during times of crisis. The best factor investments can be found in Asia ex. Japan and the US. However, risk factor based portfolio construction across the various regions enables the investor to exploit low correlation structures, reducing the overall volatility, as well as tail- and extreme risk measures. Finally, the empirical results point towards the long-only global minimum variance portfolio, as the best risk minimization strategy.

Momentum and Covered Calls Almost Everywhere
Stephen J. Choi (Multi Asset Global Investments), et al.
March 2017
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.

Dynamic Momentum and Contrarian Trading
Victoria Dobrynskaya (Nat’l Research University Higher School of Econ.)
March 2017
High momentum returns cannot be explained by risk factors, but they are negatively skewed and subject to occasional severe crashes. I explore the timing of momentum crashes and show that momentum strategies tend to crash in 1-3 months after the local stock market plunge. Next, I propose a simple dynamic trading strategy which coincides with the standard momentum strategy in calm times, but switches to the opposite contrarian strategy in one month after a market crash and keeps the contrarian position for three months, after which it reverts back to the momentum position. The dynamic momentum strategy turns all major momentum crashes into gains and yields average return, which is about 1.5 times as high as the standard momentum return. The dynamic momentum returns are positively skewed and not exposed to risk factors, have high Sharpe ratio and alpha, persist in different time periods and geographical markets around the Globe.

Asset Allocation with Time Series Momentum and Reversal
Xuezhong He (University of Technology Sydney), et al.
February 2017
To capture the well documented time series momentum and reversal in asset price, we develop a continuous-time asset price model, derive the optimal investment strategy theoretically, and test the strategy empirically. We show that, by combining market fundamentals and timing opportunity with respect to market trend and volatility, the optimal strategy based on time series momentum of moving averages over short-time horizons and reversal significantly outperforms, both in-sample and out-of-sample, the S&P500 and pure strategies based on either time series momentum or reversal only. The results are robust for different time horizons, short-sale constraints, market states, investor sentiment, and market volatility.

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