New related paper to #118 – Time Series Momentum Effect
"This study contributes to the existing literature by providing new insights into the existence of time-series momentum across global equity and commodity indices, as well as its explanatory power on the performance of international mutual funds. Indeed, we show that trends in the most traditional instruments such as equity and commodity indices are consistent and even stronger than trends in futures markets, and that trend-following strategies for these instruments are highly profitable. Interestingly, the addition of recent years to the present dataset permits an investigation of time-series momentum in light of central bank intervention, which is known to have distortedcorrelations across asset classes.
Over the years, trend-following strategies have become one of the most important investment strategies in the hedge fund universe. For instance, Moskowitz et al. (2012), Baltas and Kosowski (2013), and Hurst, Ooi, and Pedersen (2014) document that a substantial part of the hedge fund industry, such as Managed Futures Funds and CommodityTrading Advisors (CTAs), follows time-series momentum strategies. We extend this line of research by examining whether particular strategies can be associated with other types of institutional investors, i.e. international mutual funds, which can be characterized as more traditional and risk-averse, or whether they strictly concern the hedge fund industry.
We find that the time-series momentum trading strategy explains a significant proportion of international mutual fund performance. Specifically, international mutual funds have proven to be time-series momentum investors, implying that they tend to buy instruments that have been in an uptrend and sell those that have been in a downtrend. However, while time-series momentum can only partially capture mutual fund performance, a long-only portfolio that invests in instruments that have been performing well or that are in risk-free assets may entirely capture mutual fund behaviour. Therefore, it is likely that mutual funds will show an investment preference for long-only trend-following strategies. These findings are consistent and robust across all samples of asset classes and mutual funds examined. Moreover, they complement existing literature on cross-sectional momentum (e.g. Grinblatt, Titman, Wermers, 1995), where there is evidence that mutual funds have a tendency to buy winners, but they do not systematically sell losers.
We first document the existence of strong return autocorrelation across equity and commodity indices. In particular, we show that the excess return over the past 12 months positively predicts the excess return for the next year. Subsequently, the detected return continuation dissipates or exhibits reversals. These findings confirm behavioural theories of initial under-reaction and delayed over-reaction by investors (Barberis, Shleifer, and Vishny 1998; Daniel, Hirshleifer, and Subrahmanyam 1998; Hong and Stein 1999). Furthermore, the presence of return autocorrelation seems to challenge the weak form of the efficient market hypothesis, according to which future performance cannot be predicted by information contained in historical prices. Following the strong evidence of return predictability, we further construct time-series momentum strategies over a number of look-back and holding periods. We find that time-series momentum strategies deliver substantial abnormal returns with respectable Sharpe ratios for horizons of up to one year. Over longer horizons, the time-series momentum effect dissipates or reverses, as in the case of cross-sectional momentum and return continuations. These results are consistent and robust across all asset classes, subsamples, combinations of look-back and holding periods, and different sample periods.
We further show that time-series momentum strategies tend to outperform cross-sectional momentum strategies, and this finding becomes stronger over shorter holding periods. To better investigate the abnormal performance of time-series momentum, the single and diversified across assets 12-1 time-series momentum strategy is evaluated with regard to standard asset pricing models. Remarkably, the 12-1 strategy cannot be explained by any of the size, value, or growth factors examined, nor by market or commodity benchmarks, but can be partially captured by cross-sectional momentum factors such as Fama and French’s UMD (Up-minus-down) factor and Asness, Moskowitz, and Pedersen’s MOM (Momentum) factor. The relationship between time-series and cross-sectional momentum is further investigated, and it is found that these two investment philosophies are indeed highly related, but distinct from each other in their statistically significant alpha coefficients.
We also document substantial evidence that time-series momentum serves as a hedging strategy in all examined asset classes, and that its payoffs resemble those of an option straddle, which is consistent with Moskowitz et al. (2013). More precisely, it is found that time-series momentum experiences its highest gains during extreme market movements in either direction, rendering explanations of its high profitability even more puzzling from a risk-adjusted perspective. However, this study casts doubt on the future of time-series momentum profitability since the aggressive monetary policies adopted by central banks have increased correlations across asset classes. As a consequence, there are fewer independent trends from which time-series momentum can benefit.
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