Performance of Industrial Time series momentum strategies in extreme events

As analyzed in the previous sections, the Industrial Time Series Momentum strategies show an interesting feature that these strategies seem to perform well in extreme events, proven graphically by reversal cumulative returns in period of Global crisis in 2008 and 2009. This feature could inspire investors to choose the Industrial Time Series Momentum strategies for hedging. Regarding to this feature, Moskowitz et al. (2012) find that Individual time series momentum strategy performs well in extreme time. They find that individual time series momentum strategy performs well during ‘‘crashes’’ because crises often happen when the economy goes from normal to bad (making the strategies to go short risky assets), and then from bad to worse (leading to the strategy’s profits), with the Global crisis of 2008 being a prime example. In this section, I study the performance of both equally and value weighted Industrial Time Series Momentum strategies in extreme market conditions.

First, all four of the equally and value weighted Industrial Time Series Momentum return series are plotted against the returns of the S&P 500 Composite index, with the time period from January 1990 to December 2018. All the plots are depicted in Panel A of Figure IV. As seen from this panel, there is a “smile” pattern shows up for the 1-month look back equally weighted and the 12-month look back value weighted strategy. This “smile” pattern is similar to the one found in Moskowitz et al. (2012), which inspired them to conclude that their individual time series momentum strategy performs well under extreme markets. In this case for the Industrial Time Series Momentum strategies, the returns are largest during the highest up and down market movements, as known as the “smile” figure. Intuitively, these strategies generate these payoff patterns because an investor tends to go long when the market performs well and short when the market crashes. However, for the other two Industrial Time Series Momentum strategies, this

“smile” pattern does not show up clearly and even disappears for the 1-month look back value weighted strategy. Indeed, as seen from Panel A of Figure IV, the performance of 1-month look back equally weighted and 12-month look back value weighted strategies are good during extreme markets, making these strategies attractive as a hedge through these time periods.

Next, I use VIX, another source as market condition measurement to investigate the performance of Industrial Time Series Momentum strategies under financial distress. The VIX - CBOE Volatility Index provides a simple measure of the tension in the stock market. Not surprisingly, this index experiences huge swings during financial showdown, such as the financial crisis in 2008 or the dot-com bubble. Panel B of Figure IV plots all four Industrial Time Series Momentum return series against the VIX index on the time horizon from January 1990 to December 2018.

Surprisingly, when plotted against the VIX index, the volatility “smile” shows up in all four figures from Panel B of Figure IV. This finding suggests that the performance of Industrial Time Series Momentum strategies is improved during periods with extreme volatility, generally during financial distress. Note that the performance is likewise ameliorated during extremely quite times with low market volatility.

Moreover, to check whether the patterns shown in Figure IV are significant, I run two regressions of all four strategies on the market index return, S&P 500, and on the VIX index, with the time horizon from January 1990 to December 2018. In specific, for the first regression, the return series of all four Industrial Time Series Momentum strategies are regressed on the market index return, S&P 500, and the squared market index return, as following

𝑟_𝑡^{𝐼𝑇𝑆𝑀} = 𝛽₀+ 𝛽₁∗ 𝑆&𝑃_𝑡+ 𝛽₂∗ 𝑆&𝑃_𝑡 ²+ 𝜖_𝑡 (i)

Panel A of Table VII exhibits results of regression (i) for all four Industrial Time Series Momentum strategies. The betas of the squared market index return, 𝑆&𝑃², are significantly positive at 5%

level with t-statistics of 4.03 and 2.76, only from equation (2) and (3). These betas are for 1-month look back equally weighted and 12-month look back value weighted Industrial Time Series Momentum strategies, respectively. Therefore, this result indicates that these strategies deliver the highest profits during the most extreme market episodes. This finding supports the statement drawn from Panel A of Figure IV, that performance of the 1-month look back equally weighted and 12-month look back value weighted strategies are good during extreme markets.

For second regression, I regress the return series of all four Industrial Time Series Momentum strategies on the volatility index, 𝑉𝐼𝑋, and the squared volatility index. The regression for return of each Industrial Time Series Momentum strategy is as following

𝑟_𝑡^{𝐼𝑇𝑆𝑀} = 𝛽₀+ 𝛽₁∗ 𝑉𝐼𝑋_𝑡+ 𝛽₂∗ 𝑉𝐼𝑋_𝑡²+ 𝜖_𝑡 (ii)

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Figure IV. The Industrial Time Series Momentum “smile”. All equally and value weighted Industrial Time Series Momentum strategies’ return series are plotted against the contemporaneous returns on the S&P 500 (Panel A) and the VIX index (Panel B), from January 1990 to December 2018.

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As seen from Panel B of Table VII, all of the regression outputs from equation (5) to (8) show that the squared volatility measure, 𝑉𝐼𝑋², positively predicts the returns of all four Industrial Time Series Momentum strategies, proven by significantly positive betas for this variable through four equations. This result indicates that the performance of all four Industrial Time Series Momentum strategies are statistically significant higher during extreme market conditions, which supports the conclusion obtained from Panel B of Figure IV.

In conclusion, all equally and value weighted Industrial Time Series Momentum strategies perform well during extreme market conditions. Especially, the 1-month look back equally weighted and 12-month look back value weighted strategies perform better than the other strategies during extreme markets, making these strategies attractive as a hedge for investors during market crashes.

Table VII

Performance of Industrial Time Series Momentum strategies in extreme time

Regression of all equally and value weighted Industrial Time Series Momentum returns on the market index return and on the VIX index, with the time horizon from January 1990 to December 2018. Note that the VIX index is scaled down by a factor of 100. The regression equations are 𝑟_𝑡^{𝐼𝑇𝑆𝑀}= 𝛽0+ 𝛽1∗ 𝑆&𝑃𝑡+ 𝛽2∗ 𝑆&𝑃𝑡 2+ 𝜖𝑡 for Panel A, and 𝑟_𝑡^{𝐼𝑇𝑆𝑀}= 𝛽₀+ 𝛽₁∗ 𝑉𝐼𝑋_𝑡+ 𝛽₂∗ 𝑉𝐼𝑋_𝑡²+ 𝜖_𝑡 for Panel B. In parentheses are t-statistics associated with each coefficient.

Panel A: Regression on the S&P 500 index return

𝑟_𝑡^{𝐼𝑇𝑆𝑀} 𝑆&𝑃 𝑆&𝑃 ² Intercept 𝑅² Panel B: Regression on the VIX index

𝑟_𝑡^{𝐼𝑇𝑆𝑀} 𝑉𝐼𝑋 𝑉𝐼𝑋² Intercept 𝑅²