Is active or passive investing better? The answer to this question is time-varying. The outperformance of active and passive strategies is cyclical. As an active strategy, the paper examines the factor momentum strategy, where the numerous factors represent all the main investment styles such as value, momentum, size, quality or volatility. Firstly, the paper examines two signals: fast, which is 1-month momentum and slow, which is 12-month momentum. Both signals can be used independently, but the results suggest that it is better to employ the information contained in both of the signals. Like Garg et al. (2019), the signals can be combined to either trade only if both signals agree, or adjust the weights if the signals do not agree.
Slow signals tend to be unreactive to changes in trend, and fast signals are often false alarms. Therefore, the weight is set to one half. Moreover, all the factor can be dynamically weighted according to the strength of the signal. The weights are represented by the ranks of the absolute values of signals – stronger the signal greater the weight. Although the outperformance of dynamical weighting (smart factor strategy) compared to all other strategies is present on each market (US and EAFE) and each type of portfolio sort (quintiles, deciles and ventiles), the smart factor strategy would still be outperformed by a simple buy-and-hold market portfolio.
However, the market portfolio and the smart factor strategy are significantly negatively correlated. Therefore, it is natural that the portfolios could be combined to get the best of them. The paper sets a straightforward rule: look on the past 12 monthly moving averages of returns (from one- to twelve-months MA) for both strategies. The combined portfolio is weighted according to the number of “won” moving averages.
Factors are profitable when the market is not, and by the construction, the combination strategy has the biggest allocation into factors when there is a market downturn. On the other hand, the factors tend to be flat when the market is largely profitable. As a result, the combination has the largest return, the lowest volatility and max drawdown, and the highest risk-adjusted-performance. A dollar invested in the 31.12.1993 would result in the 20.28 dollars by the 31.8.2020 compared to only 14.52 dollars for the Market portfolio.
Throughout the paper, both EAFE and US market is analyzed, but we centre our attention only around the US market.
Firstly, the functionality of factor strategies was proven by numerous academic researches. The same could be said about the momentum in factors since both the time-series and cross-sectional momentum strategies are well-examined and proved to be functional. The factor momentum also solves the problem of underperforming factors because of the wrong portfolio sort (for example, when growth outperforms value or big size outperforms small size).
The blending of the factor seems to be important because the slow signals tend to be unreactive to changes in trend, and fast signals are often false alarms. Therefore, the weight of the factors should be adjusted based on signal interreactions. Lastly, the dynamical weights based on the strength of the signals is also a widely utilized approach that was found to be effective also in the factor universe.
Although the active factor strategy largely outperforms naive equal-weighting of the factors or signals alone, it would have been largely beaten by the market. However, the active factor strategy and market are negatively correlated. This correlation is statistically significant using a robust non-parametric test, and this result suggests that the two portfolios could be combined to achieve the best of the two approaches. The backtest confirms this theory, since the combined strategy using moving averages, has the largest return, the lowest volatility or drawdown, and the returns distribution is much more favourable.
Backtest period from source paper
Confidence in anomaly's validity
Notes to Confidence in Anomaly's Validity
Notes to Indicative Performance
Period of Rebalancing
Notes to Period of Rebalancing
Notes to Estimated Volatility
Number of Traded Instruments
Notes to Number of Traded Instruments
Notes to Maximum drawdown
Notes to Complexity Evaluation
Global, United States
Simple trading strategy
The investment universe consists of factors from the Alpha Architect’s Factor Investing Data Library (factor for all major investment styles such as Value, Quality, Momentum, Size and Volatility) based on the top 1500 US stocks. Firstly construct the fast and slow signals for each factor. The fast signal is the past one-month return, and the slow signal is the past twelve-months return. For each type of signal, to obtain the weights, cross-sectionally rank signals’ based on their absolute values. The weight for the individual slow or fast signal is equal to the corresponding rank divided by the sum of all ranks and multiplied by the signal’s sign (equations 3 and 4 in the paper). For the dynamically blended strategy (smart factors strategy), each factor has a final weight of three-quarters of the weight of fast signal plus one-quarter of the weight of slow signal (equation 12). Nextly, consider the top 1500 US stocks as the market portfolio. The combined smart factors and market strategy finds the weights of the market and factor portfolio using past moving averages of the returns. The combined strategy looks back on the past twelve months, and twelve MAs of the returns. Suppose the MA for active investing (factor momentum) is larger than MA for market portfolio, then the active investing scores one point. Otherwise, the market portfolio gets one point. Therefore, each month, the weight of the factor momentum and market portfolio is determined by the number of “winning” (loosing) moving averages (equations 13 and 14). The strategy is rebalanced monthly.
Hedge for stocks during bear markets
Partially - The “Smart” factor strategy is negatively correlated with the market portfolio, but the resulting combined strategy consists of both factor and market portfolio. Therefore it invests into the market and cannot be considered as an ideal hedge. However, during downturns, the strategy tends to invest more in the dynamic factor momentum and largely minimizes drawdowns (Figure 6).
Out-of-sample strategy's implementation/validation in QuantConnect's framework