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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.
Fundamental reason
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.
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Keywords
Market Factors
Confidence in Anomaly's Validity
Period of Rebalancing
Number of Traded Instruments
Notes to Number of Traded Instruments
Complexity Evaluation
Financial instruments
Backtest period from source paper
Indicative Performance
Notes to Indicative Performance
Estimated Volatility
Notes to Estimated Volatility
Notes to Maximum drawdown
Sharpe Ratio
Regions
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(chart, statistics & code)
Related picture
Source paper
Padyšák, Matúš: The active vs passive: smart factors, market portfolio or both?
Abstract: While there may be debates about passive and active investing, and even blogs about the numbers of active funds that were outperformed by the market, the history taught us that the outperformance of active or passive investing is cyclical. As a proxy for the active investing, the paper examines factor strategies and their smart allocation using fast or slow time-series momentum signals, the relative weights based on the strength of the signals and even blending the signals. While the performance can be significantly improved, using those smart approaches, the factors still got beaten by the market in both US and EAFE sample. However, the passive approach did not show to be superior. The factor strategies and market are significantly negatively correlated and impressively complement each other. The combined Smart Factors and market portfolio vastly outperforms both factors and market throughout the sample in both markets. With the combined approach, the ever-present market falls can be at least mitigated or profitable thanks to the factors.
Other papers
Kadan, Ohad and Liu, Fang and Tang, Xiaoxiao: Recovering Conditional Factor Risk Premia
Abstract: We offer an approach for recovering option-implied time-varying forward-looking risk premia of systematic factors---even if they do not possess actively-traded options. We apply this approach to the market, size, value, and momentum factors. We find that factor premia are highly volatile. Both the market and the value premia tend to be higher during slowdowns and recessions and during turbulent times. By contrast, the momentum premium is higher during periods of high economic growth and low volatility. We use the recovered factor premia to construct trading strategies, which mitigate market and momentum crash risk and to predict returns of individual stocks even if they do not possess traded options.
Favero, Carlo A. and Melone, Alessandro and Tamoni, Andrea: Macro Trends and Factor Timing
Abstract: We find that the value of well-known systematic (characteristics-based) risk factors, like SMB and HML, is anchored to macroeconomic trends related to inflation and real economic activity. Exploiting the cointegration logic, when the price of a factor is greater than the long-term value implied by the macro trends, expected returns should be lower over the next period. We provide strong supporting evidence for this intuition: deviations of factor prices from their value implied by macroeconomic conditions predict factor returns both in- and out-of-sample, translating into significant economic gains from the perspective of a mean-variance investor. Finally, our approach leads to an estimated SDF that displays sizable variation over time when benchmarked against standard long-run risk or habit models.
Geertsema, Paul G. and Lu, Helen: Patient Capital and Long-run Expected Stock Returns
Abstract: Models of expected returns are typically evaluated using monthly stock returns. However, it is not clear that one-month-ahead expected returns are appropriate for longer-horizon “patient capital” investors. We use gradient boosting machines to create predictive models of expected returns at horizons ranging from 1 month to 5 years in panel data. We find that long-horizon expected returns are distinct from 1-month expected returns. More than 80% of the variation in the 5-year expected return is unexplained by variation in the 1-month expected return. The stylized fact that market-based indicators are the most important predictors of expected returns is shown to be specific to one-month ahead predictions and does not generalize to expected returns at longer horizons. Instead, the drivers of long-horizon returns are dominated by size, industry, equity issuance and fundamental predictors. The application of common benchmark models to predicted-return strategies yield alphas that remain economically and statistically significant at all horizons, suggesting that expected returns are not well modelled by the linear factor models prevalent in the literature.